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#!/usr/bin/python
# -*- coding: utf-8 -*-
from ansible.module_utils.openshift_common import OpenShiftAnsibleModule, OpenShiftAnsibleException
DOCUMENTATION = '''
module: openshift_v1_build_config_list
short_description: OpenShift BuildConfigList
description:
- Retrieve a list of build_configs. List operations provide a snapshot read of the
underlying objects, returning a resource_version representing a consistent version
of the listed objects.
version_added: 2.3.0
author: OpenShift (@openshift)
options:
api_key:
description:
- Token used to connect to the API.
cert_file:
description:
- Path to a certificate used to authenticate with the API.
type: path
context:
description:
- The name of a context found in the Kubernetes config file.
debug:
description:
- Enable debug output from the OpenShift helper. Logging info is written to KubeObjHelper.log
default: false
type: bool
force:
description:
- If set to C(True), and I(state) is C(present), an existing object will updated,
and lists will be replaced, rather than merged.
default: false
type: bool
host:
description:
- Provide a URL for acessing the Kubernetes API.
key_file:
description:
- Path to a key file used to authenticate with the API.
type: path
kubeconfig:
description:
- Path to an existing Kubernetes config file. If not provided, and no other connection
options are provided, the openshift client will attempt to load the default
configuration file from I(~/.kube/config.json).
type: path
password:
description:
- Provide a password for connecting to the API. Use in conjunction with I(username).
resource_definition:
description:
- Provide the YAML definition for the object, bypassing any modules parameters
intended to define object attributes.
type: dict
src:
description:
- Provide a path to a file containing the YAML definition of the object. Mutually
exclusive with I(resource_definition).
type: path
ssl_ca_cert:
description:
- Path to a CA certificate used to authenticate with the API.
type: path
state:
description:
- Determines if an object should be created, patched, or deleted. When set to
C(present), the object will be created, if it does not exist, or patched, if
parameter values differ from the existing object's attributes, and deleted,
if set to C(absent). A patch operation results in merging lists and updating
dictionaries, with lists being merged into a unique set of values. If a list
contains a dictionary with a I(name) or I(type) attribute, a strategic merge
is performed, where individual elements with a matching I(name_) or I(type)
are merged. To force the replacement of lists, set the I(force) option to C(True).
default: present
choices:
- present
- absent
username:
description:
- Provide a username for connecting to the API.
verify_ssl:
description:
- Whether or not to verify the API server's SSL certificates.
type: bool
requirements:
- openshift == 0.3.3
'''
EXAMPLES = '''
'''
RETURN = '''
api_version:
type: string
description: Requested API version
build_config_list:
type: complex
returned: when I(state) = C(present)
contains:
api_version:
description:
- APIVersion defines the versioned schema of this representation of an object.
Servers should convert recognized schemas to the latest internal value, and
may reject unrecognized values.
type: str
items:
description:
- items is a list of build configs
type: list
contains:
api_version:
description:
- APIVersion defines the versioned schema of this representation of an object.
Servers should convert recognized schemas to the latest internal value,
and may reject unrecognized values.
type: str
kind:
description:
- Kind is a string value representing the REST resource this object represents.
Servers may infer this from the endpoint the client submits requests to.
Cannot be updated. In CamelCase.
type: str
metadata:
description:
- metadata for BuildConfig.
type: complex
contains:
annotations:
description:
- Annotations is an unstructured key value map stored with a resource
that may be set by external tools to store and retrieve arbitrary
metadata. They are not queryable and should be preserved when modifying
objects.
type: complex
contains: str, str
cluster_name:
description:
- The name of the cluster which the object belongs to. This is used
to distinguish resources with same name and namespace in different
clusters. This field is not set anywhere right now and apiserver is
going to ignore it if set in create or update request.
type: str
creation_timestamp:
description:
- CreationTimestamp is a timestamp representing the server time when
this object was created. It is not guaranteed to be set in happens-before
order across separate operations. Clients may not set this value.
It is represented in RFC3339 form and is in UTC. Populated by the
system. Read-only. Null for lists.
type: complex
contains: {}
deletion_grace_period_seconds:
description:
- Number of seconds allowed for this object to gracefully terminate
before it will be removed from the system. Only set when deletionTimestamp
is also set. May only be shortened. Read-only.
type: int
deletion_timestamp:
description:
- DeletionTimestamp is RFC 3339 date and time at which this resource
will be deleted. This field is set by the server when a graceful deletion
is requested by the user, and is not directly settable by a client.
The resource is expected to be deleted (no longer visible from resource
lists, and not reachable by name) after the time in this field. Once
set, this value may not be unset or be set further into the future,
although it may be shortened or the resource may be deleted prior
to this time. For example, a user may request that a pod is deleted
in 30 seconds. The Kubelet will react by sending a graceful termination
signal to the containers in the pod. After that 30 seconds, the Kubelet
will send a hard termination signal (SIGKILL) to the container and
after cleanup, remove the pod from the API. In the presence of network
partitions, this object may still exist after this timestamp, until
an administrator or automated process can determine the resource is
fully terminated. If not set, graceful deletion of the object has
not been requested. Populated by the system when a graceful deletion
is requested. Read-only.
type: complex
contains: {}
finalizers:
description:
- Must be empty before the object is deleted from the registry. Each
entry is an identifier for the responsible component that will remove
the entry from the list. If the deletionTimestamp of the object is
non-nil, entries in this list can only be removed.
type: list
contains: str
generate_name:
description:
- GenerateName is an optional prefix, used by the server, to generate
a unique name ONLY IF the Name field has not been provided. If this
field is used, the name returned to the client will be different than
the name passed. This value will also be combined with a unique suffix.
The provided value has the same validation rules as the Name field,
and may be truncated by the length of the suffix required to make
the value unique on the server. If this field is specified and the
generated name exists, the server will NOT return a 409 - instead,
it will either return 201 Created or 500 with Reason ServerTimeout
indicating a unique name could not be found in the time allotted,
and the client should retry (optionally after the time indicated in
the Retry-After header). Applied only if Name is not specified.
type: str
generation:
description:
- A sequence number representing a specific generation of the desired
state. Populated by the system. Read-only.
type: int
initializers:
description:
- An initializer is a controller which enforces some system invariant
at object creation time. This field is a list of initializers that
have not yet acted on this object. If nil or empty, this object has
been completely initialized. Otherwise, the object is considered uninitialized
and is hidden (in list/watch and get calls) from clients that haven't
explicitly asked to observe uninitialized objects. When an object
is created, the system will populate this list with the current set
of initializers. Only privileged users may set or modify this list.
Once it is empty, it may not be modified further by any user.
type: complex
contains:
pending:
description:
- Pending is a list of initializers that must execute in order before
this object is visible. When the last pending initializer is removed,
and no failing result is set, the initializers struct will be
set to nil and the object is considered as initialized and visible
to all clients.
type: list
contains:
name:
description:
- name of the process that is responsible for initializing this
object.
type: str
result:
description:
- If result is set with the Failure field, the object will be persisted
to storage and then deleted, ensuring that other clients can observe
the deletion.
type: complex
contains:
api_version:
description:
- APIVersion defines the versioned schema of this representation
of an object. Servers should convert recognized schemas to
the latest internal value, and may reject unrecognized values.
type: str
code:
description:
- Suggested HTTP return code for this status, 0 if not set.
type: int
details:
description:
- Extended data associated with the reason. Each reason may
define its own extended details. This field is optional and
the data returned is not guaranteed to conform to any schema
except that defined by the reason type.
type: complex
contains:
causes:
description:
- The Causes array includes more details associated with
the StatusReason failure. Not all StatusReasons may provide
detailed causes.
type: list
contains:
field:
description:
- 'The field of the resource that has caused this error,
as named by its JSON serialization. May include dot
and postfix notation for nested attributes. Arrays
are zero-indexed. Fields may appear more than once
in an array of causes due to fields having multiple
errors. Optional. Examples: "name" - the field "name"
on the current resource "items[0].name" - the field
"name" on the first array entry in "items"'
type: str
message:
description:
- A human-readable description of the cause of the error.
This field may be presented as-is to a reader.
type: str
reason:
description:
- A machine-readable description of the cause of the
error. If this value is empty there is no information
available.
type: str
group:
description:
- The group attribute of the resource associated with the
status StatusReason.
type: str
kind:
description:
- The kind attribute of the resource associated with the
status StatusReason. On some operations may differ from
the requested resource Kind.
type: str
name:
description:
- The name attribute of the resource associated with the
status StatusReason (when there is a single name which
can be described).
type: str
retry_after_seconds:
description:
- If specified, the time in seconds before the operation
should be retried.
type: int
uid:
description:
- UID of the resource. (when there is a single resource
which can be described).
type: str
kind:
description:
- Kind is a string value representing the REST resource this
object represents. Servers may infer this from the endpoint
the client submits requests to. Cannot be updated. In CamelCase.
type: str
message:
description:
- A human-readable description of the status of this operation.
type: str
metadata:
description:
- Standard list metadata.
type: complex
contains:
resource_version:
description:
- String that identifies the server's internal version of
this object that can be used by clients to determine when
objects have changed. Value must be treated as opaque
by clients and passed unmodified back to the server. Populated
by the system. Read-only.
type: str
self_link:
description:
- SelfLink is a URL representing this object. Populated
by the system. Read-only.
type: str
reason:
description:
- A machine-readable description of why this operation is in
the "Failure" status. If this value is empty there is no information
available. A Reason clarifies an HTTP status code but does
not override it.
type: str
status:
description:
- 'Status of the operation. One of: "Success" or "Failure".'
type: str
labels:
description:
- Map of string keys and values that can be used to organize and categorize
(scope and select) objects. May match selectors of replication controllers
and services.
type: complex
contains: str, str
name:
description:
- Name must be unique within a namespace. Is required when creating
resources, although some resources may allow a client to request the
generation of an appropriate name automatically. Name is primarily
intended for creation idempotence and configuration definition. Cannot
be updated.
type: str
namespace:
description:
- Namespace defines the space within each name must be unique. An empty
namespace is equivalent to the "default" namespace, but "default"
is the canonical representation. Not all objects are required to be
scoped to a namespace - the value of this field for those objects
will be empty. Must be a DNS_LABEL. Cannot be updated.
type: str
owner_references:
description:
- List of objects depended by this object. If ALL objects in the list
have been deleted, this object will be garbage collected. If this
object is managed by a controller, then an entry in this list will
point to this controller, with the controller field set to true. There
cannot be more than one managing controller.
type: list
contains:
api_version:
description:
- API version of the referent.
type: str
block_owner_deletion:
description:
- If true, AND if the owner has the "foregroundDeletion" finalizer,
then the owner cannot be deleted from the key-value store until
this reference is removed. Defaults to false. To set this field,
a user needs "delete" permission of the owner, otherwise 422 (Unprocessable
Entity) will be returned.
type: bool
controller:
description:
- If true, this reference points to the managing controller.
type: bool
kind:
description:
- Kind of the referent.
type: str
name:
description:
- Name of the referent.
type: str
uid:
description:
- UID of the referent.
type: str
resource_version:
description:
- An opaque value that represents the internal version of this object
that can be used by clients to determine when objects have changed.
May be used for optimistic concurrency, change detection, and the
watch operation on a resource or set of resources. Clients must treat
these values as opaque and passed unmodified back to the server. They
may only be valid for a particular resource or set of resources. Populated
by the system. Read-only. Value must be treated as opaque by clients
and .
type: str
self_link:
description:
- SelfLink is a URL representing this object. Populated by the system.
Read-only.
type: str
uid:
description:
- UID is the unique in time and space value for this object. It is typically
generated by the server on successful creation of a resource and is
not allowed to change on PUT operations. Populated by the system.
Read-only.
type: str
spec:
description:
- spec holds all the input necessary to produce a new build, and the conditions
when to trigger them.
type: complex
contains:
completion_deadline_seconds:
description:
- completionDeadlineSeconds is an optional duration in seconds, counted
from the time when a build pod gets scheduled in the system, that
the build may be active on a node before the system actively tries
to terminate the build; value must be positive integer
type: int
failed_builds_history_limit:
description:
- failedBuildsHistoryLimit is the number of old failed builds to retain.
If not specified, all failed builds are retained.
type: int
node_selector:
description:
- nodeSelector is a selector which must be true for the build pod to
fit on a node If nil, it can be overridden by default build nodeselector
values for the cluster. If set to an empty map or a map with any values,
default build nodeselector values are ignored.
type: complex
contains: str, str
output:
description:
- output describes the Docker image the Strategy should produce.
type: complex
contains:
image_labels:
description:
- imageLabels define a list of labels that are applied to the resulting
image. If there are multiple labels with the same name then the
last one in the list is used.
type: list
contains:
name:
description:
- name defines the name of the label. It must have non-zero
length.
type: str
value:
description:
- value defines the literal value of the label.
type: str
push_secret:
description:
- PushSecret is the name of a Secret that would be used for setting
up the authentication for executing the Docker push to authentication
enabled Docker Registry (or Docker Hub).
type: complex
contains:
name:
description:
- Name of the referent.
type: str
to:
description:
- to defines an optional location to push the output of this build
to. Kind must be one of 'ImageStreamTag' or 'DockerImage'. This
value will be used to look up a Docker image repository to push
to. In the case of an ImageStreamTag, the ImageStreamTag will
be looked for in the namespace of the build unless Namespace is
specified.
type: complex
contains:
api_version:
description:
- API version of the referent.
type: str
field_path:
description:
- 'If referring to a piece of an object instead of an entire
object, this string should contain a valid JSON/Go field access
statement, such as desiredState.manifest.containers[2]. For
example, if the object reference is to a container within
a pod, this would take on a value like: "spec.containers{name}"
(where "name" refers to the name of the container that triggered
the event) or if no container name is specified "spec.containers[2]"
(container with index 2 in this pod). This syntax is chosen
only to have some well-defined way of referencing a part of
an object.'
type: str
kind:
description:
- Kind of the referent.
type: str
name:
description:
- Name of the referent.
type: str
namespace:
description:
- Namespace of the referent.
type: str
resource_version:
description:
- Specific resourceVersion to which this reference is made,
if any.
type: str
uid:
description:
- UID of the referent.
type: str
post_commit:
description:
- postCommit is a build hook executed after the build output image is
committed, before it is pushed to a registry.
type: complex
contains:
args:
description:
- args is a list of arguments that are provided to either Command,
Script or the Docker image's default entrypoint. The arguments
are placed immediately after the command to be run.
type: list
contains: str
command:
description:
- command is the command to run. It may not be specified with Script.
This might be needed if the image doesn't have `/bin/sh`, or if
you do not want to use a shell. In all other cases, using Script
might be more convenient.
type: list
contains: str
script:
description:
- script is a shell script to be run with `/bin/sh -ic`. It may
not be specified with Command. Use Script when a shell script
is appropriate to execute the post build hook, for example for
running unit tests with `rake test`. If you need control over
the image entrypoint, or if the image does not have `/bin/sh`,
use Command and/or Args. The `-i` flag is needed to support CentOS
and RHEL images that use Software Collections (SCL), in order
to have the appropriate collections enabled in the shell. E.g.,
in the Ruby image, this is necessary to make `ruby`, `bundle`
and other binaries available in the PATH.
type: str
resources:
description:
- resources computes resource requirements to execute the build.
type: complex
contains:
limits:
description:
- Limits describes the maximum amount of compute resources allowed.
type: complex
contains: str, str
requests:
description:
- Requests describes the minimum amount of compute resources required.
If Requests is omitted for a container, it defaults to Limits
if that is explicitly specified, otherwise to an implementation-defined
value.
type: complex
contains: str, str
revision:
description:
- revision is the information from the source for a specific repo snapshot.
This is optional.
type: complex
contains:
git:
description:
- Git contains information about git-based build source
type: complex
contains:
author:
description:
- author is the author of a specific commit
type: complex
contains:
email:
description:
- email of the source control user
type: str
name:
description:
- name of the source control user
type: str
commit:
description:
- commit is the commit hash identifying a specific commit
type: str
committer:
description:
- committer is the committer of a specific commit
type: complex
contains:
email:
description:
- email of the source control user
type: str
name:
description:
- name of the source control user
type: str
message:
description:
- message is the description of a specific commit
type: str
type:
description:
- type of the build source, may be one of 'Source', 'Dockerfile',
'Binary', or 'Images'
type: str
run_policy:
description:
- RunPolicy describes how the new build created from this build configuration
will be scheduled for execution. This is optional, if not specified
we default to "Serial".
type: str
service_account:
description:
- serviceAccount is the name of the ServiceAccount to use to run the
pod created by this build. The pod will be allowed to use secrets
referenced by the ServiceAccount
type: str
source:
description:
- source describes the SCM in use.
type: complex
contains:
binary:
description:
- binary builds accept a binary as their input. The binary is generally
assumed to be a tar, gzipped tar, or zip file depending on the
strategy. For Docker builds, this is the build context and an
optional Dockerfile may be specified to override any Dockerfile
in the build context. For Source builds, this is assumed to be
an archive as described above. For Source and Docker builds, if
binary.asFile is set the build will receive a directory with a
single file. contextDir may be used when an archive is provided.
Custom builds will receive this binary as input on STDIN.
type: complex
contains:
as_file:
description:
- asFile indicates that the provided binary input should be
considered a single file within the build input. For example,
specifying "webapp.war" would place the provided binary as
`/webapp.war` for the builder. If left empty, the Docker and
Source build strategies assume this file is a zip, tar, or
tar.gz file and extract it as the source. The custom strategy
receives this binary as standard input. This filename may
not contain slashes or be '..' or '.'.
type: str
context_dir:
description:
- contextDir specifies the sub-directory where the source code for
the application exists. This allows to have buildable sources
in directory other than root of repository.
type: str
dockerfile:
description:
- dockerfile is the raw contents of a Dockerfile which should be
built. When this option is specified, the FROM may be modified
based on your strategy base image and additional ENV stanzas from
your strategy environment will be added after the FROM, but before
the rest of your Dockerfile stanzas. The Dockerfile source type
may be used with other options like git - in those cases the Git
repo will have any innate Dockerfile replaced in the context dir.
type: str
git:
description:
- git contains optional information about git build source
type: complex
contains:
http_proxy:
description:
- httpProxy is a proxy used to reach the git repository over
http
type: str
https_proxy:
description:
- httpsProxy is a proxy used to reach the git repository over
https
type: str
no_proxy:
description:
- noProxy is the list of domains for which the proxy should
not be used
type: str
ref:
description:
- ref is the branch/tag/ref to build.
type: str
uri:
description:
- uri points to the source that will be built. The structure
of the source will depend on the type of build to run
type: str
images:
description:
- images describes a set of images to be used to provide source
for the build
type: list
contains:
_from:
description:
- from is a reference to an ImageStreamTag, ImageStreamImage,
or DockerImage to copy source from.
type: complex
contains:
api_version:
description:
- API version of the referent.
type: str
field_path:
description:
- 'If referring to a piece of an object instead of an entire
object, this string should contain a valid JSON/Go field
access statement, such as desiredState.manifest.containers[2].
For example, if the object reference is to a container
within a pod, this would take on a value like: "spec.containers{name}"
(where "name" refers to the name of the container that
triggered the event) or if no container name is specified
"spec.containers[2]" (container with index 2 in this pod).
This syntax is chosen only to have some well-defined way
of referencing a part of an object.'
type: str
kind:
description:
- Kind of the referent.
type: str
name:
description:
- Name of the referent.
type: str
namespace:
description:
- Namespace of the referent.
type: str
resource_version:
description:
- Specific resourceVersion to which this reference is made,
if any.
type: str
uid:
description:
- UID of the referent.
type: str
paths:
description:
- paths is a list of source and destination paths to copy from
the image.
type: list
contains:
destination_dir:
description:
- destinationDir is the relative directory within the build
directory where files copied from the image are placed.
type: str
source_path:
description:
- sourcePath is the absolute path of the file or directory
inside the image to copy to the build directory. If the
source path ends in /. then the content of the directory
will be copied, but the directory itself will not be created
at the destination.
type: str
pull_secret:
description:
- pullSecret is a reference to a secret to be used to pull the
image from a registry If the image is pulled from the OpenShift
registry, this field does not need to be set.
type: complex
contains:
name:
description:
- Name of the referent.
type: str
secrets:
description:
- secrets represents a list of secrets and their destinations that
will be used only for the build.
type: list
contains:
destination_dir:
description:
- destinationDir is the directory where the files from the secret
should be available for the build time. For the Source build
strategy, these will be injected into a container where the
assemble script runs. Later, when the script finishes, all
files injected will be truncated to zero length. For the Docker
build strategy, these will be copied into the build directory,
where the Dockerfile is located, so users can ADD or COPY
them during docker build.
type: str
secret:
description:
- secret is a reference to an existing secret that you want
to use in your build.
type: complex
contains:
name:
description:
- Name of the referent.
type: str
source_secret:
description:
- "sourceSecret is the name of a Secret that would be used for setting\
\ up the authentication for cloning private repository. The secret\
\ contains valid credentials for remote repository, where the\
\ data's key represent the authentication method to be used and\
\ value is the base64 encoded credentials. Supported auth methods\
\ are: ssh-privatekey."
type: complex
contains:
name:
description:
- Name of the referent.
type: str
type:
description:
- type of build input to accept
type: str
strategy:
description:
- strategy defines how to perform a build.
type: complex
contains:
custom_strategy:
description:
- customStrategy holds the parameters to the Custom build strategy
type: complex
contains:
_from:
description:
- from is reference to an DockerImage, ImageStreamTag, or ImageStreamImage
from which the docker image should be pulled
type: complex
contains:
api_version:
description:
- API version of the referent.
type: str
field_path:
description:
- 'If referring to a piece of an object instead of an entire
object, this string should contain a valid JSON/Go field
access statement, such as desiredState.manifest.containers[2].
For example, if the object reference is to a container
within a pod, this would take on a value like: "spec.containers{name}"
(where "name" refers to the name of the container that
triggered the event) or if no container name is specified
"spec.containers[2]" (container with index 2 in this pod).
This syntax is chosen only to have some well-defined way
of referencing a part of an object.'
type: str
kind:
description:
- Kind of the referent.
type: str
name:
description:
- Name of the referent.
type: str
namespace:
description:
- Namespace of the referent.
type: str
resource_version:
description:
- Specific resourceVersion to which this reference is made,
if any.
type: str
uid:
description:
- UID of the referent.
type: str
build_api_version:
description:
- buildAPIVersion is the requested API version for the Build
object serialized and passed to the custom builder
type: str
env:
description:
- env contains additional environment variables you want to
pass into a builder container.
type: list
contains:
name:
description:
- Name of the environment variable. Must be a C_IDENTIFIER.
type: str
value:
description:
- 'Variable references $(VAR_NAME) are expanded using the
previous defined environment variables in the container
and any service environment variables. If a variable cannot
be resolved, the reference in the input string will be
unchanged. The $(VAR_NAME) syntax can be escaped with
a double $$, ie: $$(VAR_NAME). Escaped references will
never be expanded, regardless of whether the variable
exists or not. Defaults to "".'
type: str
value_from:
description:
- Source for the environment variable's value. Cannot be
used if value is not empty.
type: complex
contains:
config_map_key_ref:
description:
- Selects a key of a ConfigMap.
type: complex
contains:
key:
description:
- The key to select.
type: str
name:
description:
- Name of the referent.
type: str
optional:
description:
- Specify whether the ConfigMap or it's key must
be defined
type: bool
field_ref:
description:
- 'Selects a field of the pod: supports metadata.name,
metadata.namespace, metadata.labels, metadata.annotations,
spec.nodeName, spec.serviceAccountName, status.hostIP,
status.podIP.'
type: complex
contains:
api_version:
description:
- Version of the schema the FieldPath is written
in terms of, defaults to "v1".
type: str
field_path:
description:
- Path of the field to select in the specified API
version.
type: str
resource_field_ref:
description:
- 'Selects a resource of the container: only resources
limits and requests (limits.cpu, limits.memory, requests.cpu
and requests.memory) are currently supported.'
type: complex
contains:
container_name:
description:
- 'Container name: required for volumes, optional
for env vars'
type: str
divisor:
description:
- Specifies the output format of the exposed resources,
defaults to "1"
type: str
resource:
description:
- 'Required: resource to select'
type: str
secret_key_ref:
description:
- Selects a key of a secret in the pod's namespace
type: complex
contains:
key:
description:
- The key of the secret to select from. Must be
a valid secret key.
type: str
name:
description:
- Name of the referent.
type: str
optional:
description:
- Specify whether the Secret or it's key must be
defined
type: bool
expose_docker_socket:
description:
- exposeDockerSocket will allow running Docker commands (and
build Docker images) from inside the Docker container.
type: bool
force_pull:
description:
- forcePull describes if the controller should configure the
build pod to always pull the images for the builder or only
pull if it is not present locally
type: bool
pull_secret:
description:
- pullSecret is the name of a Secret that would be used for
setting up the authentication for pulling the Docker images
from the private Docker registries
type: complex
contains:
name:
description:
- Name of the referent.
type: str
secrets:
description:
- secrets is a list of additional secrets that will be included
in the build pod
type: list
contains:
mount_path:
description:
- mountPath is the path at which to mount the secret
type: str
secret_source:
description:
- secretSource is a reference to the secret
type: complex
contains:
name:
description:
- Name of the referent.
type: str
docker_strategy:
description:
- dockerStrategy holds the parameters to the Docker build strategy.
type: complex
contains:
_from:
description:
- from is reference to an DockerImage, ImageStreamTag, or ImageStreamImage
from which the docker image should be pulled the resulting
image will be used in the FROM line of the Dockerfile for
this build.
type: complex
contains:
api_version:
description:
- API version of the referent.
type: str
field_path:
description:
- 'If referring to a piece of an object instead of an entire
object, this string should contain a valid JSON/Go field
access statement, such as desiredState.manifest.containers[2].
For example, if the object reference is to a container
within a pod, this would take on a value like: "spec.containers{name}"
(where "name" refers to the name of the container that
triggered the event) or if no container name is specified
"spec.containers[2]" (container with index 2 in this pod).
This syntax is chosen only to have some well-defined way
of referencing a part of an object.'
type: str
kind:
description:
- Kind of the referent.
type: str
name:
description:
- Name of the referent.
type: str
namespace:
description:
- Namespace of the referent.
type: str
resource_version:
description:
- Specific resourceVersion to which this reference is made,
if any.
type: str
uid:
description:
- UID of the referent.
type: str
build_args:
description:
- buildArgs contains build arguments that will be resolved in
the Dockerfile. See
type: list
contains:
name:
description:
- Name of the environment variable. Must be a C_IDENTIFIER.
type: str
value:
description:
- 'Variable references $(VAR_NAME) are expanded using the
previous defined environment variables in the container
and any service environment variables. If a variable cannot
be resolved, the reference in the input string will be
unchanged. The $(VAR_NAME) syntax can be escaped with
a double $$, ie: $$(VAR_NAME). Escaped references will
never be expanded, regardless of whether the variable
exists or not. Defaults to "".'
type: str
value_from:
description:
- Source for the environment variable's value. Cannot be
used if value is not empty.
type: complex
contains:
config_map_key_ref:
description:
- Selects a key of a ConfigMap.
type: complex
contains:
key:
description:
- The key to select.
type: str
name:
description:
- Name of the referent.
type: str
optional:
description:
- Specify whether the ConfigMap or it's key must
be defined
type: bool
field_ref:
description:
- 'Selects a field of the pod: supports metadata.name,
metadata.namespace, metadata.labels, metadata.annotations,
spec.nodeName, spec.serviceAccountName, status.hostIP,
status.podIP.'
type: complex
contains:
api_version:
description:
- Version of the schema the FieldPath is written
in terms of, defaults to "v1".
type: str
field_path:
description:
- Path of the field to select in the specified API
version.
type: str
resource_field_ref:
description:
- 'Selects a resource of the container: only resources
limits and requests (limits.cpu, limits.memory, requests.cpu
and requests.memory) are currently supported.'
type: complex
contains:
container_name:
description:
- 'Container name: required for volumes, optional
for env vars'
type: str
divisor:
description:
- Specifies the output format of the exposed resources,
defaults to "1"
type: str
resource:
description:
- 'Required: resource to select'
type: str
secret_key_ref:
description:
- Selects a key of a secret in the pod's namespace
type: complex
contains:
key:
description:
- The key of the secret to select from. Must be
a valid secret key.
type: str
name:
description:
- Name of the referent.
type: str
optional:
description:
- Specify whether the Secret or it's key must be
defined
type: bool
dockerfile_path:
description:
- dockerfilePath is the path of the Dockerfile that will be
used to build the Docker image, relative to the root of the
context (contextDir).
type: str
env:
description:
- env contains additional environment variables you want to
pass into a builder container.
type: list
contains:
name:
description:
- Name of the environment variable. Must be a C_IDENTIFIER.
type: str
value:
description:
- 'Variable references $(VAR_NAME) are expanded using the
previous defined environment variables in the container
and any service environment variables. If a variable cannot
be resolved, the reference in the input string will be
unchanged. The $(VAR_NAME) syntax can be escaped with
a double $$, ie: $$(VAR_NAME). Escaped references will
never be expanded, regardless of whether the variable
exists or not. Defaults to "".'
type: str
value_from:
description:
- Source for the environment variable's value. Cannot be
used if value is not empty.
type: complex
contains:
config_map_key_ref:
description:
- Selects a key of a ConfigMap.
type: complex
contains:
key:
description:
- The key to select.
type: str
name:
description:
- Name of the referent.
type: str
optional:
description:
- Specify whether the ConfigMap or it's key must
be defined
type: bool
field_ref:
description:
- 'Selects a field of the pod: supports metadata.name,
metadata.namespace, metadata.labels, metadata.annotations,
spec.nodeName, spec.serviceAccountName, status.hostIP,
status.podIP.'
type: complex
contains:
api_version:
description:
- Version of the schema the FieldPath is written
in terms of, defaults to "v1".
type: str
field_path:
description:
- Path of the field to select in the specified API
version.
type: str
resource_field_ref:
description:
- 'Selects a resource of the container: only resources
limits and requests (limits.cpu, limits.memory, requests.cpu
and requests.memory) are currently supported.'
type: complex
contains:
container_name:
description:
- 'Container name: required for volumes, optional
for env vars'
type: str
divisor:
description:
- Specifies the output format of the exposed resources,
defaults to "1"
type: str
resource:
description:
- 'Required: resource to select'
type: str
secret_key_ref:
description:
- Selects a key of a secret in the pod's namespace
type: complex
contains:
key:
description:
- The key of the secret to select from. Must be
a valid secret key.
type: str
name:
description:
- Name of the referent.
type: str
optional:
description:
- Specify whether the Secret or it's key must be
defined
type: bool
force_pull:
description:
- forcePull describes if the builder should pull the images
from registry prior to building.
type: bool
image_optimization_policy:
description:
- imageOptimizationPolicy describes what optimizations the system
can use when building images to reduce the final size or time
spent building the image. The default policy is 'None' which
means the final build image will be equivalent to an image
created by the Docker build API. The experimental policy 'SkipLayers'
will avoid commiting new layers in between each image step,
and will fail if the Dockerfile cannot provide compatibility
with the 'None' policy. An additional experimental policy
'SkipLayersAndWarn' is the same as 'SkipLayers' but simply
warns if compatibility cannot be preserved.
type: str
no_cache:
description:
- noCache if set to true indicates that the docker build must
be executed with the --no-cache=true flag
type: bool
pull_secret:
description:
- pullSecret is the name of a Secret that would be used for
setting up the authentication for pulling the Docker images
from the private Docker registries
type: complex
contains:
name:
description:
- Name of the referent.
type: str
jenkins_pipeline_strategy:
description:
- JenkinsPipelineStrategy holds the parameters to the Jenkins Pipeline
build strategy. This strategy is in tech preview.
type: complex
contains:
env:
description:
- env contains additional environment variables you want to
pass into a build pipeline.
type: list
contains:
name:
description:
- Name of the environment variable. Must be a C_IDENTIFIER.
type: str
value:
description:
- 'Variable references $(VAR_NAME) are expanded using the
previous defined environment variables in the container
and any service environment variables. If a variable cannot
be resolved, the reference in the input string will be
unchanged. The $(VAR_NAME) syntax can be escaped with
a double $$, ie: $$(VAR_NAME). Escaped references will
never be expanded, regardless of whether the variable
exists or not. Defaults to "".'
type: str
value_from:
description:
- Source for the environment variable's value. Cannot be
used if value is not empty.
type: complex
contains:
config_map_key_ref:
description:
- Selects a key of a ConfigMap.
type: complex
contains:
key:
description:
- The key to select.
type: str
name:
description:
- Name of the referent.
type: str
optional:
description:
- Specify whether the ConfigMap or it's key must
be defined
type: bool
field_ref:
description:
- 'Selects a field of the pod: supports metadata.name,
metadata.namespace, metadata.labels, metadata.annotations,
spec.nodeName, spec.serviceAccountName, status.hostIP,
status.podIP.'
type: complex
contains:
api_version:
description:
- Version of the schema the FieldPath is written
in terms of, defaults to "v1".
type: str
field_path:
description:
- Path of the field to select in the specified API
version.
type: str
resource_field_ref:
description:
- 'Selects a resource of the container: only resources
limits and requests (limits.cpu, limits.memory, requests.cpu
and requests.memory) are currently supported.'
type: complex
contains:
container_name:
description:
- 'Container name: required for volumes, optional
for env vars'
type: str
divisor:
description:
- Specifies the output format of the exposed resources,
defaults to "1"
type: str
resource:
description:
- 'Required: resource to select'
type: str
secret_key_ref:
description:
- Selects a key of a secret in the pod's namespace
type: complex
contains:
key:
description:
- The key of the secret to select from. Must be
a valid secret key.
type: str
name:
description:
- Name of the referent.
type: str
optional:
description:
- Specify whether the Secret or it's key must be
defined
type: bool
jenkinsfile:
description:
- Jenkinsfile defines the optional raw contents of a Jenkinsfile
which defines a Jenkins pipeline build.
type: str
jenkinsfile_path:
description:
- JenkinsfilePath is the optional path of the Jenkinsfile that
will be used to configure the pipeline relative to the root
of the context (contextDir). If both JenkinsfilePath & Jenkinsfile
are both not specified, this defaults to Jenkinsfile in the
root of the specified contextDir.
type: str
source_strategy:
description:
- sourceStrategy holds the parameters to the Source build strategy.
type: complex
contains:
_from:
description:
- from is reference to an DockerImage, ImageStreamTag, or ImageStreamImage
from which the docker image should be pulled
type: complex
contains:
api_version:
description:
- API version of the referent.
type: str
field_path:
description:
- 'If referring to a piece of an object instead of an entire
object, this string should contain a valid JSON/Go field
access statement, such as desiredState.manifest.containers[2].
For example, if the object reference is to a container
within a pod, this would take on a value like: "spec.containers{name}"
(where "name" refers to the name of the container that
triggered the event) or if no container name is specified
"spec.containers[2]" (container with index 2 in this pod).
This syntax is chosen only to have some well-defined way
of referencing a part of an object.'
type: str
kind:
description:
- Kind of the referent.
type: str
name:
description:
- Name of the referent.
type: str
namespace:
description:
- Namespace of the referent.
type: str
resource_version:
description:
- Specific resourceVersion to which this reference is made,
if any.
type: str
uid:
description:
- UID of the referent.
type: str
env:
description:
- env contains additional environment variables you want to
pass into a builder container.
type: list
contains:
name:
description:
- Name of the environment variable. Must be a C_IDENTIFIER.
type: str
value:
description:
- 'Variable references $(VAR_NAME) are expanded using the
previous defined environment variables in the container
and any service environment variables. If a variable cannot
be resolved, the reference in the input string will be
unchanged. The $(VAR_NAME) syntax can be escaped with
a double $$, ie: $$(VAR_NAME). Escaped references will
never be expanded, regardless of whether the variable
exists or not. Defaults to "".'
type: str
value_from:
description:
- Source for the environment variable's value. Cannot be
used if value is not empty.
type: complex
contains:
config_map_key_ref:
description:
- Selects a key of a ConfigMap.
type: complex
contains:
key:
description:
- The key to select.
type: str
name:
description:
- Name of the referent.
type: str
optional:
description:
- Specify whether the ConfigMap or it's key must
be defined
type: bool
field_ref:
description:
- 'Selects a field of the pod: supports metadata.name,
metadata.namespace, metadata.labels, metadata.annotations,
spec.nodeName, spec.serviceAccountName, status.hostIP,
status.podIP.'
type: complex
contains:
api_version:
description:
- Version of the schema the FieldPath is written
in terms of, defaults to "v1".
type: str
field_path:
description:
- Path of the field to select in the specified API
version.
type: str
resource_field_ref:
description:
- 'Selects a resource of the container: only resources
limits and requests (limits.cpu, limits.memory, requests.cpu
and requests.memory) are currently supported.'
type: complex
contains:
container_name:
description:
- 'Container name: required for volumes, optional
for env vars'
type: str
divisor:
description:
- Specifies the output format of the exposed resources,
defaults to "1"
type: str
resource:
description:
- 'Required: resource to select'
type: str
secret_key_ref:
description:
- Selects a key of a secret in the pod's namespace
type: complex
contains:
key:
description:
- The key of the secret to select from. Must be
a valid secret key.
type: str
name:
description:
- Name of the referent.
type: str
optional:
description:
- Specify whether the Secret or it's key must be
defined
type: bool
force_pull:
description:
- forcePull describes if the builder should pull the images
from registry prior to building.
type: bool
incremental:
description:
- incremental flag forces the Source build to do incremental
builds if true.
type: bool
pull_secret:
description:
- pullSecret is the name of a Secret that would be used for
setting up the authentication for pulling the Docker images
from the private Docker registries
type: complex
contains:
name:
description:
- Name of the referent.
type: str
runtime_artifacts:
description:
- 'runtimeArtifacts specifies a list of source/destination pairs
that will be copied from the builder to the runtime image.
sourcePath can be a file or directory. destinationDir must
be a directory. destinationDir can also be empty or equal
to ".", in this case it just refers to the root of WORKDIR.
Deprecated: This feature will be removed in a future release.
Use ImageSource to copy binary artifacts created from one
build into a separate runtime image.'
type: list
contains:
destination_dir:
description:
- destinationDir is the relative directory within the build
directory where files copied from the image are placed.
type: str
source_path:
description:
- sourcePath is the absolute path of the file or directory
inside the image to copy to the build directory. If the
source path ends in /. then the content of the directory
will be copied, but the directory itself will not be created
at the destination.
type: str
runtime_image:
description:
- 'runtimeImage is an optional image that is used to run an
application without unneeded dependencies installed. The building
of the application is still done in the builder image but,
post build, you can copy the needed artifacts in the runtime
image for use. Deprecated: This feature will be removed in
a future release. Use ImageSource to copy binary artifacts
created from one build into a separate runtime image.'
type: complex
contains:
api_version:
description:
- API version of the referent.
type: str
field_path:
description:
- 'If referring to a piece of an object instead of an entire
object, this string should contain a valid JSON/Go field
access statement, such as desiredState.manifest.containers[2].
For example, if the object reference is to a container
within a pod, this would take on a value like: "spec.containers{name}"
(where "name" refers to the name of the container that
triggered the event) or if no container name is specified
"spec.containers[2]" (container with index 2 in this pod).
This syntax is chosen only to have some well-defined way
of referencing a part of an object.'
type: str
kind:
description:
- Kind of the referent.
type: str
name:
description:
- Name of the referent.
type: str
namespace:
description:
- Namespace of the referent.
type: str
resource_version:
description:
- Specific resourceVersion to which this reference is made,
if any.
type: str
uid:
description:
- UID of the referent.
type: str
scripts:
description:
- scripts is the location of Source scripts
type: str
type:
description:
- type is the kind of build strategy.
type: str
successful_builds_history_limit:
description:
- successfulBuildsHistoryLimit is the number of old successful builds
to retain. If not specified, all successful builds are retained.
type: int
triggers:
description:
- triggers determine how new Builds can be launched from a BuildConfig.
If no triggers are defined, a new build can only occur as a result
of an explicit client build creation.
type: list
contains:
bitbucket:
description:
- BitbucketWebHook contains the parameters for a Bitbucket webhook
type of trigger
type: complex
contains:
allow_env:
description:
- allowEnv determines whether the webhook can set environment
variables; can only be set to true for GenericWebHook.
type: bool
secret:
description:
- secret used to validate requests.
type: str
generic:
description:
- generic contains the parameters for a Generic webhook type of
trigger
type: complex
contains:
allow_env:
description:
- allowEnv determines whether the webhook can set environment
variables; can only be set to true for GenericWebHook.
type: bool
secret:
description:
- secret used to validate requests.
type: str
github:
description:
- github contains the parameters for a GitHub webhook type of trigger
type: complex
contains:
allow_env:
description:
- allowEnv determines whether the webhook can set environment
variables; can only be set to true for GenericWebHook.
type: bool
secret:
description:
- secret used to validate requests.
type: str
gitlab:
description:
- GitLabWebHook contains the parameters for a GitLab webhook type
of trigger
type: complex
contains:
allow_env:
description:
- allowEnv determines whether the webhook can set environment
variables; can only be set to true for GenericWebHook.
type: bool
secret:
description:
- secret used to validate requests.
type: str
image_change:
description:
- imageChange contains parameters for an ImageChange type of trigger
type: complex
contains:
_from:
description:
- from is a reference to an ImageStreamTag that will trigger
a build when updated It is optional. If no From is specified,
the From image from the build strategy will be used. Only
one ImageChangeTrigger with an empty From reference is allowed
in a build configuration.
type: complex
contains:
api_version:
description:
- API version of the referent.
type: str
field_path:
description:
- 'If referring to a piece of an object instead of an entire
object, this string should contain a valid JSON/Go field
access statement, such as desiredState.manifest.containers[2].
For example, if the object reference is to a container
within a pod, this would take on a value like: "spec.containers{name}"
(where "name" refers to the name of the container that
triggered the event) or if no container name is specified
"spec.containers[2]" (container with index 2 in this pod).
This syntax is chosen only to have some well-defined way
of referencing a part of an object.'
type: str
kind:
description:
- Kind of the referent.
type: str
name:
description:
- Name of the referent.
type: str
namespace:
description:
- Namespace of the referent.
type: str
resource_version:
description:
- Specific resourceVersion to which this reference is made,
if any.
type: str
uid:
description:
- UID of the referent.
type: str
last_triggered_image_id:
description:
- lastTriggeredImageID is used internally by the ImageChangeController
to save last used image ID for build
type: str
type:
description:
- type is the type of build trigger
type: str
status:
description:
- status holds any relevant information about a build config
type: complex
contains:
last_version:
description:
- lastVersion is used to inform about number of last triggered build.
type: int
kind:
description:
- Kind is a string value representing the REST resource this object represents.
Servers may infer this from the endpoint the client submits requests to. Cannot
be updated. In CamelCase.
type: str
metadata:
description:
- metadata for BuildConfigList.
type: complex
contains:
resource_version:
description:
- String that identifies the server's internal version of this object that
can be used by clients to determine when objects have changed. Value must
be treated as opaque by clients and passed unmodified back to the server.
Populated by the system. Read-only.
type: str
self_link:
description:
- SelfLink is a URL representing this object. Populated by the system. Read-only.
type: str
'''
if __name__ == '__main__':
main()
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5563,
13,
198,
9641,
62,
29373,
25,
362,
13,
18,
13,
15,
198,
9800,
25,
4946,
33377,
4275,
44813,
29323,
8,
198,
25811,
25,
198,
220,
40391,
62,
2539,
25,
198,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
532,
29130,
973,
284,
2018,
284,
262,
7824,
13,
198,
220,
5051,
62,
7753,
25,
198,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
532,
10644,
284,
257,
10703,
973,
284,
8323,
5344,
351,
262,
7824,
13,
198,
220,
220,
220,
2099,
25,
3108,
198,
220,
4732,
25,
198,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
532,
383,
1438,
286,
257,
4732,
1043,
287,
262,
12554,
527,
3262,
274,
4566,
2393,
13,
198,
220,
14257,
25,
198,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
532,
27882,
14257,
5072,
422,
262,
4946,
33377,
31904,
13,
5972,
2667,
7508,
318,
3194,
284,
509,
3266,
49201,
47429,
13,
6404,
198,
220,
220,
220,
4277,
25,
3991,
198,
220,
220,
220,
2099,
25,
20512,
198,
220,
2700,
25,
198,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
532,
1002,
900,
284,
327,
7,
17821,
828,
290,
314,
7,
5219,
8,
318,
327,
7,
25579,
828,
281,
4683,
2134,
481,
6153,
11,
198,
220,
220,
220,
220,
220,
290,
8341,
481,
307,
6928,
11,
2138,
621,
23791,
13,
198,
220,
220,
220,
4277,
25,
3991,
198,
220,
220,
220,
2099,
25,
20512,
198,
220,
2583,
25,
198,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
532,
44290,
257,
10289,
329,
257,
919,
278,
262,
12554,
527,
3262,
274,
7824,
13,
198,
220,
1994,
62,
7753,
25,
198,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
532,
10644,
284,
257,
1994,
2393,
973,
284,
8323,
5344,
351,
262,
7824,
13,
198,
220,
220,
220,
2099,
25,
3108,
198,
220,
479,
549,
721,
261,
5647,
25,
198,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
532,
10644,
284,
281,
4683,
12554,
527,
3262,
274,
4566,
2393,
13,
1002,
407,
2810,
11,
290,
645,
584,
4637,
198,
220,
220,
220,
220,
220,
3689,
389,
2810,
11,
262,
9808,
29323,
5456,
481,
2230,
284,
3440,
262,
4277,
198,
220,
220,
220,
220,
220,
8398,
2393,
422,
314,
7,
93,
11757,
74,
3266,
14,
11250,
13,
17752,
737,
198,
220,
220,
220,
2099,
25,
3108,
198,
220,
9206,
25,
198,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
532,
44290,
257,
9206,
329,
14320,
284,
262,
7824,
13,
5765,
287,
17856,
351,
314,
7,
29460,
737,
198,
220,
8271,
62,
46758,
25,
198,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
532,
44290,
262,
575,
2390,
43,
6770,
329,
262,
2134,
11,
17286,
278,
597,
13103,
10007,
198,
220,
220,
220,
220,
220,
5292,
284,
8160,
2134,
12608,
13,
198,
220,
220,
220,
2099,
25,
8633,
198,
220,
12351,
25,
198,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
532,
44290,
257,
3108,
284,
257,
2393,
7268,
262,
575,
2390,
43,
6770,
286,
262,
2134,
13,
13859,
935,
198,
220,
220,
220,
220,
220,
8568,
351,
314,
7,
31092,
62,
46758,
737,
198,
220,
220,
220,
2099,
25,
3108,
198,
220,
264,
6649,
62,
6888,
62,
22583,
25,
198,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
532,
10644,
284,
257,
7257,
10703,
973,
284,
8323,
5344,
351,
262,
7824,
13,
198,
220,
220,
220,
2099,
25,
3108,
198,
220,
1181,
25,
198,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
532,
360,
13221,
274,
611,
281,
2134,
815,
307,
2727,
11,
39378,
11,
393,
13140,
13,
1649,
900,
284,
198,
220,
220,
220,
220,
220,
327,
7,
25579,
828,
262,
2134,
481,
307,
2727,
11,
611,
340,
857,
407,
2152,
11,
393,
39378,
11,
611,
198,
220,
220,
220,
220,
220,
11507,
3815,
13238,
422,
262,
4683,
2134,
338,
12608,
11,
290,
13140,
11,
198,
220,
220,
220,
220,
220,
611,
900,
284,
327,
7,
8937,
298,
737,
317,
8529,
4905,
2482,
287,
35981,
8341,
290,
19698,
198,
220,
220,
220,
220,
220,
48589,
3166,
11,
351,
8341,
852,
23791,
656,
257,
3748,
900,
286,
3815,
13,
1002,
257,
1351,
198,
220,
220,
220,
220,
220,
4909,
257,
22155,
351,
257,
314,
7,
3672,
8,
393,
314,
7,
4906,
8,
11688,
11,
257,
10039,
20121,
198,
220,
220,
220,
220,
220,
318,
6157,
11,
810,
1981,
4847,
351,
257,
12336,
314,
7,
3672,
62,
8,
393,
314,
7,
4906,
8,
198,
220,
220,
220,
220,
220,
389,
23791,
13,
1675,
2700,
262,
9014,
286,
8341,
11,
900,
262,
314,
7,
3174,
8,
3038,
284,
327,
7,
17821,
737,
198,
220,
220,
220,
4277,
25,
1944,
198,
220,
220,
220,
7747,
25,
198,
220,
220,
220,
532,
1944,
198,
220,
220,
220,
532,
13717,
198,
220,
20579,
25,
198,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
532,
44290,
257,
20579,
329,
14320,
284,
262,
7824,
13,
198,
220,
11767,
62,
45163,
25,
198,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
532,
10127,
393,
407,
284,
11767,
262,
7824,
4382,
338,
25952,
20835,
13,
198,
220,
220,
220,
2099,
25,
20512,
198,
8897,
18883,
25,
198,
12,
9808,
29323,
6624,
657,
13,
18,
13,
18,
198,
7061,
6,
198,
198,
6369,
2390,
6489,
1546,
796,
705,
7061,
198,
7061,
6,
198,
198,
26087,
27064,
796,
705,
7061,
198,
15042,
62,
9641,
25,
198,
220,
2099,
25,
4731,
198,
220,
6764,
25,
19390,
276,
7824,
2196,
198,
11249,
62,
11250,
62,
4868,
25,
198,
220,
2099,
25,
3716,
198,
220,
4504,
25,
618,
314,
7,
5219,
8,
796,
327,
7,
25579,
8,
198,
220,
4909,
25,
198,
220,
220,
220,
40391,
62,
9641,
25,
198,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
532,
3486,
3824,
6900,
15738,
262,
2196,
276,
32815,
286,
428,
10552,
286,
281,
2134,
13,
198,
220,
220,
220,
220,
220,
220,
220,
2930,
690,
815,
10385,
8018,
3897,
5356,
284,
262,
3452,
5387,
1988,
11,
290,
198,
220,
220,
220,
220,
220,
220,
220,
743,
4968,
43483,
1143,
3815,
13,
198,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
3709,
25,
198,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
532,
3709,
318,
257,
1351,
286,
1382,
4566,
82,
198,
220,
220,
220,
220,
220,
2099,
25,
1351,
198,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
40391,
62,
9641,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
3486,
3824,
6900,
15738,
262,
2196,
276,
32815,
286,
428,
10552,
286,
281,
2134,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2930,
690,
815,
10385,
8018,
3897,
5356,
284,
262,
3452,
5387,
1988,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
743,
4968,
43483,
1143,
3815,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
1611,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
14927,
318,
257,
4731,
1988,
10200,
262,
30617,
8271,
428,
2134,
6870,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2930,
690,
743,
13249,
428,
422,
262,
36123,
262,
5456,
850,
24883,
7007,
284,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26003,
307,
6153,
13,
554,
43281,
20448,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
20150,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
20150,
329,
10934,
16934,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
37647,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
47939,
318,
281,
555,
7249,
1522,
1994,
1988,
3975,
8574,
351,
257,
8271,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
326,
743,
307,
900,
416,
7097,
4899,
284,
3650,
290,
19818,
14977,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20150,
13,
1119,
389,
407,
12405,
540,
290,
815,
307,
17232,
618,
30620,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5563,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
965,
11,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13946,
62,
3672,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
383,
1438,
286,
262,
13946,
543,
262,
2134,
14448,
284,
13,
770,
318,
973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
284,
15714,
4133,
351,
976,
1438,
290,
25745,
287,
1180,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
23163,
13,
770,
2214,
318,
407,
900,
6609,
826,
783,
290,
2471,
5847,
332,
318,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1016,
284,
8856,
340,
611,
900,
287,
2251,
393,
4296,
2581,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6282,
62,
16514,
27823,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
21582,
14967,
27823,
318,
257,
41033,
10200,
262,
4382,
640,
618,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
428,
2134,
373,
2727,
13,
632,
318,
407,
11462,
284,
307,
900,
287,
4325,
12,
19052,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1502,
1973,
4553,
4560,
13,
1012,
2334,
743,
407,
900,
428,
1988,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
632,
318,
7997,
287,
30978,
2091,
2670,
1296,
290,
318,
287,
18119,
13,
8099,
4817,
416,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1080,
13,
4149,
12,
8807,
13,
35886,
329,
8341,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
39948,
62,
2164,
558,
62,
41007,
62,
43012,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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6764,
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20150,
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1351,
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13,
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4909,
25,
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220,
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220,
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8271,
62,
9641,
25,
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6764,
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220,
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326,
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262,
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338,
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2134,
326,
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307,
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7534,
284,
5004,
618,
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220,
220,
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220,
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220,
220,
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220,
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220,
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220,
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220,
220,
220,
220,
5563,
423,
3421,
13,
11052,
1276,
307,
5716,
355,
32191,
198,
220,
220,
220,
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7534,
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736,
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13,
8099,
4817,
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220,
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13,
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12,
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13,
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8726,
25,
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6764,
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11280,
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257,
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262,
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13,
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13,
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1738,
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6764,
25,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
262,
366,
50015,
1,
3722,
13,
1002,
428,
1988,
318,
6565,
612,
318,
645,
1321,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1695,
13,
317,
23219,
10212,
6945,
281,
14626,
3722,
2438,
475,
857,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
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407,
20957,
340,
13,
198,
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3722,
25,
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220,
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6764,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
19580,
286,
262,
4905,
13,
1881,
286,
25,
366,
33244,
1,
393,
366,
50015,
1911,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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2099,
25,
965,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
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14722,
25,
198,
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220,
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220,
220,
220,
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220,
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220,
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6764,
25,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
9347,
286,
4731,
8251,
290,
3815,
326,
460,
307,
973,
284,
16481,
290,
17851,
1096,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
29982,
290,
2922,
8,
5563,
13,
1737,
2872,
2922,
669,
286,
30330,
20624,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
2594,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
965,
11,
965,
198,
220,
220,
220,
220,
220,
220,
220,
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4909,
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25,
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6764,
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2512,
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220,
788,
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2314,
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422,
262,
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12,
8367,
3650,
1566,
198,
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220,
220,
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220,
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6764,
25,
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220,
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2173,
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13,
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220,
220,
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220,
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25,
20512,
198,
220,
220,
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1611,
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6764,
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14927,
286,
262,
6773,
429,
13,
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220,
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25,
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220,
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1438,
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6764,
25,
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220,
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220,
220,
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220,
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220,
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220,
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532,
6530,
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429,
13,
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25,
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220,
220,
220,
220,
220,
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220,
220,
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220,
220,
220,
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334,
312,
25,
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6764,
25,
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220,
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25105,
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220,
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8271,
62,
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25,
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6764,
25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
1052,
32191,
1988,
326,
6870,
262,
5387,
2196,
286,
428,
2134,
198,
220,
220,
220,
220,
220,
220,
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326,
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307,
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7534,
284,
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618,
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423,
3421,
13,
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220,
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220,
220,
220,
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220,
1737,
307,
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329,
16915,
1673,
13382,
11,
1487,
13326,
11,
290,
262,
198,
220,
220,
220,
220,
220,
220,
220,
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220,
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220,
220,
220,
220,
220,
2342,
4905,
319,
257,
8271,
393,
900,
286,
4133,
13,
1012,
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1276,
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220,
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777,
3815,
355,
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290,
3804,
555,
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284,
262,
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13,
1119,
198,
220,
220,
220,
220,
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220,
220,
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220,
220,
220,
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220,
743,
691,
307,
4938,
329,
257,
1948,
8271,
393,
900,
286,
4133,
13,
8099,
4817,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
416,
262,
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13,
4149,
12,
8807,
13,
11052,
1276,
307,
5716,
355,
32191,
416,
7534,
198,
220,
220,
220,
220,
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220,
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290,
764,
198,
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220,
2099,
25,
965,
198,
220,
220,
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220,
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220,
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220,
2116,
62,
8726,
25,
198,
220,
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6764,
25,
198,
220,
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220,
220,
220,
532,
12189,
11280,
318,
257,
10289,
10200,
428,
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13,
8099,
4817,
416,
262,
1080,
13,
198,
220,
220,
220,
220,
220,
220,
220,
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4149,
12,
8807,
13,
198,
220,
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220,
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220,
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220,
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2099,
25,
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220,
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334,
312,
25,
198,
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6764,
25,
198,
220,
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220,
220,
220,
220,
220,
220,
532,
25105,
318,
262,
3748,
287,
640,
290,
2272,
1988,
329,
428,
2134,
13,
632,
318,
6032,
198,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
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220,
7560,
416,
262,
4382,
319,
4388,
6282,
286,
257,
8271,
290,
318,
198,
220,
220,
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220,
407,
3142,
284,
1487,
319,
350,
3843,
4560,
13,
8099,
4817,
416,
262,
1080,
13,
198,
220,
220,
220,
220,
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220,
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220,
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4149,
12,
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13,
198,
220,
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2099,
25,
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1020,
25,
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6764,
25,
198,
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220,
220,
532,
1020,
6622,
477,
262,
5128,
3306,
284,
4439,
257,
649,
1382,
11,
290,
262,
3403,
198,
220,
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284,
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606,
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25,
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4909,
25,
198,
220,
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11939,
62,
25124,
1370,
62,
43012,
25,
198,
220,
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6764,
25,
198,
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11939,
20489,
1370,
12211,
82,
318,
281,
11902,
9478,
287,
4201,
11,
14789,
198,
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262,
640,
618,
257,
1382,
24573,
3011,
7530,
287,
262,
1080,
11,
326,
198,
220,
220,
220,
220,
220,
220,
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1382,
743,
307,
4075,
319,
257,
10139,
878,
262,
1080,
10630,
8404,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
284,
23654,
262,
1382,
26,
1988,
1276,
307,
3967,
18253,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
493,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4054,
62,
11249,
82,
62,
23569,
62,
32374,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
4054,
15580,
82,
18122,
39184,
318,
262,
1271,
286,
1468,
4054,
12188,
284,
12377,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1002,
407,
7368,
11,
477,
4054,
12188,
389,
17383,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
493,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10139,
62,
19738,
273,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
10139,
17563,
273,
318,
257,
31870,
543,
1276,
307,
2081,
329,
262,
1382,
24573,
284,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4197,
319,
257,
10139,
1002,
18038,
11,
340,
460,
307,
23170,
4651,
416,
4277,
1382,
18666,
2771,
801,
273,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3815,
329,
262,
13946,
13,
1002,
900,
284,
281,
6565,
3975,
393,
257,
3975,
351,
597,
3815,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4277,
1382,
18666,
2771,
801,
273,
3815,
389,
9514,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
965,
11,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5072,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
5072,
8477,
262,
25716,
2939,
262,
20561,
815,
4439,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2939,
62,
23912,
1424,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
2939,
17822,
1424,
8160,
257,
1351,
286,
14722,
326,
389,
5625,
284,
262,
7186,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2939,
13,
1002,
612,
389,
3294,
14722,
351,
262,
976,
1438,
788,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
938,
530,
287,
262,
1351,
318,
973,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
1351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
1438,
15738,
262,
1438,
286,
262,
6167,
13,
632,
1276,
423,
1729,
12,
22570,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4129,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
1988,
15738,
262,
18875,
1988,
286,
262,
6167,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
62,
21078,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
23691,
23725,
318,
262,
1438,
286,
257,
3943,
326,
561,
307,
973,
329,
4634,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
510,
262,
18239,
329,
23710,
262,
25716,
4574,
284,
18239,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9343,
25716,
33432,
357,
273,
25716,
14699,
737,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
284,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
284,
15738,
281,
11902,
4067,
284,
4574,
262,
5072,
286,
428,
1382,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
284,
13,
14927,
1276,
307,
530,
286,
705,
5159,
12124,
24835,
6,
393,
705,
35,
12721,
5159,
4458,
770,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
481,
307,
973,
284,
804,
510,
257,
25716,
2939,
16099,
284,
4574,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
284,
13,
554,
262,
1339,
286,
281,
7412,
12124,
24835,
11,
262,
7412,
12124,
24835,
481,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
307,
3114,
329,
287,
262,
25745,
286,
262,
1382,
4556,
28531,
10223,
318,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7368,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
40391,
62,
9641,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
7824,
2196,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2214,
62,
6978,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
1532,
9759,
284,
257,
3704,
286,
281,
2134,
2427,
286,
281,
2104,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2134,
11,
428,
4731,
815,
3994,
257,
4938,
19449,
14,
5247,
2214,
1895,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2643,
11,
884,
355,
10348,
9012,
13,
805,
8409,
13,
3642,
50221,
58,
17,
4083,
1114,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1672,
11,
611,
262,
2134,
4941,
318,
284,
257,
9290,
1626,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
24573,
11,
428,
561,
1011,
319,
257,
1988,
588,
25,
366,
16684,
13,
3642,
50221,
90,
3672,
36786,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
3003,
366,
3672,
1,
10229,
284,
262,
1438,
286,
262,
9290,
326,
13973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
262,
1785,
8,
393,
611,
645,
9290,
1438,
318,
7368,
366,
16684,
13,
3642,
50221,
58,
17,
30866,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
34924,
351,
6376,
362,
287,
428,
24573,
737,
770,
15582,
318,
7147,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
691,
284,
423,
617,
880,
12,
23211,
835,
286,
32578,
257,
636,
286,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
281,
2134,
2637,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1611,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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9672,
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31577,
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12384,
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220,
220,
220,
220,
10965,
62,
15908,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
10965,
35277,
318,
262,
8619,
810,
262,
3696,
422,
262,
3200,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
815,
307,
1695,
329,
262,
1382,
640,
13,
1114,
262,
8090,
1382,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4811,
11,
777,
481,
307,
25077,
656,
257,
9290,
810,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
25432,
4226,
4539,
13,
11450,
11,
618,
262,
4226,
20271,
11,
477,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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220,
220,
220,
220,
220,
220,
220,
3696,
25077,
481,
307,
40122,
515,
284,
6632,
4129,
13,
1114,
262,
25716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
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220,
220,
220,
220,
220,
1382,
4811,
11,
777,
481,
307,
18984,
656,
262,
1382,
8619,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
810,
262,
25716,
7753,
318,
5140,
11,
523,
2985,
460,
27841,
393,
27975,
56,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
606,
1141,
36253,
1382,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3200,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
3200,
318,
257,
4941,
284,
281,
4683,
3200,
326,
345,
765,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
284,
779,
287,
534,
1382,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2723,
62,
21078,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
366,
10459,
23725,
318,
262,
1438,
286,
257,
3943,
326,
561,
307,
973,
329,
4634,
59,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3467,
510,
262,
18239,
329,
45973,
2839,
16099,
13,
383,
3200,
59,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3467,
4909,
4938,
18031,
329,
6569,
16099,
11,
810,
262,
59,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3467,
1366,
338,
1994,
2380,
262,
18239,
2446,
284,
307,
973,
290,
59,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3467,
1988,
318,
262,
2779,
2414,
30240,
18031,
13,
36848,
6284,
5050,
59,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3467,
389,
25,
26678,
12,
19734,
2539,
526,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
2099,
286,
1382,
5128,
284,
2453,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4811,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
4811,
15738,
703,
284,
1620,
257,
1382,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2183,
62,
2536,
4338,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
2183,
13290,
4338,
6622,
262,
10007,
284,
262,
8562,
1382,
4811,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
6738,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
422,
318,
4941,
284,
281,
25716,
5159,
11,
7412,
12124,
24835,
11,
393,
7412,
12124,
5159,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
422,
543,
262,
36253,
2939,
815,
307,
5954,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
40391,
62,
9641,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
7824,
2196,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2214,
62,
6978,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
1532,
9759,
284,
257,
3704,
286,
281,
2134,
2427,
286,
281,
2104,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2134,
11,
428,
4731,
815,
3994,
257,
4938,
19449,
14,
5247,
2214,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1895,
2643,
11,
884,
355,
10348,
9012,
13,
805,
8409,
13,
3642,
50221,
58,
17,
4083,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1114,
1672,
11,
611,
262,
2134,
4941,
318,
284,
257,
9290,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1626,
257,
24573,
11,
428,
561,
1011,
319,
257,
1988,
588,
25,
366,
16684,
13,
3642,
50221,
90,
3672,
36786,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
3003,
366,
3672,
1,
10229,
284,
262,
1438,
286,
262,
9290,
326,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13973,
262,
1785,
8,
393,
611,
645,
9290,
1438,
318,
7368,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
16684,
13,
3642,
50221,
58,
17,
30866,
357,
34924,
351,
6376,
362,
287,
428,
24573,
737,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
770,
15582,
318,
7147,
691,
284,
423,
617,
880,
12,
23211,
835,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
286,
32578,
257,
636,
286,
281,
2134,
2637,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1611,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
14927,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
25745,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
28531,
10223,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8271,
62,
9641,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
17377,
8271,
14815,
284,
543,
428,
4941,
318,
925,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
597,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
334,
312,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
25105,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1382,
62,
15042,
62,
9641,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
1382,
2969,
3824,
6900,
318,
262,
9167,
7824,
2196,
329,
262,
10934,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2134,
11389,
1143,
290,
3804,
284,
262,
2183,
27098,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17365,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
17365,
4909,
3224,
2858,
9633,
345,
765,
284,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1208,
656,
257,
27098,
9290,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
1351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
2858,
7885,
13,
12039,
307,
257,
327,
62,
25256,
5064,
38311,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
43015,
10288,
29568,
53,
1503,
62,
20608,
8,
389,
9902,
1262,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2180,
5447,
2858,
9633,
287,
262,
9290,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
597,
2139,
2858,
9633,
13,
1002,
257,
7885,
2314,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
307,
12939,
11,
262,
4941,
287,
262,
5128,
4731,
481,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
21588,
13,
383,
29568,
53,
1503,
62,
20608,
8,
15582,
460,
307,
13537,
351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
4274,
32382,
11,
37941,
25,
32382,
7,
53,
1503,
62,
20608,
737,
16319,
5813,
10288,
481,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1239,
307,
9902,
11,
7692,
286,
1771,
262,
7885,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7160,
393,
407,
13,
2896,
13185,
284,
366,
1911,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
62,
6738,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
8090,
329,
262,
2858,
7885,
338,
1988,
13,
26003,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
973,
611,
1988,
318,
407,
6565,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4566,
62,
8899,
62,
2539,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
9683,
82,
257,
1994,
286,
257,
17056,
13912,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
383,
1994,
284,
2922,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
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3722,
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4909,
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40391,
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220,
220,
220,
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10628,
286,
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32815,
262,
7663,
15235,
318,
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220,
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220,
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220,
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2846,
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26235,
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2214,
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8271,
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3245,
62,
5420,
25,
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7095,
290,
7007,
357,
49196,
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11,
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13,
31673,
11,
7007,
13,
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13,
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4909,
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9290,
62,
3672,
25,
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6764,
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220,
220,
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532,
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29869,
1438,
25,
2672,
329,
15343,
11,
11902,
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220,
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220,
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329,
17365,
410,
945,
6,
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220,
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25,
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2659,
271,
273,
25,
198,
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6764,
25,
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532,
18291,
6945,
262,
5072,
5794,
286,
262,
7362,
4133,
11,
198,
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26235,
284,
366,
16,
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8271,
25,
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6764,
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532,
705,
37374,
25,
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2922,
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3200,
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2539,
62,
5420,
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6764,
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220,
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220,
220,
220,
220,
220,
532,
9683,
82,
257,
1994,
286,
257,
3200,
287,
262,
24573,
338,
25745,
198,
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220,
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220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
383,
1994,
286,
262,
3200,
284,
2922,
422,
13,
12039,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
4938,
3200,
1994,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11902,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
18291,
1958,
1771,
262,
3943,
393,
340,
338,
1994,
1276,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5447,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15651,
62,
45986,
62,
44971,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
15651,
35,
12721,
39105,
481,
1249,
2491,
25716,
9729,
357,
392,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1382,
25716,
4263,
8,
422,
2641,
262,
25716,
9290,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2700,
62,
31216,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
2700,
42940,
8477,
611,
262,
10444,
815,
17425,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1382,
24573,
284,
1464,
2834,
262,
4263,
329,
262,
27098,
393,
691,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2834,
611,
340,
318,
407,
1944,
15726,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2834,
62,
21078,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
2834,
23725,
318,
262,
1438,
286,
257,
3943,
326,
561,
307,
973,
329,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4634,
510,
262,
18239,
329,
10427,
262,
25716,
4263,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
422,
262,
2839,
25716,
4214,
1678,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13141,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
13141,
318,
257,
1351,
286,
3224,
13141,
326,
481,
307,
3017,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
287,
262,
1382,
24573,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
1351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3817,
62,
6978,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
3817,
15235,
318,
262,
3108,
379,
543,
284,
3817,
262,
3200,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3200,
62,
10459,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
3200,
7416,
318,
257,
4941,
284,
262,
3200,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
36253,
62,
2536,
4338,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
36253,
13290,
4338,
6622,
262,
10007,
284,
262,
25716,
1382,
4811,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
6738,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
422,
318,
4941,
284,
281,
25716,
5159,
11,
7412,
12124,
24835,
11,
393,
7412,
12124,
5159,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
422,
543,
262,
36253,
2939,
815,
307,
5954,
262,
7186,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2939,
481,
307,
973,
287,
262,
16034,
1627,
286,
262,
25716,
7753,
329,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
1988,
62,
6738,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
8090,
329,
262,
2858,
7885,
338,
1988,
13,
26003,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
973,
611,
1988,
318,
407,
6565,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4566,
62,
8899,
62,
2539,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
9683,
82,
257,
1994,
286,
257,
17056,
13912,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
383,
1994,
284,
2922,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11902,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
18291,
1958,
1771,
262,
17056,
13912,
393,
340,
338,
1994,
1276,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
307,
5447,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2214,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
17563,
82,
257,
2214,
286,
262,
24573,
25,
6971,
20150,
13,
3672,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20150,
13,
14933,
10223,
11,
20150,
13,
23912,
1424,
11,
20150,
13,
34574,
602,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1020,
13,
17440,
5376,
11,
1020,
13,
15271,
30116,
5376,
11,
3722,
13,
4774,
4061,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3722,
13,
33320,
4061,
2637,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
40391,
62,
9641,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
10628,
286,
262,
32815,
262,
7663,
15235,
318,
3194,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
287,
2846,
286,
11,
26235,
284,
366,
85,
16,
1911,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2214,
62,
6978,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
10644,
286,
262,
2214,
284,
2922,
287,
262,
7368,
7824,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2196,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8271,
62,
3245,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
17563,
82,
257,
8271,
286,
262,
9290,
25,
691,
4133,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7095,
290,
7007,
357,
49196,
13,
36166,
11,
7095,
13,
31673,
11,
7007,
13,
36166,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
7007,
13,
31673,
8,
389,
3058,
4855,
2637,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9290,
62,
3672,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
29869,
1438,
25,
2672,
329,
15343,
11,
11902,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
17365,
410,
945,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2659,
271,
273,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
18291,
6945,
262,
5072,
5794,
286,
262,
7362,
4133,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26235,
284,
366,
16,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8271,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
37374,
25,
8271,
284,
2922,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3200,
62,
2539,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
9683,
82,
257,
1994,
286,
257,
3200,
287,
262,
24573,
338,
25745,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
383,
1994,
286,
262,
3200,
284,
2922,
422,
13,
12039,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
4938,
3200,
1994,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11902,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
18291,
1958,
1771,
262,
3943,
393,
340,
338,
1994,
1276,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5447,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
36253,
7753,
62,
6978,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
36253,
7753,
15235,
318,
262,
3108,
286,
262,
25716,
7753,
326,
481,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
973,
284,
1382,
262,
25716,
2939,
11,
3585,
284,
262,
6808,
286,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4732,
357,
22866,
35277,
737,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17365,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
17365,
4909,
3224,
2858,
9633,
345,
765,
284,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1208,
656,
257,
27098,
9290,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
1351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
2858,
7885,
13,
12039,
307,
257,
327,
62,
25256,
5064,
38311,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
43015,
10288,
29568,
53,
1503,
62,
20608,
8,
389,
9902,
1262,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2180,
5447,
2858,
9633,
287,
262,
9290,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
597,
2139,
2858,
9633,
13,
1002,
257,
7885,
2314,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
307,
12939,
11,
262,
4941,
287,
262,
5128,
4731,
481,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
21588,
13,
383,
29568,
53,
1503,
62,
20608,
8,
15582,
460,
307,
13537,
351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
4274,
32382,
11,
37941,
25,
32382,
7,
53,
1503,
62,
20608,
737,
16319,
5813,
10288,
481,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1239,
307,
9902,
11,
7692,
286,
1771,
262,
7885,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7160,
393,
407,
13,
2896,
13185,
284,
366,
1911,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
62,
6738,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
8090,
329,
262,
2858,
7885,
338,
1988,
13,
26003,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
973,
611,
1988,
318,
407,
6565,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4566,
62,
8899,
62,
2539,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
9683,
82,
257,
1994,
286,
257,
17056,
13912,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
383,
1994,
284,
2922,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11902,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
18291,
1958,
1771,
262,
17056,
13912,
393,
340,
338,
1994,
1276,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
307,
5447,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2214,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
17563,
82,
257,
2214,
286,
262,
24573,
25,
6971,
20150,
13,
3672,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20150,
13,
14933,
10223,
11,
20150,
13,
23912,
1424,
11,
20150,
13,
34574,
602,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1020,
13,
17440,
5376,
11,
1020,
13,
15271,
30116,
5376,
11,
3722,
13,
4774,
4061,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3722,
13,
33320,
4061,
2637,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
40391,
62,
9641,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
10628,
286,
262,
32815,
262,
7663,
15235,
318,
3194,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
287,
2846,
286,
11,
26235,
284,
366,
85,
16,
1911,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2214,
62,
6978,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
10644,
286,
262,
2214,
284,
2922,
287,
262,
7368,
7824,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2196,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8271,
62,
3245,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
17563,
82,
257,
8271,
286,
262,
9290,
25,
691,
4133,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7095,
290,
7007,
357,
49196,
13,
36166,
11,
7095,
13,
31673,
11,
7007,
13,
36166,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
7007,
13,
31673,
8,
389,
3058,
4855,
2637,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9290,
62,
3672,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
29869,
1438,
25,
2672,
329,
15343,
11,
11902,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
17365,
410,
945,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2659,
271,
273,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
18291,
6945,
262,
5072,
5794,
286,
262,
7362,
4133,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26235,
284,
366,
16,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8271,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
37374,
25,
8271,
284,
2922,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3200,
62,
2539,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
9683,
82,
257,
1994,
286,
257,
3200,
287,
262,
24573,
338,
25745,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
383,
1994,
286,
262,
3200,
284,
2922,
422,
13,
12039,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
4938,
3200,
1994,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11902,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
18291,
1958,
1771,
262,
3943,
393,
340,
338,
1994,
1276,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5447,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2700,
62,
31216,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
2700,
42940,
8477,
611,
262,
27098,
815,
2834,
262,
4263,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
422,
20478,
3161,
284,
2615,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2939,
62,
40085,
1634,
62,
30586,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
2939,
27871,
320,
1634,
36727,
8477,
644,
41446,
262,
1080,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
460,
779,
618,
2615,
4263,
284,
4646,
262,
2457,
2546,
393,
640,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3377,
2615,
262,
2939,
13,
383,
4277,
2450,
318,
705,
14202,
6,
543,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1724,
262,
2457,
1382,
2939,
481,
307,
7548,
284,
281,
2939,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2727,
416,
262,
25716,
1382,
7824,
13,
383,
11992,
2450,
705,
50232,
43,
6962,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
481,
3368,
725,
1780,
649,
11685,
287,
1022,
1123,
2939,
2239,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
481,
2038,
611,
262,
25716,
7753,
2314,
2148,
17764,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
351,
262,
705,
14202,
6,
2450,
13,
1052,
3224,
11992,
2450,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
50232,
43,
6962,
1870,
54,
1501,
6,
318,
262,
976,
355,
705,
50232,
43,
6962,
6,
475,
2391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
22145,
611,
17764,
2314,
307,
17232,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
645,
62,
23870,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
645,
30562,
611,
900,
284,
2081,
9217,
326,
262,
36253,
1382,
1276,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
307,
10945,
351,
262,
1377,
3919,
12,
23870,
28,
7942,
6056,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2834,
62,
21078,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
2834,
23725,
318,
262,
1438,
286,
257,
3943,
326,
561,
307,
973,
329,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4634,
510,
262,
18239,
329,
10427,
262,
25716,
4263,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
422,
262,
2839,
25716,
4214,
1678,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
474,
268,
5331,
62,
79,
541,
4470,
62,
2536,
4338,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
21835,
47,
541,
4470,
13290,
4338,
6622,
262,
10007,
284,
262,
21835,
37709,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1382,
4811,
13,
770,
4811,
318,
287,
7261,
12714,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
17365,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
17365,
4909,
3224,
2858,
9633,
345,
765,
284,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1208,
656,
257,
1382,
11523,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
1351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
2858,
7885,
13,
12039,
307,
257,
327,
62,
25256,
5064,
38311,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
43015,
10288,
29568,
53,
1503,
62,
20608,
8,
389,
9902,
1262,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2180,
5447,
2858,
9633,
287,
262,
9290,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
597,
2139,
2858,
9633,
13,
1002,
257,
7885,
2314,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
307,
12939,
11,
262,
4941,
287,
262,
5128,
4731,
481,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
21588,
13,
383,
29568,
53,
1503,
62,
20608,
8,
15582,
460,
307,
13537,
351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
4274,
32382,
11,
37941,
25,
32382,
7,
53,
1503,
62,
20608,
737,
16319,
5813,
10288,
481,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1239,
307,
9902,
11,
7692,
286,
1771,
262,
7885,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7160,
393,
407,
13,
2896,
13185,
284,
366,
1911,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
62,
6738,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
8090,
329,
262,
2858,
7885,
338,
1988,
13,
26003,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
973,
611,
1988,
318,
407,
6565,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4566,
62,
8899,
62,
2539,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
9683,
82,
257,
1994,
286,
257,
17056,
13912,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
383,
1994,
284,
2922,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11902,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
18291,
1958,
1771,
262,
17056,
13912,
393,
340,
338,
1994,
1276,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
307,
5447,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2214,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
17563,
82,
257,
2214,
286,
262,
24573,
25,
6971,
20150,
13,
3672,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20150,
13,
14933,
10223,
11,
20150,
13,
23912,
1424,
11,
20150,
13,
34574,
602,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1020,
13,
17440,
5376,
11,
1020,
13,
15271,
30116,
5376,
11,
3722,
13,
4774,
4061,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3722,
13,
33320,
4061,
2637,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
40391,
62,
9641,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
10628,
286,
262,
32815,
262,
7663,
15235,
318,
3194,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
287,
2846,
286,
11,
26235,
284,
366,
85,
16,
1911,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2214,
62,
6978,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
10644,
286,
262,
2214,
284,
2922,
287,
262,
7368,
7824,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2196,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8271,
62,
3245,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
17563,
82,
257,
8271,
286,
262,
9290,
25,
691,
4133,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7095,
290,
7007,
357,
49196,
13,
36166,
11,
7095,
13,
31673,
11,
7007,
13,
36166,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
7007,
13,
31673,
8,
389,
3058,
4855,
2637,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9290,
62,
3672,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
29869,
1438,
25,
2672,
329,
15343,
11,
11902,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
17365,
410,
945,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2659,
271,
273,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
18291,
6945,
262,
5072,
5794,
286,
262,
7362,
4133,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26235,
284,
366,
16,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8271,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
37374,
25,
8271,
284,
2922,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3200,
62,
2539,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
9683,
82,
257,
1994,
286,
257,
3200,
287,
262,
24573,
338,
25745,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
383,
1994,
286,
262,
3200,
284,
2922,
422,
13,
12039,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
4938,
3200,
1994,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11902,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
18291,
1958,
1771,
262,
3943,
393,
340,
338,
1994,
1276,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5447,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
474,
268,
5331,
7753,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
21835,
7753,
15738,
262,
11902,
8246,
10154,
286,
257,
21835,
7753,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
543,
15738,
257,
21835,
11523,
1382,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
474,
268,
5331,
7753,
62,
6978,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
21835,
7753,
15235,
318,
262,
11902,
3108,
286,
262,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
43015,
10288,
29568,
53,
1503,
62,
20608,
8,
389,
9902,
1262,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2180,
5447,
2858,
9633,
287,
262,
9290,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
597,
2139,
2858,
9633,
13,
1002,
257,
7885,
2314,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
307,
12939,
11,
262,
4941,
287,
262,
5128,
4731,
481,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
21588,
13,
383,
29568,
53,
1503,
62,
20608,
8,
15582,
460,
307,
13537,
351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
4274,
32382,
11,
37941,
25,
32382,
7,
53,
1503,
62,
20608,
737,
16319,
5813,
10288,
481,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1239,
307,
9902,
11,
7692,
286,
1771,
262,
7885,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7160,
393,
407,
13,
2896,
13185,
284,
366,
1911,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
62,
6738,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
8090,
329,
262,
2858,
7885,
338,
1988,
13,
26003,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
973,
611,
1988,
318,
407,
6565,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4566,
62,
8899,
62,
2539,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
9683,
82,
257,
1994,
286,
257,
17056,
13912,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
383,
1994,
284,
2922,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11902,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
18291,
1958,
1771,
262,
17056,
13912,
393,
340,
338,
1994,
1276,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
307,
5447,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2214,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
17563,
82,
257,
2214,
286,
262,
24573,
25,
6971,
20150,
13,
3672,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20150,
13,
14933,
10223,
11,
20150,
13,
23912,
1424,
11,
20150,
13,
34574,
602,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1020,
13,
17440,
5376,
11,
1020,
13,
15271,
30116,
5376,
11,
3722,
13,
4774,
4061,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3722,
13,
33320,
4061,
2637,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
40391,
62,
9641,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
10628,
286,
262,
32815,
262,
7663,
15235,
318,
3194,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
287,
2846,
286,
11,
26235,
284,
366,
85,
16,
1911,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2214,
62,
6978,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
10644,
286,
262,
2214,
284,
2922,
287,
262,
7368,
7824,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2196,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8271,
62,
3245,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
17563,
82,
257,
8271,
286,
262,
9290,
25,
691,
4133,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7095,
290,
7007,
357,
49196,
13,
36166,
11,
7095,
13,
31673,
11,
7007,
13,
36166,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
7007,
13,
31673,
8,
389,
3058,
4855,
2637,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9290,
62,
3672,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
29869,
1438,
25,
2672,
329,
15343,
11,
11902,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
17365,
410,
945,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2659,
271,
273,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
18291,
6945,
262,
5072,
5794,
286,
262,
7362,
4133,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26235,
284,
366,
16,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8271,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
705,
37374,
25,
8271,
284,
2922,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3200,
62,
2539,
62,
5420,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
9683,
82,
257,
1994,
286,
257,
3200,
287,
262,
24573,
338,
25745,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
383,
1994,
286,
262,
3200,
284,
2922,
422,
13,
12039,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
4938,
3200,
1994,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11902,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
18291,
1958,
1771,
262,
3943,
393,
340,
338,
1994,
1276,
307,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5447,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2700,
62,
31216,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
2700,
42940,
8477,
611,
262,
27098,
815,
2834,
262,
4263,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
422,
20478,
3161,
284,
2615,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
29497,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
29497,
6056,
3386,
262,
8090,
1382,
284,
466,
29497,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12188,
611,
2081,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
20512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2834,
62,
21078,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
2834,
23725,
318,
262,
1438,
286,
257,
3943,
326,
561,
307,
973,
329,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4634,
510,
262,
18239,
329,
10427,
262,
25716,
4263,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
422,
262,
2839,
25716,
4214,
1678,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
3716,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4909,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6764,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
6530,
286,
262,
6773,
429,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2099,
25,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
19124,
62,
50179,
25,
198,
220,
220,
220,
220,
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] | 1.763511 | 54,641 |
import json
import logging
import os
import inspect
import urllib
import urllib.request
from urllib.error import HTTPError
# logger
logger = logging.getLogger()
logger_level = logging.getLevelName(os.environ['LOGGER_LEVEL'])
logger.setLevel(logger_level)
# validate access
# create response
# download json file
# entry point -> return region info
# entry point -> return region info
# End; | [
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] | 3.254098 | 122 |
import math
import torch
import torch.nn as nn
from models.neural import MultiHeadedAttention, PositionwiseFeedForward
from models.rnn import LayerNormLSTM
| [
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] | 3.4375 | 48 |
import requests
from allauth.socialaccount.providers.oauth2.views import (
OAuth2Adapter,
OAuth2CallbackView,
OAuth2LoginView,
)
from .provider import DropboxOAuth2Provider
oauth_login = OAuth2LoginView.adapter_view(DropboxOAuth2Adapter)
oauth_callback = OAuth2CallbackView.adapter_view(DropboxOAuth2Adapter)
| [
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] | 2.834783 | 115 |
# SPDX-License-Identifier: MIT
# pylint: disable=redefined-builtin,invalid-name
"""See https://www.sphinx-doc.org/en/master/usage/configuration.html"""
from typing import Sequence
import os
import sys
# region Path setup
sys.path.insert(0, os.path.abspath('..'))
# endregion
# region Project information
project = 'Upkeep'
copyright = '2020, Andrew Udvare'
author = 'Andrew Udvare'
# The short X.Y version
version = '1.2.7'
# The full version, including alpha/beta/rc tags
release = f'v{version}'
# endregion
# region General configuration
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon']
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns: Sequence[str] = []
master_doc = 'index'
# endregion
# region Options for HTML output
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = 'alabaster'
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# endregion
# region Extension configuration
# endregion
| [
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] | 3.409382 | 469 |
import os
import platform
import shutil
import sys
from collections import Counter
from collections import defaultdict, OrderedDict
from os.path import join
from typing import List, Dict, Set
from CppHeaderParser import CppHeaderParser
from CppHeaderParser.CppHeaderParser import CppMethod
import generators.dependency_tree
from generators.config import common_includes, PCL_BASE, PATH_LOADER, PATH_MODULES, MODULES_TO_BUILD, \
HEADERS_TO_SKIP, ATTRIBUTES_TO_SKIP, CLASSES_TO_IGNORE, METHODS_TO_SKIP, SUBMODULES_TO_SKIP, EXPLICIT_INCLUDES, \
SPECIALIZED_TEMPLATED_TYPES_TO_SKIP
from generators.definitions.function import generate_function_definitions, get_methods_defined_outside
from generators.definitions.method import split_methods_by_type
from generators.definitions.submodule_loader import generate_loader
from generators.definitions.templated_class import ClassDefinition
from generators.instantiations import Instantiations
from generators.point_types_utils import unpack_yaml_point_types
from generators.utils import make_header_include_name, sort_headers_by_dependencies, \
generate_main_loader, make_namespace_class, read_header_file
def flag_instantiatable_class(dependency_tree, main_classes):
"""determine if the class can be instantiated"""
main_classes_by_name_namespace = {make_namespace_class(c["namespace"], c["name"]): c
for classes in main_classes.values() for c in classes}
for module, header_name in main_classes:
for class_ in main_classes[(module, header_name)]:
can_be_instantiated = True
if class_["abstract"]:
can_be_instantiated = False
else:
# check if any pure virtual method is not implemented
all_implemented_inherited_methods = get_all_class_methods_not_pure_virtual(class_)
namespace_class = make_namespace_class(class_["namespace"], class_["name"])
for base_name_nsp in dependency_tree.breadth_first_iterator(namespace_class):
base_class = main_classes_by_name_namespace.get(base_name_nsp)
if base_class:
base_class_methods = get_all_class_methods_not_pure_virtual(base_class)
all_implemented_inherited_methods.update(base_class_methods)
for base_name_nsp in dependency_tree.breadth_first_iterator(namespace_class):
base_class = main_classes_by_name_namespace.get(base_name_nsp)
if base_class and base_class["abstract"]:
base_pure_virtual_methods = get_pure_virtual_methods(base_class)
if base_pure_virtual_methods - all_implemented_inherited_methods:
can_be_instantiated = False
class_["can_be_instantiated"] = can_be_instantiated
def generate(headers_to_generate, skip_macros, not_every_point_type=False) -> OrderedDict:
"""
:return: OrderedDict
"""
main_classes, module_functions, module_variables, module_enums = {}, {}, {}, {}
for module, header_name, path in headers_to_generate[:]:
header_full_path = join(PCL_BASE, path) if path else join(PCL_BASE, module, header_name)
header = read_header(header_full_path, skip_macros)
main_classes[(module, header_name)] = get_main_classes(header, module, header_name)
module_functions[(module, header_name)] = get_functions(header, module)
module_variables[(module, header_name)] = get_variables(header)
module_enums[(module, header_name)] = get_enums(header)
classes = [c for module, header, path in headers_to_generate
for c in main_classes[(module, header)]]
dependency_tree = generators.dependency_tree.DependencyTree(classes)
loaded_point_types = load_yaml_point_types(not_every_point_type)
classes_point_types: OrderedDict = dependency_tree.get_point_types_with_dependencies(loaded_point_types)
classes_sorted_base_first = list(dependency_tree.leaf_iterator())
# sort classes inside modules based on inheritance
for module, header in main_classes:
main_classes[(module, header)] = list(sorted(main_classes[(module, header)], key=index_for_class))
headers_to_generate = sort_headers_by_dependencies(headers_to_generate, skip_macros=skip_macros)
methods_need_overloading = check_if_needs_overloading(main_classes)
flag_instantiatable_class(dependency_tree, main_classes)
generated_headers = OrderedDict()
for module, header, path in headers_to_generate:
generated_headers[(module, header)] = generate_header(module, header, path, keep_if_no_instantiation=False)
return generated_headers
if __name__ == '__main__':
main()
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11,
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62,
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62,
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62,
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8,
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220,
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62,
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6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
1388,
3419,
198
] | 2.551797 | 1,892 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# All params from IdM is stored in environment and you can get them by os.environ["paramName"]
import sys, os
# this is needed for importing file winrm_wrapper from parent dir
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
import winrm_wrapper
import codecs
uid = os.environ["__UID__"]
winrm_wrapper.writeLog("Delete start for " + uid)
# Load PS script from file and replace params
winrm_wrapper.writeLog("loading script")
f = codecs.open(os.environ["script"], encoding='utf-8', mode='r')
command = f.read()
command = command.replace("$uid", uid)
# Call wrapper
winrm_wrapper.executeScript(os.environ["endpoint"], os.environ["authentication"], os.environ["user"],
os.environ["password"], os.environ["caTrustPath"], os.environ["ignoreCaValidation"], command, uid)
winrm_wrapper.writeLog("Delete end for " + uid)
print("__UID__=" + uid)
sys.exit()
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] | 2.761628 | 344 |
# Copyright 2019-2021 ETH Zurich and the DaCe authors. All rights reserved.
import unittest
from aenum import Enum, auto
from dace import registry
if __name__ == '__main__':
unittest.main()
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] | 3.125 | 64 |
import asyncio
import logging
from json import loads
import pytest
from aiologstash2 import create_tcp_handler
logging.getLogger().setLevel(logging.DEBUG)
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"""TcEx Framework Playbook module"""
# standard library
import base64
import json
import re
from collections import OrderedDict
from collections.abc import Iterable
def _is_tc_entity_array(self, data):
"""Return True if provided data has proper structure for TC Entity Array."""
for d in data:
if not self._is_tc_entity(d):
return False
return True
def _parse_output_variables(self):
"""Parse the output variables provided to Playbook Class.
**Example Variable Format**::
['#App:1234:status!String', '#App:1234:status_code!String']
"""
self._output_variables_by_name = {}
self._output_variables_by_type = {}
for ov in self._output_variables:
# parse the variable to get individual parts
parsed_variable = self.parse_variable(ov)
variable_name = parsed_variable.get('name')
variable_type = parsed_variable.get('type')
# store the variables in dict by name (e.g. "status_code")
self._output_variables_by_name[variable_name] = {'variable': ov}
# store the variables in dict by name-type (e.g. "status_code-String")
self._output_variables_by_type[f'{variable_name}-{variable_type}'] = {'variable': ov}
def _read(self, key, embedded=True, b64decode=True, decode=False):
"""Create the value in Redis if applicable."""
if key is None:
self.log.warning('The key is None.')
return None
# get variable type from variable value
variable_type = self.variable_type(key)
try:
value = self.tcex.key_value_store.read(self._context, key.strip())
except RuntimeError as e:
self.log.error(e)
return None
if value is None:
return value
if variable_type == 'Binary':
value = self._load_value(value)
if b64decode:
value = base64.b64decode(value)
if decode:
value = self._decode_binary(value)
elif variable_type == 'KeyValue':
# embedded variable can be unquoted, which breaks JSON.
value = self._wrap_embedded_keyvalue(value)
if embedded:
value = self._read_embedded(value)
value = self._load_value(value)
elif variable_type == 'String':
if embedded:
value = self._read_embedded(value)
# coerce string values
value = self._coerce_string_value(self._load_value(value))
elif variable_type == 'TCEntity':
value = self._load_value(value)
return value
def _read_array(self, key, embedded=True, b64decode=True, decode=False):
"""Create the value in Redis if applicable."""
if key is None: # pragma: no cover
self.log.warning('The null value for key was provided.')
return None
# get variable type from variable value
variable_type = self.variable_type(key)
try:
value = self.tcex.key_value_store.read(self._context, key.strip())
except RuntimeError as e:
self.log.error(e)
return None
if value is None:
return value
if variable_type == 'BinaryArray':
value = json.loads(value, object_pairs_hook=OrderedDict)
values = []
for v in value:
if v is not None and b64decode:
v = base64.b64decode(v)
if decode:
v = self._decode_binary(v)
values.append(v)
value = values
elif variable_type == 'KeyValueArray':
# embedded variable can be unquoted, which breaks JSON.
value = self._wrap_embedded_keyvalue(value)
if embedded:
value = self._read_embedded(value)
try:
value = json.loads(value, object_pairs_hook=OrderedDict)
except ValueError as e: # pragma: no cover
raise RuntimeError(f'Failed loading JSON data ({value}). Error: ({e})')
elif variable_type == 'StringArray':
if embedded:
value = self._read_embedded(value)
# convert int to str
value_coerced = []
for v in self._load_value(value):
# coerce string values
value_coerced.append(self._coerce_string_value(v))
value = value_coerced
elif variable_type in ['TCEntityArray', 'TCEnhancedEntity', 'TCEnhancedEntityArray']:
value = self._load_value(value)
# self.log.trace(f'pb create - context: {self._context}, key: {key}, value: {value}')
return value
def _read_embedded(self, value):
"""Read method for "embedded" variables.
.. Note:: The ``read()`` method will automatically determine if the input is a variable or
needs to be searched for embedded variables.
Embedded variable rules:
* Only user input can have embedded variables.
* Only String and KeyValueArray variables can have embedded variables.
* Variables can only be embedded one level deep.
This method will automatically covert variables embedded in a string with value retrieved
from DB. If there are no keys/variables the raw string will be returned.
Examples::
DB Values
#App:7979:variable_name!String:
"embedded \\"variable\\""
#App:7979:two!String:
"two"
#App:7979:variable_name!StringArray:
["one", "two", "three"]
Examples 1:
Input: "This input has a embedded #App:7979:variable_name!String"
Examples 2:
Input: ["one", #App:7979:two!String, "three"]
Examples 3:
Input: [{
"key": "embedded string",
"value": "This input has a embedded #App:7979:variable_name!String"
}, {
"key": "string array",
"value": #App:7979:variable_name!StringArray
}, {
"key": "string",
"value": #App:7979:variable_name!String
}]
Args:
value (str): The value to parsed and updated from the DB.
Returns:
(str): Results retrieved from DB
"""
if value is None: # pragma: no cover
return value
for variable in (v.group(0) for v in re.finditer(self._variable_parse, str(value))):
v = self.read(variable)
self.log.trace(f'embedded variable: {variable}, value: {v}')
if isinstance(v, (dict, list)):
v = json.dumps(v)
# for KeyValueArray with nested dict/list type replace the
# quoted value to ensure the resulting data is loadable JSON
value = re.sub(f'"{variable}"', v, value)
if v is not None:
# only replace variable if a non-null value is returned from kv store
# APP-1030 need to revisit this to handle variable references in kv/kvarrays that
# are None. Would like to be able to say if value is just the variable reference,
# sub None value, else insert '' in string. That would require a kv-specific
# version of this method that gets the entire list/dict instead of just the string.
value = re.sub(variable, v, value)
return value
def _wrap_embedded_keyvalue(self, data):
"""Wrap keyvalue embedded variable in double quotes.
Args:
data (str): The data with embedded variables.
Returns:
(str): Results retrieved from DB
"""
# TODO: need to verify if core still sends improper JSON for KeyValueArrays
if data is not None: # pragma: no cover
variables = []
for v in re.finditer(self._vars_keyvalue_embedded, data):
variables.append(v.group(0))
for var in set(variables): # recursion over set to handle duplicates
# pull (#App:1441:embedded_string!String) from (": #App:1441:embedded_string!String)
variable_string = re.search(self._variable_parse, var).group(0)
# reformat to replace the correct instance only, handling the case where a variable
# is embedded multiple times in the same key value array.
data = data.replace(var, f'": "{variable_string}"')
return data
def create_raw(self, key, value):
"""Create method of CRUD operation for raw data.
..important:: Raw data can only be a byte, str or int. Other data structures
(dict, list, etc) must be serialized.
Args:
key (str): The variable to write to the DB.
value (bytes|int|string): The data to write to the DB.
Returns:
(str): Result of DB write.
"""
data = None
if key is not None and value is not None:
try:
data = self.tcex.key_value_store.create(self._context, key.strip(), value)
except RuntimeError as e:
self.log.error(e)
else:
self.log.warning('The key or value field was None.')
return data
def read_raw(self, key):
"""Read method of CRUD operation for raw data.
..important:: Bytes input will be returned a as string as there is
no way to determine data from redis originated as bytes or string.
Args:
key (str): The variable to read from the DB.
Returns:
(str): Results retrieved from DB.
"""
value = None
if key is not None:
value = self.tcex.key_value_store.read(self._context, key.strip())
else:
self.log.warning('The key field was None.')
return value
def parse_variable(self, variable): # pragma: no cover
"""Set placeholder for child method."""
raise NotImplementedError('Implemented in child class')
def read(self, key, array=False, embedded=True): # pragma: no cover
"""Set placeholder for child method."""
raise NotImplementedError('Implemented in child class')
def variable_type(self, variable): # pragma: no cover
"""Set placeholder for child method."""
raise NotImplementedError('Implemented in child class')
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220,
220,
220,
220,
1441,
6045,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
7885,
2099,
422,
7885,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
7885,
62,
4906,
796,
2116,
13,
45286,
62,
4906,
7,
2539,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13,
83,
344,
87,
13,
2539,
62,
8367,
62,
8095,
13,
961,
7,
944,
13557,
22866,
11,
1994,
13,
36311,
28955,
198,
220,
220,
220,
220,
220,
220,
220,
2845,
43160,
12331,
355,
304,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
6404,
13,
18224,
7,
68,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
6045,
628,
220,
220,
220,
220,
220,
220,
220,
611,
1988,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
1988,
628,
220,
220,
220,
220,
220,
220,
220,
611,
7885,
62,
4906,
6624,
705,
33,
3219,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13557,
2220,
62,
8367,
7,
8367,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
275,
2414,
12501,
1098,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2779,
2414,
13,
65,
2414,
12501,
1098,
7,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
36899,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13557,
12501,
1098,
62,
39491,
7,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
7885,
62,
4906,
6624,
705,
9218,
11395,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
14553,
7885,
460,
307,
555,
421,
5191,
11,
543,
9457,
19449,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13557,
37150,
62,
20521,
9395,
62,
2539,
8367,
7,
8367,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
14553,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13557,
961,
62,
20521,
9395,
7,
8367,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13557,
2220,
62,
8367,
7,
8367,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
7885,
62,
4906,
6624,
705,
10100,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
14553,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13557,
961,
62,
20521,
9395,
7,
8367,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
31255,
344,
4731,
3815,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13557,
1073,
263,
344,
62,
8841,
62,
8367,
7,
944,
13557,
2220,
62,
8367,
7,
8367,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
7885,
62,
4906,
6624,
705,
4825,
32398,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13557,
2220,
62,
8367,
7,
8367,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1441,
1988,
628,
220,
220,
220,
825,
4808,
961,
62,
18747,
7,
944,
11,
1994,
11,
14553,
28,
17821,
11,
275,
2414,
12501,
1098,
28,
17821,
11,
36899,
28,
25101,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
16447,
262,
1988,
287,
2297,
271,
611,
9723,
526,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
318,
6045,
25,
220,
1303,
23864,
2611,
25,
645,
3002,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
6404,
13,
43917,
10786,
464,
9242,
1988,
329,
1994,
373,
2810,
2637,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
6045,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
7885,
2099,
422,
7885,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
7885,
62,
4906,
796,
2116,
13,
45286,
62,
4906,
7,
2539,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13,
83,
344,
87,
13,
2539,
62,
8367,
62,
8095,
13,
961,
7,
944,
13557,
22866,
11,
1994,
13,
36311,
28955,
198,
220,
220,
220,
220,
220,
220,
220,
2845,
43160,
12331,
355,
304,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
6404,
13,
18224,
7,
68,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
6045,
628,
220,
220,
220,
220,
220,
220,
220,
611,
1988,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
1988,
628,
220,
220,
220,
220,
220,
220,
220,
611,
7885,
62,
4906,
6624,
705,
33,
3219,
19182,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
33918,
13,
46030,
7,
8367,
11,
2134,
62,
79,
3468,
62,
25480,
28,
35422,
1068,
35,
713,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3815,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
410,
287,
1988,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
410,
318,
407,
6045,
290,
275,
2414,
12501,
1098,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
796,
2779,
2414,
13,
65,
2414,
12501,
1098,
7,
85,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
36899,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
796,
2116,
13557,
12501,
1098,
62,
39491,
7,
85,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3815,
13,
33295,
7,
85,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
3815,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
7885,
62,
4906,
6624,
705,
9218,
11395,
19182,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
14553,
7885,
460,
307,
555,
421,
5191,
11,
543,
9457,
19449,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13557,
37150,
62,
20521,
9395,
62,
2539,
8367,
7,
8367,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
14553,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13557,
961,
62,
20521,
9395,
7,
8367,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
33918,
13,
46030,
7,
8367,
11,
2134,
62,
79,
3468,
62,
25480,
28,
35422,
1068,
35,
713,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
11052,
12331,
355,
304,
25,
220,
1303,
23864,
2611,
25,
645,
3002,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
43160,
12331,
7,
69,
6,
37,
6255,
11046,
19449,
1366,
37913,
8367,
92,
737,
13047,
25,
37913,
68,
30072,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
7885,
62,
4906,
6624,
705,
10100,
19182,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
14553,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13557,
961,
62,
20521,
9395,
7,
8367,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
10385,
493,
284,
965,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
62,
1073,
263,
771,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
410,
287,
2116,
13557,
2220,
62,
8367,
7,
8367,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
31255,
344,
4731,
3815,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
62,
1073,
263,
771,
13,
33295,
7,
944,
13557,
1073,
263,
344,
62,
8841,
62,
8367,
7,
85,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
1988,
62,
1073,
263,
771,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
7885,
62,
4906,
287,
37250,
4825,
32398,
19182,
3256,
705,
4825,
49026,
32398,
3256,
705,
4825,
49026,
32398,
19182,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
2116,
13557,
2220,
62,
8367,
7,
8367,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
2116,
13,
6404,
13,
40546,
7,
69,
6,
40842,
2251,
532,
4732,
25,
1391,
944,
13557,
22866,
5512,
1994,
25,
1391,
2539,
5512,
1988,
25,
1391,
8367,
92,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
1988,
628,
220,
220,
220,
825,
4808,
961,
62,
20521,
9395,
7,
944,
11,
1988,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
5569,
2446,
329,
366,
20521,
9395,
1,
9633,
13,
628,
220,
220,
220,
220,
220,
220,
220,
11485,
5740,
3712,
383,
7559,
961,
3419,
15506,
2446,
481,
6338,
5004,
611,
262,
5128,
318,
257,
7885,
393,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2476,
284,
307,
16499,
329,
14553,
9633,
13,
628,
220,
220,
220,
220,
220,
220,
220,
13302,
47238,
7885,
3173,
25,
628,
220,
220,
220,
220,
220,
220,
220,
1635,
5514,
2836,
5128,
460,
423,
14553,
9633,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1635,
5514,
10903,
290,
7383,
11395,
19182,
9633,
460,
423,
14553,
9633,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1635,
15965,
2977,
460,
691,
307,
14553,
530,
1241,
2769,
13,
628,
220,
220,
220,
220,
220,
220,
220,
770,
2446,
481,
6338,
22624,
9633,
14553,
287,
257,
4731,
351,
1988,
29517,
198,
220,
220,
220,
220,
220,
220,
220,
422,
20137,
13,
1002,
612,
389,
645,
8251,
14,
25641,
2977,
262,
8246,
4731,
481,
307,
4504,
13,
628,
220,
220,
220,
220,
220,
220,
220,
21066,
3712,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20137,
27068,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4677,
25,
3720,
3720,
25,
45286,
62,
3672,
0,
10100,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
20521,
9395,
3467,
7879,
45286,
6852,
15931,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4677,
25,
3720,
3720,
25,
11545,
0,
10100,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
11545,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4677,
25,
3720,
3720,
25,
45286,
62,
3672,
0,
10100,
19182,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14631,
505,
1600,
366,
11545,
1600,
366,
15542,
8973,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
21066,
352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
23412,
25,
220,
366,
1212,
5128,
468,
257,
14553,
1303,
4677,
25,
3720,
3720,
25,
45286,
62,
3672,
0,
10100,
1,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
21066,
362,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
23412,
25,
14631,
505,
1600,
1303,
4677,
25,
3720,
3720,
25,
11545,
0,
10100,
11,
366,
15542,
8973,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
21066,
513,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
23412,
25,
685,
90,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
2539,
1298,
366,
20521,
9395,
4731,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
8367,
1298,
366,
1212,
5128,
468,
257,
14553,
1303,
4677,
25,
3720,
3720,
25,
45286,
62,
3672,
0,
10100,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8964,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
2539,
1298,
366,
8841,
7177,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
8367,
1298,
1303,
4677,
25,
3720,
3720,
25,
45286,
62,
3672,
0,
10100,
19182,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8964,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
2539,
1298,
366,
8841,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
8367,
1298,
1303,
4677,
25,
3720,
3720,
25,
45286,
62,
3672,
0,
10100,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1782,
60,
628,
220,
220,
220,
220,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
357,
2536,
2599,
383,
1988,
284,
44267,
290,
6153,
422,
262,
20137,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
2536,
2599,
15691,
29517,
422,
20137,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1988,
318,
6045,
25,
220,
1303,
23864,
2611,
25,
645,
3002,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
1988,
628,
220,
220,
220,
220,
220,
220,
220,
329,
7885,
287,
357,
85,
13,
8094,
7,
15,
8,
329,
410,
287,
302,
13,
19796,
2676,
7,
944,
13557,
45286,
62,
29572,
11,
965,
7,
8367,
4008,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
796,
2116,
13,
961,
7,
45286,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
6404,
13,
40546,
7,
69,
6,
20521,
9395,
7885,
25,
1391,
45286,
5512,
1988,
25,
1391,
85,
92,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
318,
39098,
7,
85,
11,
357,
11600,
11,
1351,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
796,
33918,
13,
67,
8142,
7,
85,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
329,
7383,
11395,
19182,
351,
28376,
8633,
14,
4868,
2099,
6330,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
10947,
1988,
284,
4155,
262,
7186,
1366,
318,
3440,
540,
19449,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
302,
13,
7266,
7,
69,
29653,
90,
45286,
36786,
3256,
410,
11,
1988,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
410,
318,
407,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
691,
6330,
7885,
611,
257,
1729,
12,
8423,
1988,
318,
4504,
422,
479,
85,
3650,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
43504,
12,
940,
1270,
761,
284,
32302,
428,
284,
5412,
7885,
10288,
287,
479,
85,
14,
74,
85,
3258,
592,
326,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
389,
6045,
13,
220,
10928,
588,
284,
307,
1498,
284,
910,
611,
1988,
318,
655,
262,
7885,
4941,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
850,
6045,
1988,
11,
2073,
7550,
10148,
287,
4731,
13,
220,
1320,
561,
2421,
257,
479,
85,
12,
11423,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2196,
286,
428,
2446,
326,
3011,
262,
2104,
1351,
14,
11600,
2427,
286,
655,
262,
4731,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
302,
13,
7266,
7,
45286,
11,
410,
11,
1988,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
1988,
628,
220,
220,
220,
825,
4808,
37150,
62,
20521,
9395,
62,
2539,
8367,
7,
944,
11,
1366,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
54,
2416,
1994,
8367,
14553,
7885,
287,
4274,
13386,
13,
628,
220,
220,
220,
220,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
357,
2536,
2599,
383,
1366,
351,
14553,
9633,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
2536,
2599,
15691,
29517,
422,
20137,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
16926,
46,
25,
761,
284,
11767,
611,
4755,
991,
12800,
18992,
19449,
329,
7383,
11395,
3163,
20477,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1366,
318,
407,
6045,
25,
220,
1303,
23864,
2611,
25,
645,
3002,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9633,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
410,
287,
302,
13,
19796,
2676,
7,
944,
13557,
85,
945,
62,
2539,
8367,
62,
20521,
9395,
11,
1366,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9633,
13,
33295,
7,
85,
13,
8094,
7,
15,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1401,
287,
900,
7,
25641,
2977,
2599,
220,
1303,
664,
24197,
625,
900,
284,
5412,
14184,
16856,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2834,
17426,
4677,
25,
1415,
3901,
25,
20521,
9395,
62,
8841,
0,
10100,
8,
422,
357,
1298,
1303,
4677,
25,
1415,
3901,
25,
20521,
9395,
62,
8841,
0,
10100,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7885,
62,
8841,
796,
302,
13,
12947,
7,
944,
13557,
45286,
62,
29572,
11,
1401,
737,
8094,
7,
15,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4975,
265,
284,
6330,
262,
3376,
4554,
691,
11,
9041,
262,
1339,
810,
257,
7885,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
318,
14553,
3294,
1661,
287,
262,
976,
1994,
1988,
7177,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
796,
1366,
13,
33491,
7,
7785,
11,
277,
6,
1298,
45144,
45286,
62,
8841,
36786,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
1366,
628,
220,
220,
220,
825,
2251,
62,
1831,
7,
944,
11,
1994,
11,
1988,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
16447,
2446,
286,
8740,
8322,
4905,
329,
8246,
1366,
13,
628,
220,
220,
220,
220,
220,
220,
220,
11485,
18049,
3712,
16089,
1366,
460,
691,
307,
257,
18022,
11,
965,
393,
493,
13,
3819,
1366,
8573,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
11600,
11,
1351,
11,
3503,
8,
1276,
307,
11389,
1143,
13,
628,
220,
220,
220,
220,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
357,
2536,
2599,
383,
7885,
284,
3551,
284,
262,
20137,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
357,
33661,
91,
600,
91,
8841,
2599,
383,
1366,
284,
3551,
284,
262,
20137,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
2536,
2599,
25414,
286,
20137,
3551,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
796,
6045,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
318,
407,
6045,
290,
1988,
318,
407,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1366,
796,
2116,
13,
83,
344,
87,
13,
2539,
62,
8367,
62,
8095,
13,
17953,
7,
944,
13557,
22866,
11,
1994,
13,
36311,
22784,
1988,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
43160,
12331,
355,
304,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
6404,
13,
18224,
7,
68,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
6404,
13,
43917,
10786,
464,
1994,
393,
1988,
2214,
373,
6045,
2637,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
1366,
628,
220,
220,
220,
825,
1100,
62,
1831,
7,
944,
11,
1994,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
5569,
2446,
286,
8740,
8322,
4905,
329,
8246,
1366,
13,
628,
220,
220,
220,
220,
220,
220,
220,
11485,
18049,
3712,
2750,
4879,
5128,
481,
307,
4504,
257,
355,
4731,
355,
612,
318,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
645,
835,
284,
5004,
1366,
422,
2266,
271,
20973,
355,
9881,
393,
4731,
13,
628,
220,
220,
220,
220,
220,
220,
220,
943,
14542,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
357,
2536,
2599,
383,
7885,
284,
1100,
422,
262,
20137,
13,
628,
220,
220,
220,
220,
220,
220,
220,
16409,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
2536,
2599,
15691,
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] | 2.233194 | 4,790 |
# coding=utf-8
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
import modeling
import optimization
import tokenization
import tensorflow as tf
import horovod.tensorflow as hvd
import time
from utils.utils import LogEvalRunHook, LogTrainRunHook, setup_xla_flags
from utils.gpu_affinity import set_affinity
import utils.dllogger_class
from dllogger import Verbosity
from utils.create_glue_data import *
import numpy as np
import tf_metrics
flags = tf.flags
FLAGS = flags.FLAGS
## Required parameters
flags.DEFINE_string(
"data_dir", None,
"The input data dir. Should contain the .tsv files (or other data files) "
"for the task.")
flags.DEFINE_string(
"bert_config_file", None,
"The config json file corresponding to the pre-trained BERT model. "
"This specifies the model architecture.")
flags.DEFINE_string("task_name", None, "The name of the task to train.")
flags.DEFINE_string("vocab_file", None,
"The vocabulary file that the BERT model was trained on.")
flags.DEFINE_string(
"output_dir", None,
"The output directory where the model checkpoints will be written.")
## Other parameters
flags.DEFINE_string(
"dllog_path", "/results/bert_dllog.json",
"filename where dllogger writes to")
flags.DEFINE_string(
"optimizer_type", "lamb",
"Optimizer type : adam or lamb")
flags.DEFINE_string(
"init_checkpoint", None,
"Initial checkpoint (usually from a pre-trained BERT model).")
flags.DEFINE_bool(
"do_lower_case", True,
"Whether to lower case the input text. Should be True for uncased "
"models and False for cased models.")
flags.DEFINE_integer(
"max_seq_length", 128,
"The maximum total input sequence length after WordPiece tokenization. "
"Sequences longer than this will be truncated, and sequences shorter "
"than this will be padded.")
flags.DEFINE_bool("do_train", False, "Whether to run training.")
flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
flags.DEFINE_bool(
"do_predict", False,
"Whether to run the model in inference mode on the test set.")
flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")
flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
flags.DEFINE_bool("use_trt", False, "Whether to use TF-TRT")
flags.DEFINE_float("num_train_epochs", 3.0,
"Total number of training epochs to perform.")
flags.DEFINE_float(
"warmup_proportion", 0.1,
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10% of training.")
flags.DEFINE_integer("save_checkpoints_steps", 1000,
"How often to save the model checkpoint.")
flags.DEFINE_integer("display_loss_steps", 10,
"How often to print loss from estimator")
flags.DEFINE_integer("iterations_per_loop", 1000,
"How many steps to make in each estimator call.")
flags.DEFINE_integer("num_accumulation_steps", 1,
"Number of accumulation steps before gradient update"
"Global batch size = num_accumulation_steps * train_batch_size")
flags.DEFINE_bool("amp", True, "Whether to enable AMP ops. When false, uses TF32 on A100 and FP32 on V100 GPUS.")
flags.DEFINE_bool("use_xla", True, "Whether to enable XLA JIT compilation.")
flags.DEFINE_bool("horovod", False, "Whether to use Horovod for multi-gpu runs")
flags.DEFINE_bool(
"verbose_logging", False,
"If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
def file_based_input_fn_builder(input_file, batch_size, seq_length, is_training,
drop_remainder, hvd=None):
"""Creates an `input_fn` closure to be passed to Estimator."""
name_to_features = {
"input_ids": tf.io.FixedLenFeature([seq_length], tf.int64),
"input_mask": tf.io.FixedLenFeature([seq_length], tf.int64),
"segment_ids": tf.io.FixedLenFeature([seq_length], tf.int64),
"label_ids": tf.io.FixedLenFeature([], tf.int64),
}
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
example[name] = t
return example
def input_fn():
"""The actual input function."""
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
d = tf.data.TFRecordDataset(input_file)
if is_training:
if hvd is not None: d = d.shard(hvd.size(), hvd.rank())
d = d.repeat()
d = d.shuffle(buffer_size=100)
d = d.apply(
tf.contrib.data.map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=batch_size,
drop_remainder=drop_remainder))
return d
return input_fn
def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
labels, num_labels, use_one_hot_embeddings):
"""Creates a classification model."""
model = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
compute_type=tf.float32)
# In the demo, we are doing a simple classification task on the entire
# segment.
#
# If you want to use the token-level output, use model.get_sequence_output()
# instead.
output_layer = model.get_pooled_output()
hidden_size = output_layer.shape[-1].value
output_weights = tf.get_variable(
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"output_bias", [num_labels], initializer=tf.zeros_initializer())
with tf.variable_scope("loss"):
if is_training:
# I.e., 0.1 dropout
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias, name='cls_logits')
probabilities = tf.nn.softmax(logits, axis=-1, name='cls_probabilities')
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1, name='cls_per_example_loss')
loss = tf.reduce_mean(per_example_loss, name='cls_loss')
return (loss, per_example_loss, logits, probabilities)
def model_fn_builder(task_name, bert_config, num_labels, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps,
use_one_hot_embeddings, hvd=None):
"""Returns `model_fn` closure for Estimator."""
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for Estimator."""
tf.compat.v1.logging.info("*** Features ***")
tf.compat.v1.logging.info("*** Features ***")
for name in sorted(features.keys()):
tf.compat.v1.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
label_ids = features["label_ids"]
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
if not is_training and FLAGS.use_trt:
trt_graph = get_frozen_tftrt_model(bert_config, input_ids.shape, num_labels, use_one_hot_embeddings, init_checkpoint)
(total_loss, per_example_loss, logits, probabilities) = tf.import_graph_def(trt_graph,
input_map={'input_ids':input_ids, 'input_mask':input_mask, 'segment_ids':segment_ids, 'label_ids':label_ids},
return_elements=['loss/cls_loss:0', 'loss/cls_per_example_loss:0', 'loss/cls_logits:0', 'loss/cls_probabilities:0'],
name='')
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {"probabilities": probabilities}
output_spec = tf.estimator.EstimatorSpec(
mode=mode, predictions=predictions)
elif mode == tf.estimator.ModeKeys.EVAL:
eval_metric_ops = metric_fn(per_example_loss, label_ids, logits)
output_spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
eval_metric_ops=eval_metric_ops)
return output_spec
(total_loss, per_example_loss, logits, probabilities) = create_model(
bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
num_labels, use_one_hot_embeddings)
tvars = tf.trainable_variables()
initialized_variable_names = {}
if init_checkpoint and (hvd is None or hvd.rank() == 0):
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
if FLAGS.verbose_logging:
tf.compat.v1.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.compat.v1.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer(
total_loss, learning_rate, num_train_steps, num_warmup_steps,
hvd, False, FLAGS.amp, FLAGS.num_accumulation_steps, FLAGS.optimizer_type)
output_spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op)
elif mode == tf.estimator.ModeKeys.EVAL:
dummy_op = tf.no_op()
# Need to call mixed precision graph rewrite if fp16 to enable graph rewrite
if FLAGS.amp:
loss_scaler = tf.train.experimental.FixedLossScale(1)
dummy_op = tf.train.experimental.enable_mixed_precision_graph_rewrite(
optimization.LAMBOptimizer(learning_rate=0.0), loss_scaler)
eval_metric_ops = metric_fn(per_example_loss, label_ids, logits)
output_spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
eval_metric_ops=eval_metric_ops)
else:
dummy_op = tf.no_op()
# Need to call mixed precision graph rewrite if fp16 to enable graph rewrite
if FLAGS.amp:
dummy_op = tf.train.experimental.enable_mixed_precision_graph_rewrite(
optimization.LAMBOptimizer(learning_rate=0.0))
output_spec = tf.estimator.EstimatorSpec(
mode=mode, predictions=probabilities)
return output_spec
return model_fn
# This function is not used by this file but is still used by the Colab and
# people who depend on it.
def input_fn_builder(features, batch_size, seq_length, is_training, drop_remainder, hvd=None):
"""Creates an `input_fn` closure to be passed to Estimator."""
all_input_ids = []
all_input_mask = []
all_segment_ids = []
all_label_ids = []
for feature in features:
all_input_ids.append(feature.input_ids)
all_input_mask.append(feature.input_mask)
all_segment_ids.append(feature.segment_ids)
all_label_ids.append(feature.label_id)
def input_fn():
"""The actual input function."""
num_examples = len(features)
# This is for demo purposes and does NOT scale to large data sets. We do
# not use Dataset.from_generator() because that uses tf.py_func which is
# not TPU compatible. The right way to load data is with TFRecordReader.
d = tf.data.Dataset.from_tensor_slices({
"input_ids":
tf.constant(
all_input_ids, shape=[num_examples, seq_length],
dtype=tf.int32),
"input_mask":
tf.constant(
all_input_mask,
shape=[num_examples, seq_length],
dtype=tf.int32),
"segment_ids":
tf.constant(
all_segment_ids,
shape=[num_examples, seq_length],
dtype=tf.int32),
"label_ids":
tf.constant(all_label_ids, shape=[num_examples], dtype=tf.int32),
})
if is_training:
if hvd is not None: d = d.shard(hvd.size(), hvd.rank())
d = d.repeat()
d = d.shuffle(buffer_size=100)
d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
return d
return input_fn
if __name__ == "__main__":
flags.mark_flag_as_required("data_dir")
flags.mark_flag_as_required("task_name")
flags.mark_flag_as_required("vocab_file")
flags.mark_flag_as_required("bert_config_file")
flags.mark_flag_as_required("output_dir")
tf.compat.v1.app.run()
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] | 2.487469 | 5,666 |
import datetime
import json
import os
import xlrd
import logging
from io import BytesIO
from xlsxwriter.workbook import Workbook
from xhtml2pdf import pisa
from itertools import chain
from django.contrib.auth.models import User
from django.http import HttpResponse, HttpResponseRedirect, JsonResponse
from django.shortcuts import get_object_or_404, render
from django.template.loader import get_template
from django.views.decorators.csrf import csrf_exempt
from django.template.loader import render_to_string
from django.contrib.auth.decorators import login_required
from applications.academic_procedures.models import MinimumCredits, Register, InitialRegistration, course_registration, AssistantshipClaim,Assistantship_status
from applications.globals.models import (Designation, ExtraInfo,
HoldsDesignation, DepartmentInfo)
from .forms import AcademicTimetableForm, ExamTimetableForm, MinuteForm
from .models import (Calendar, Course, Exam_timetable, Grades, Curriculum_Instructor,Constants,
Meeting, Student, Student_attendance, Timetable,Curriculum)
from applications.programme_curriculum.models import (CourseSlot, Course as Courses, Batch, Semester, Programme, Discipline)
from applications.academic_procedures.views import acad_proced_global_context
from applications.programme_curriculum.models import Batch
def get_context(request):
"""
This function gets basic gata from database to send to template
@param:
request - contains metadata about the requested page
@variables:
acadTtForm - the form to add academic calender
examTtForm - the form required to add exam timetable
exam_t - all the exam timetable objects
timetable - all the academic timetable objects
calendar - all the academic calender objects
context - the datas to be displayed in the webpage
this_sem_course - tha data of thsi semester courses
next_sem_courses - the data of next semester courses
courses - all the courses in curriculum
course_type - list the type of courses
"""
if user_check(request):
return HttpResponseRedirect('/academic-procedures/')
course_list = sem_for_generate_sheet()
if(course_list[0]==1):
course_list_2 = [2, 4, 6, 8]
else:
course_list_2 = [1, 3, 5, 7]
# examTtForm = ExamTimetableForm()
# acadTtForm = AcademicTimetableForm()
# calendar = Calendar.objects.all()
# this_sem_courses = Curriculum.objects.all().filter(sem__in=course_list).filter(floated=True)
# next_sem_courses = Curriculum.objects.all().filter(sem__in=course_list).filter(floated=True)
# courses = Course.objects.all()
# course_type = Constants.COURSE_TYPE
# timetable = Timetable.objects.all()
# exam_t = Exam_timetable.objects.all()
procedures_context = acad_proced_global_context()
try:
examTtForm = ExamTimetableForm()
acadTtForm = AcademicTimetableForm()
calendar = Calendar.objects.all()
this_sem_courses = Curriculum.objects.all().select_related().filter(sem__in=course_list).filter(floated=True)
next_sem_courses = Curriculum.objects.all().select_related().filter(sem__in=course_list_2).filter(floated=True)
courses = Course.objects.all()
courses_list = Courses.objects.all()
course_type = Constants.COURSE_TYPE
timetable = Timetable.objects.all()
exam_t = Exam_timetable.objects.all()
pgstudent = Student.objects.filter(programme = "M.Tech") | Student.objects.filter(programme = "PhD")
assistant_list = AssistantshipClaim.objects.filter(ta_supervisor_remark = True).filter(thesis_supervisor_remark = True).filter(hod_approval =True).filter(acad_approval = False)
assistant_approve_list = AssistantshipClaim.objects.filter(ta_supervisor_remark = True).filter(thesis_supervisor_remark = True).filter(hod_approval =True).filter(hod_approval = True)
assistant_list_length = len(assistant_list.filter(acad_approval = False))
assis_stat = Assistantship_status.objects.all()
for obj in assis_stat:
assistant_flag = obj.student_status
hod_flag = obj.hod_status
account_flag = obj.account_status
except Exception as e:
examTtForm = ""
acadTtForm = ""
calendar = ""
this_sem_courses = ""
next_sem_courses = ""
courses = ""
course_type = ""
timetable = ""
exam_t = ""
pass
context = {
'acadTtForm': acadTtForm,
'examTtForm': examTtForm,
'courses': courses,
'courses_list': courses_list,
'course_type': course_type,
'exam': exam_t,
'timetable': timetable,
'academic_calendar': calendar,
'next_sem_course': next_sem_courses,
'this_sem_course': this_sem_courses,
'curriculum': curriculum,
'pgstudent' : pgstudent,
'assistant_list' : assistant_list,
'assistant_approve_list' : assistant_approve_list,
'assistant_list_length' : assistant_list_length,
'tab_id': ['1','1'],
'context': procedures_context['context'],
'lists': procedures_context['lists'],
'date': procedures_context['date'],
'query_option1': procedures_context['query_option1'],
'query_option2': procedures_context['query_option2'],
'course_verification_date' : procedures_context['course_verification_date'],
'submitted_course_list' : procedures_context['submitted_course_list'],
'result_year' : procedures_context['result_year'],
'batch_grade_data' : procedures_context['batch_grade_data'],
'batch_branch_data' : procedures_context['batch_branch_data'],
'assistant_flag' : assistant_flag,
'hod_flag' : hod_flag,
'account_flag' : account_flag
}
return context
# ####################################
# # curriculum #
# ####################################
#Generate Attendance Sheet
def sem_for_generate_sheet():
"""
This function generates semester grade sheet
@variables:
now - current datetime
month - current month
"""
now = datetime.datetime.now()
month = int(now.month)
if month >= 7 and month <= 12:
return [1, 3, 5, 7]
else:
return [2, 4, 6, 8]
def get_faculty_list():
"""
to get faculty list from database
@param:
request - contains metadata about the requested page.
@variables:
f1,f2,f3 - temporary varibles
faculty - details of faculty of data
faculty_list - list of faculty
"""
try:
f1 = HoldsDesignation.objects.select_related().filter(designation=Designation.objects.get(name = "Assistant Professor"))
f2 = HoldsDesignation.objects.select_related().filter(designation=Designation.objects.get(name = "Professor"))
f3 = HoldsDesignation.objects.select_related().filter(designation=Designation.objects.get(name = "Associate Professor"))
except Exception as e:
f1=f2=f3=""
pass
faculty = list(chain(f1,f2,f3))
faculty_list = []
for i in faculty:
faculty_list.append(i)
return faculty_list
# # ---------------------senator------------------
# @csrf_exempt
def senator(request):
# """
# to add a new student senator
# @param:
# request - contains metadata about the requested page
# @variables:
# current_user - gets the data of current user.
# user_details - gets the details of the required user.
# desig_id - used to check the designation ID.
# extraInfo - extraInfo object of the student with that rollno
# s - designation object of senator
# hDes - holdsDesignation object to store that the particualr student is holding the senator designation
# student - the student object of the new senator
# data - data of the student to be displayed in teh webpage
# """
# current_user = get_object_or_404(User, username=request.user.username)
# user_details = ExtraInfo.objects.all().filter(user=current_user).first()
# desig_id = Designation.objects.all().filter(name='Upper Division Clerk')
temp = HoldsDesignation.objects.all().select_related().filter(designation = desig_id).first()
#print (temp)
# print (current_user)
# acadadmin = temp.working
# k = str(user_details).split()
# print(k)
# final_user = k[2]
# if (str(acadadmin) != str(final_user)):
# return HttpResponseRedirect('/academic-procedures/')
# if request.method == 'POST':
# print(request.POST, ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>")
# rollno = request.POST.getlist('Roll Number')[0]
# # print(request.POST.get('rollno'))
# extraInfo = ExtraInfo.objects.get(id=rollno)
# s = Designation.objects.get(name='Senator')
# hDes = HoldsDesignation()
# hDes.user = extraInfo.user
# hDes.working = extraInfo.user
# hDes.designation = s
# hDes.save()
# student = Student.objects.get(id=extraInfo)
# data = {
# 'name': extraInfo.user.username,
# 'rollno': extraInfo.id,
# 'programme': student.programme,
# 'branch': extraInfo.department.name
# }
# return HttpResponseRedirect('/aims/')
# # return JsonResponse(data)
# else:
# return HttpResponseRedirect('/aims/')
# @csrf_exempt
# if request.POST:
# s = get_object_or_404(Designation, name="Senator")
# student = get_object_or_404(ExtraInfo, id=request.POST.getlist("senate_id")[0])
# hDes = get_object_or_404( HoldsDesignation, user = student.user)
# hDes.delete()
# return HttpResponseRedirect('/aims/')
# else:
# return HttpResponseRedirect('/aims/')# ####################################################
# # ##########covenors and coconvenors##################
# @csrf_exempt
# p = Designation.objects.get(name='Co Convenor')
# if request.method == 'POST':
# rollno = request.POST.get('rollno_convenor')
# extraInfo = ExtraInfo.objects.get(id=rollno)
# s = Designation.objects.get(name='Convenor')
# p = Designation.objects.get(name='Co Convenor')
# result = request.POST.get('designation')
# hDes = HoldsDesignation()
# hDes.user = extraInfo.user
# hDes.working = extraInfo.user
# if result == "Convenor":
# hDes.designation = s
# else:
# hDes.designation = p
# hDes.save()
# data = {
# 'name': extraInfo.user.username,
# 'rollno_convenor': extraInfo.id,
# 'designation': hDes.designation.name,
# }
# return JsonResponse(data)
# else:
# data = {}
# return JsonResponse(data)
# @csrf_exempt
# student = get_object_or_404(ExtraInfo, id=pk)
# hDes = HoldsDesignation.objects.filter(user = student.user)
# designation = []
# for des in hDes:
# if des.designation == s or des.designation == c:
# designation = des.designation.name
# des.delete()
# data = {
# 'id': pk,
# 'designation': designation,
# }
# return JsonResponse(data)# ######################################################
# # ##########Senate meeting Minute##################
# @csrf_exempt
# print (temp)
# print (current_user)
# acadadmin = temp.working
# k = str(user_details).split()
# print(k)
# final_user = k[2]
# if (str(acadadmin) != str(final_user)):
# return HttpResponseRedirect('/academic-procedures/')
# if request.method == 'POST' and request.FILES:
# form = MinuteForm(request.POST, request.FILES)
# if form.is_valid():
# form.save()
# return HttpResponse('sucess')
# else:
# return HttpResponse('not uploaded')
# return render(request, "ais/ais.html", {})
# # ######################################################
# # ##########Student basic profile##################
# @csrf_exempt
# roll = ExtraInfo.objects.get(id=request.POST.get('rollno'))
# programme = request.POST.get('programme')
# batch = request.POST.get('batch')
# ph = request.POST.get('phoneno')
# if not Student.objects.filter(id=roll).exists():
# db = Student()
# st = ExtraInfo.objects.get(id=roll.id)
# db.name = name.upper()
# db.id = roll
# db.batch = batch
# db.programme = programme
# st.phone_no = ph
# db.save()
# st.save()
# data = {
# 'name': name,
# 'rollno': roll.id,
# 'programme': programme,
# 'phoneno': ph,
# 'batch': batch
# }
# print(data)
# return JsonResponse(data)
# else:
# data = {}
# return JsonResponse(data)
# else:
# data = {}
# return JsonResponse(data)
# @csrf_exempt
# user = get_object_or_404(User, username = e.user.username)
# s = get_object_or_404(Student, id=e)
# data = {
# 'rollno': pk,
# }
# s.delete()
# e.delete()
# u.delete()
# return JsonResponse(data)# #########################################################
# '''
# # view to add attendance data to database
# def curriculum(request):
# '''
# user_details = ExtraInfo.objects.all().filter(user=current_user).first()
# desig_id = Designation.objects.all().filter(name='Upper Division Clerk')
# temp = HoldsDesignation.objects.all().filter(designation = desig_id).first()
# print (temp)
# print (current_user)
# acadadmin = temp.working
# k = str(user_details).split()
# print(k)
# final_user = k[2]
# if (str(acadadmin) != str(final_user)):
# return HttpResponseRedirect('/academic-procedures/')
# if request.method == "POST":
# st = request.POST['delete']
# arr = st.split("-")
# stu = arr[0]
# if Student.objects.get(id=stu):
# s = Student.objects.get(id=stu)
# s.father_name = ""
# s.mother_name = ""
# s.hall_no = 1
# s.room_no = ""
# s.save()
# else:
# return HttpResponse("Data Does Not Exist")
# return HttpResponse("Data Deleted Successfully")
# user_details = ExtraInfo.objects.all().filter(user=current_user).first()
# desig_id = Designation.objects.all().filter(name='Upper Division Clerk')
# temp = HoldsDesignation.objects.all().filter(designation = desig_id).first()
# print (temp)
# print (current_user)
# acadadmin = temp.working
# k = str(user_details).split()
# print(k)
# final_user = k[2]
# if (str(acadadmin) != str(final_user)):
# return HttpResponseRedirect('/academic-procedures/')
# if request.method == "POST":
# print(request.POST)
# rollno=request.POST.get('roll')
# print(rollno)
# student = ExtraInfo.objects.get(id=rollno)
# print(student.address)
# if not student:
# data = {}
# return JsonResponse(data)
# else:
# father = request.POST.get('father')
# mother = request.POST.get('mother')
# add = request.POST.get('address')
# hall = request.POST.get('hall')
# room = request.POST.get('room')
# cpi = request.POST.get('cpi')
# student.address = str(hall) + " " + str(room)
# student.save()
# s = Student.objects.get(id=student)
# s.father_name=father
# s.mother_name=mother
# s.hall_no = hall
# s.room_no = room
# s.save()
# return HttpResponseRedirect('/academic-procedures/')
# return HttpResponseRedirect('/academic-procedures/')
# choices = request.POST.getlist('choice')
# for i in choices:
# course = Course.objects.all().filter(course_id=i).first()
# course.acad_selection = True
# course.save()
# courses = Course.objects.all()
# for i in courses:
# if i.course_id not in choices:
# i.acad_selection = False
# i.save()
# return HttpResponseRedirect('/academic-procedures/')
# sem_cred.append(0)
# for i in range(1, 10):
# sem = "sem_"+"1"
# sem_cred.append(request.POST.getlist(sem)[0])
# for i in range(1, 9):
# sem = MinimumCredits.objects.all().filter(semester=i).first()
# sem.credits = sem_cred[i+1]
# sem.save()
# return HttpResponse("Worked")
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] | 2.297855 | 7,460 |
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""Functional test cases for subject-replicator"""
import sys
from subject.tests import functional
from subject.tests.utils import execute
| [
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12260,
628
] | 3.539604 | 202 |
# -*- coding: utf-8 -*-
# marketanalysis
# ----------------
# A fast, efficient Python library for generating country, province and state
# specific sets of marketmarketholidayss on the fly. It aims to make determining whether a
# specific date is a holiday as fast and flexible as possible.
#
# Author: MichealOmojola <[email protected]>
# Website: https://github.com/OmoMicheal/trading_days
# License: MIT (see LICENSE file)
# Version: 0.1 (April 7, 2021)
import unittest
from datetime import date
from dateutil.relativedelta import relativedelta
# import sys
# sys.path.insert(0, 'C:/Users/momojola/projects/marketanalysis/marketanalysis/')
from marketanalysis import marketholidays
from marketanalysis import markettradingdays
# if __name__ == "__main__":
# unittest.main() | [
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220,
220,
220,
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555,
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3419
] | 3.103448 | 261 |
#!/usr/bin/env python
from distutils.core import setup
from catkin_pkg.python_setup import generate_distutils_setup
d = generate_distutils_setup(
packages=['rosmsg'],
package_dir={'': 'src'},
scripts=['scripts/rosmsg', 'scripts/rosmsg-proto', 'scripts/rossrv'],
requires=['genmsg', 'rosbag', 'roslib', 'rospkg']
)
setup(**d)
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#!/usr/bin/env python
# linked_list.py - Linked list implementation in Python by Sergey 2015
"""
Linked list implementation in Python
"""
# Standard modules
import unittest
import sys
import os
import argparse
import re
import random
import subprocess
import getpass
import shutil
# Additional modules
###############################################################################
# Linked_list Class
###############################################################################
###############################################################################
# Executable code
###############################################################################
def main():
# Sandbox
sb = Linked_list(" ".join(sys.argv[1:]))
sb.run()
###############################################################################
# Unit Tests
###############################################################################
if __name__ == "__main__":
if sys.argv[-1] == "-ut":
unittest.main(argv=[" "])
main()
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] | 4.377119 | 236 |
from devito import VectorTimeFunction, TimeFunction, NODE
from devito.tools import memoized_meth
from examples.seismic import PointSource
from examples.seismic.viscoacoustic.operators import (ForwardOperator, AdjointOperator)
def adjoint(self, rec, srca=None, va=None, pa=None, vp=None, qp=None, b=None, r=None,
**kwargs):
"""
Adjoint modelling function that creates the necessary
data objects for running an adjoint modelling operator.
Parameters
----------
rec : SparseTimeFunction or array-like
The receiver data. Please note that
these act as the source term in the adjoint run.
srca : SparseTimeFunction or array-like
The resulting data for the interpolated at the
original source location.
va : VectorTimeFunction, optional
The computed particle velocity.
pa : TimeFunction, optional
Stores the computed wavefield.
vp : Function or float, optional
The time-constant velocity.
qp : Function, optional
The P-wave quality factor.
b : Function, optional
The time-constant inverse density.
r : TimeFunction, optional
The computed memory variable.
Returns
-------
Adjoint source, wavefield and performance summary.
"""
# Create a new adjoint source and receiver symbol
srca = srca or PointSource(name='srca', grid=self.model.grid,
time_range=self.geometry.time_axis,
coordinates=self.geometry.src_positions)
if self.time_order == 1:
va = va or VectorTimeFunction(name="va", grid=self.model.grid,
time_order=self.time_order,
space_order=self.space_order)
kwargs.update({k.name: k for k in va})
pa = pa or TimeFunction(name="pa", grid=self.model.grid,
time_order=self.time_order, space_order=self.space_order,
staggered=NODE)
# Memory variable:
r = r or TimeFunction(name="r", grid=self.model.grid, time_order=self.time_order,
space_order=self.space_order, staggered=NODE)
b = b or self.model.b
qp = qp or self.model.qp
# Pick vp from model unless explicitly provided
vp = vp or self.model.vp
# Execute operator and return wavefield and receiver data
if self.kernel == 'sls':
# Execute operator and return wavefield and receiver data
# With Memory variable
summary = self.op_adj().apply(src=srca, rec=rec, pa=pa, r=r, b=b, vp=vp,
qp=qp, dt=kwargs.pop('dt', self.dt), **kwargs)
else:
summary = self.op_adj().apply(src=srca, rec=rec, pa=pa, vp=vp, b=b, qp=qp,
dt=kwargs.pop('dt', self.dt), **kwargs)
return srca, pa, va, summary
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] | 2.109987 | 1,482 |
import pandas as pd
import os
from tqdm import tqdm
from collections import defaultdict
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori
dataPath = "data/static"
itemSetList = []
if __name__ == '__main__':
dataInit()
loadDataSet()
df = myApriori()
frequent_itemsets = apriori(df, min_support=0.0035, use_colnames=True)
frequent_itemsets['length'] = frequent_itemsets['itemsets'].apply(lambda x: len(x))
print(frequent_itemsets[(frequent_itemsets['length'] >= 2)])
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] | 2.804124 | 194 |
import os
from tonclient.client import TonClient
from tonclient.types import Abi, CallSet, Signer, ClientConfig, \
ParamsOfEncodeMessage, ParamsOfProcessMessage
BASE_DIR = os.path.dirname(__file__)
SAMPLES_DIR = os.path.join(BASE_DIR, 'samples')
GIVER_ADDRESS = '0:f5c2510bfe407363cb1db6b9d7bc1184a05f8b343aeaa828189c580e8569ee23'
client_config = ClientConfig()
client_config.network.endpoints = ['https://tonos.freeton.surf']
async_core_client = TonClient(config=client_config)
sync_core_client = TonClient(config=client_config, is_core_async=False)
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] | 2.616822 | 214 |
from kajiki import i18n
from flask import request
from flask_kajiki import render_template
# N. B. settting i18n.gettext would affect tests from all modules,
# so we test for request path that only functions from this module could set
i18n.gettext = gettext
def test_does_translations(app):
"""Callback interface is able to inject Translator filter"""
with app.test_request_context(path='/test_i18n'):
rendered = render_template('i18n.html')
# TODO DOCTYPE; see also render_args
expected = '<p>HELLO!</p>'
assert rendered == expected
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] | 2.929293 | 198 |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule, build_upsample_layer
from mmcv.ops import Conv2d
from mmcv.ops.carafe import CARAFEPack
from mmcv.runner import auto_fp16, force_fp32
from torch.nn.modules.utils import _pair
from mmdet.core import mask_target
from mmdet.models.builder import HEADS, build_loss
BYTES_PER_FLOAT = 4
# TODO: This memory limit may be too much or too little. It would be better to
# determine it based on available resources.
GPU_MEM_LIMIT = 1024**3 # 1 GB memory limit
def _do_paste_mask(masks, boxes, img_h, img_w, skip_empty=True):
"""Paste instance masks acoording to boxes.
This implementation is modified from
https://github.com/facebookresearch/detectron2/
Args:
masks (Tensor): N, 1, H, W
boxes (Tensor): N, 4
img_h (int): Height of the image to be pasted.
img_w (int): Width of the image to be pasted.
skip_empty (bool): Only paste masks within the region that
tightly bound all boxes, and returns the results this region only.
An important optimization for CPU.
Returns:
tuple: (Tensor, tuple). The first item is mask tensor, the second one
is the slice object.
If skip_empty == False, the whole image will be pasted. It will
return a mask of shape (N, img_h, img_w) and an empty tuple.
If skip_empty == True, only area around the mask will be pasted.
A mask of shape (N, h', w') and its start and end coordinates
in the original image will be returned.
"""
# On GPU, paste all masks together (up to chunk size)
# by using the entire image to sample the masks
# Compared to pasting them one by one,
# this has more operations but is faster on COCO-scale dataset.
device = masks.device
if skip_empty:
x0_int, y0_int = torch.clamp(
boxes.min(dim=0).values.floor()[:2] - 1,
min=0).to(dtype=torch.int32)
x1_int = torch.clamp(
boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32)
y1_int = torch.clamp(
boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32)
else:
x0_int, y0_int = 0, 0
x1_int, y1_int = img_w, img_h
x0, y0, x1, y1 = torch.split(boxes, 1, dim=1) # each is Nx1
N = masks.shape[0]
img_y = torch.arange(
y0_int, y1_int, device=device, dtype=torch.float32) + 0.5
img_x = torch.arange(
x0_int, x1_int, device=device, dtype=torch.float32) + 0.5
img_y = (img_y - y0) / (y1 - y0) * 2 - 1
img_x = (img_x - x0) / (x1 - x0) * 2 - 1
# img_x, img_y have shapes (N, w), (N, h)
if torch.isinf(img_x).any():
inds = torch.where(torch.isinf(img_x))
img_x[inds] = 0
if torch.isinf(img_y).any():
inds = torch.where(torch.isinf(img_y))
img_y[inds] = 0
gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1))
gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1))
grid = torch.stack([gx, gy], dim=3)
img_masks = F.grid_sample(
masks.to(dtype=torch.float32), grid, align_corners=False)
if skip_empty:
return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int))
else:
return img_masks[:, 0], ()
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] | 2.26298 | 1,483 |
# -*- coding:utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
| [
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] | 3.043478 | 46 |
import torch.nn as nn
import torch as torch
import math
import torch.utils.model_zoo as model_zoo
###########################################################
#
# U-ResNet
# U-net witih ResNet modules
#
# Semantic segmentation network used by MicroBooNE
# to label track/shower pixels
#
# resnet implementation from pytorch.torchvision module
# U-net from (cite)
#
# meant to be copy of caffe version
#
###########################################################
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,padding=1, bias=False)
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] | 2.978166 | 229 |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import random
import time
import json
import math
from functools import partial
import numpy as np
import paddle
from paddle.io import DataLoader
from args import parse_args
import paddlenlp as ppnlp
from paddlenlp.data import Pad, Stack, Tuple, Dict
from paddlenlp.transformers import BertForQuestionAnswering, BertTokenizer
from paddlenlp.transformers import ErnieForQuestionAnswering, ErnieTokenizer
from paddlenlp.transformers import ErnieGramForQuestionAnswering, ErnieGramTokenizer
from paddlenlp.transformers import RobertaForQuestionAnswering, RobertaTokenizer
from paddlenlp.transformers import LinearDecayWithWarmup
from paddlenlp.metrics.squad import squad_evaluate, compute_prediction
from paddlenlp.datasets import load_dataset
MODEL_CLASSES = {
"bert": (BertForQuestionAnswering, BertTokenizer),
"ernie": (ErnieForQuestionAnswering, ErnieTokenizer),
"ernie_gram": (ErnieGramForQuestionAnswering, ErnieGramTokenizer),
"roberta": (RobertaForQuestionAnswering, RobertaTokenizer)
}
def run(args):
paddle.set_device(args.device)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
rank = paddle.distributed.get_rank()
task_name = args.task_name.lower()
args.model_type = args.model_type.lower()
model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path)
set_seed(args)
if rank == 0:
if os.path.exists(args.model_name_or_path):
print("init checkpoint from %s" % args.model_name_or_path)
model = model_class.from_pretrained(args.model_name_or_path)
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
if args.do_train:
if args.train_file:
train_ds = load_dataset(task_name, data_files=args.train_file)
else:
train_ds = load_dataset(task_name, splits='train')
train_ds.map(prepare_train_features, batched=True)
train_batch_sampler = paddle.io.DistributedBatchSampler(
train_ds, batch_size=args.batch_size, shuffle=True)
train_batchify_fn = lambda samples, fn=Dict({
"input_ids": Pad(axis=0, pad_val=tokenizer.pad_token_id),
"token_type_ids": Pad(axis=0, pad_val=tokenizer.pad_token_type_id),
"start_positions": Stack(dtype="int64"),
"end_positions": Stack(dtype="int64")
}): fn(samples)
train_data_loader = DataLoader(
dataset=train_ds,
batch_sampler=train_batch_sampler,
collate_fn=train_batchify_fn,
return_list=True)
num_training_steps = args.max_steps if args.max_steps > 0 else len(
train_data_loader) * args.num_train_epochs
num_train_epochs = math.ceil(num_training_steps /
len(train_data_loader))
lr_scheduler = LinearDecayWithWarmup(
args.learning_rate, num_training_steps, args.warmup_proportion)
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
]
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
epsilon=args.adam_epsilon,
parameters=model.parameters(),
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in decay_params)
criterion = CrossEntropyLossForSQuAD()
global_step = 0
tic_train = time.time()
for epoch in range(num_train_epochs):
for step, batch in enumerate(train_data_loader):
global_step += 1
input_ids, token_type_ids, start_positions, end_positions = batch
logits = model(
input_ids=input_ids, token_type_ids=token_type_ids)
loss = criterion(logits, (start_positions, end_positions))
if global_step % args.logging_steps == 0:
print(
"global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
% (global_step, epoch + 1, step + 1, loss,
args.logging_steps / (time.time() - tic_train)))
tic_train = time.time()
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.clear_grad()
if global_step % args.save_steps == 0 or global_step == num_training_steps:
if rank == 0:
output_dir = os.path.join(args.output_dir,
"model_%d" % global_step)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# need better way to get inner model of DataParallel
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
print('Saving checkpoint to:', output_dir)
if global_step == num_training_steps:
break
if args.do_predict and rank == 0:
if args.predict_file:
dev_ds = load_dataset(task_name, data_files=args.predict_file)
else:
dev_ds = load_dataset(task_name, splits='dev')
dev_ds.map(prepare_validation_features, batched=True)
dev_batch_sampler = paddle.io.BatchSampler(
dev_ds, batch_size=args.batch_size, shuffle=False)
dev_batchify_fn = lambda samples, fn=Dict({
"input_ids": Pad(axis=0, pad_val=tokenizer.pad_token_id),
"token_type_ids": Pad(axis=0, pad_val=tokenizer.pad_token_type_id)
}): fn(samples)
dev_data_loader = DataLoader(
dataset=dev_ds,
batch_sampler=dev_batch_sampler,
collate_fn=dev_batchify_fn,
return_list=True)
evaluate(model, dev_data_loader, args)
if __name__ == "__main__":
args = parse_args()
run(args)
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] | 2.192726 | 3,217 |
# Credit to https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0
import gym
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
env = gym.make('FrozenLake-v0')
# NEURAL NETWORK IMPLEMENTATION
tf.reset_default_graph()
# Feature vector for current state representation
input1 = tf.placeholder(shape=[1, env.observation_space.n], dtype=tf.float32)
# tf.Variable(<initial-value>, name=<optional-name>)
# tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None)
# Weighting W vector in range 0 - 0.01 (like the way Andrew Ng did with *0.01
W = tf.Variable(tf.random_uniform([env.observation_space.n, env.action_space.n], 0, 0.01))
# Qout with shape [1, env.action_space.n] - Action state value for Q[s, a] with every a available at a state
Qout = tf.matmul(input1, W)
# Greedy action at a state
predict = tf.argmax(Qout, axis=1)
# Feature vector for next state representation
nextQ = tf.placeholder(shape=[1, env.action_space.n], dtype=tf.float32)
# Entropy loss
loss = tf.reduce_sum(tf.square(Qout - nextQ))
trainer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
updateModel = trainer.minimize(loss)
# TRAIN THE NETWORK
init = tf.global_variables_initializer()
# Set learning parameters
y = 0.99
e = 0.1
number_episodes = 2000
# List to store total rewards and steps per episode
jList = []
rList = []
with tf.Session() as sess:
sess.run(init)
for i in range(number_episodes):
print("Episode #{} is running!".format(i))
# First state
s = env.reset()
rAll = 0
d = False
j = 0
# Q network
while j < 200: # or While not d:
j += 1
# Choose action by epsilon (e) greedy
# print("s = ", s," --> Identity s:s+1: ", np.identity(env.observation_space.n)[s:s+1])
# s = 0 --> Identity s: s + 1: [[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
# s = 1 --> Identity s: s + 1: [[0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
# Identity [s:s+1] is a one-hot vector
# Therefore W is the actual Q value
a, allQ = sess.run([predict, Qout], feed_dict={input1: np.identity(env.observation_space.n)[s:s+1]})
if np.random.rand(1) < e:
a[0] = env.action_space.sample()
s1, r, d, _ = env.step(a[0])
# Obtain next state Q value by feeding the new state throughout the network
Q1 = sess.run(Qout, feed_dict={input1: np.identity(env.observation_space.n)[s1:s1+1]})
maxQ1 = np.max(Q1)
targetQ = allQ
targetQ[0, a[0]] = r + y * maxQ1
# Train our network using target and predicted Q values
_, W1 = sess.run([updateModel, W], feed_dict={input1: np.identity(env.observation_space.n)[s:s+1], nextQ: targetQ})
rAll += r
s = s1
if d:
e = 1./((i/50) + 10)
break
jList.append(j)
rList.append(rAll)
env.close()
plt.figure()
plt.plot(rList, label="Return - Q Learning")
plt.show()
plt.figure()
plt.plot(jList, label="Steps - Q Learning")
plt.show()
# -------------------------------------------------------------------------
# TABULAR IMPLEMENTATION
#
# # Set learning parameters
# lr = 0.8
# y = 0.95
# number_episodes = 20000
#
# # Initial table with all zeros
# Q = np.zeros([env.observation_space.n, env.action_space.n])
#
# # List of reward and steps per episode
# rList = []
# for i in range (number_episodes):
# print("Episode #{} is running!".format(i))
# s = env.reset()
# rAll = 0
# d = False
# j = 0
# while j < 99:
# j += 1
# # Choose an action by greedily (with noise) picking from Q table
# # Because of the noise, it is epsilon-greedy with epsilon decreasing over time
# a = np.argmax(Q[s, :] + np.random.rand(1, env.action_space.n)*(1./(i + 1)))
# s1, r, d, _ = env.step(a)
# # env.render()
#
# # Update Q table with new knowledge
# Q[s, a] = Q[s, a] + lr * (r + y * np.max(Q[s1, :]) - Q[s, a])
# rAll += r
# s = s1
# if d:
# break
# rList.append(rAll)
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] | 2.200509 | 1,965 |
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""All KeyTypes and which languages support them."""
# Placeholder for import for type annotations
from tink import aead
from tink import daead
from tink import hybrid
from tink import mac
from tink import prf
from tink import signature
from tink import streaming_aead
from tink.proto import tink_pb2
# All languages supported by cross-language tests.
ALL_LANGUAGES = ['cc', 'java', 'go', 'python']
# All KeyTypes (without the prefix 'type.googleapis.com/google.crypto.tink.')
AEAD_KEY_TYPES = [
'AesEaxKey',
'AesGcmKey',
'AesGcmSivKey',
'AesCtrHmacAeadKey',
'ChaCha20Poly1305Key',
'XChaCha20Poly1305Key',
]
DAEAD_KEY_TYPES = ['AesSivKey']
STREAMING_AEAD_KEY_TYPES = [
'AesCtrHmacStreamingKey',
'AesGcmHkdfStreamingKey',
]
HYBRID_PRIVATE_KEY_TYPES = ['EciesAeadHkdfPrivateKey']
MAC_KEY_TYPES = [
'AesCmacKey',
'HmacKey',
]
SIGNATURE_KEY_TYPES = [
'EcdsaPrivateKey',
'Ed25519PrivateKey',
'RsaSsaPkcs1PrivateKey',
'RsaSsaPssPrivateKey',
]
PRF_KEY_TYPES = [
'AesCmacPrfKey',
'HmacPrfKey',
'HkdfPrfKey',
]
ALL_KEY_TYPES = (
AEAD_KEY_TYPES + DAEAD_KEY_TYPES + STREAMING_AEAD_KEY_TYPES +
HYBRID_PRIVATE_KEY_TYPES + MAC_KEY_TYPES + SIGNATURE_KEY_TYPES +
PRF_KEY_TYPES)
# All languages that are supported by a KeyType
SUPPORTED_LANGUAGES = {
'AesEaxKey': ['cc', 'java', 'python'],
'AesGcmKey': ['cc', 'java', 'go', 'python'],
'AesGcmSivKey': ['cc', 'python'],
'AesCtrHmacAeadKey': ['cc', 'java', 'go', 'python'],
'ChaCha20Poly1305Key': ['java', 'go'],
'XChaCha20Poly1305Key': ['cc', 'java', 'go', 'python'],
'AesSivKey': ['cc', 'java', 'go', 'python'],
'AesCtrHmacStreamingKey': ['cc', 'java', 'go', 'python'],
'AesGcmHkdfStreamingKey': ['cc', 'java', 'go', 'python'],
'EciesAeadHkdfPrivateKey': ['cc', 'java', 'go', 'python'],
'AesCmacKey': ['cc', 'java', 'go', 'python'],
'HmacKey': ['cc', 'java', 'go', 'python'],
'EcdsaPrivateKey': ['cc', 'java', 'go', 'python'],
'Ed25519PrivateKey': ['cc', 'java', 'go', 'python'],
'RsaSsaPkcs1PrivateKey': ['cc', 'java', 'python'],
'RsaSsaPssPrivateKey': ['cc', 'java', 'python'],
'AesCmacPrfKey': ['cc', 'java', 'go', 'python'],
'HmacPrfKey': ['cc', 'java', 'go', 'python'],
'HkdfPrfKey': ['cc', 'java', 'go', 'python'],
}
KEY_TYPE_FROM_URL = {
'type.googleapis.com/google.crypto.tink.' + key_type: key_type
for key_type in ALL_KEY_TYPES}
# For each KeyType, a list of all KeyTemplate Names that must be supported.
KEY_TEMPLATE_NAMES = {
'AesEaxKey': ['AES128_EAX', 'AES256_EAX'],
'AesGcmKey': ['AES128_GCM', 'AES256_GCM'],
'AesGcmSivKey': ['AES128_GCM_SIV', 'AES256_GCM_SIV'],
'AesCtrHmacAeadKey': ['AES128_CTR_HMAC_SHA256', 'AES256_CTR_HMAC_SHA256'],
'ChaCha20Poly1305Key': ['CHACHA20_POLY1305'],
'XChaCha20Poly1305Key': ['XCHACHA20_POLY1305'],
'AesSivKey': ['AES256_SIV'],
'AesCtrHmacStreamingKey': [
'AES128_CTR_HMAC_SHA256_4KB',
'AES256_CTR_HMAC_SHA256_4KB',
],
'AesGcmHkdfStreamingKey': [
'AES128_GCM_HKDF_4KB',
'AES256_GCM_HKDF_4KB',
'AES256_GCM_HKDF_1MB',
],
'EciesAeadHkdfPrivateKey': [
'ECIES_P256_HKDF_HMAC_SHA256_AES128_GCM',
'ECIES_P256_HKDF_HMAC_SHA256_AES128_CTR_HMAC_SHA256'
],
'AesCmacKey': ['AES_CMAC'],
'HmacKey': [
'HMAC_SHA256_128BITTAG', 'HMAC_SHA256_256BITTAG',
'HMAC_SHA512_256BITTAG', 'HMAC_SHA512_512BITTAG'
],
'EcdsaPrivateKey': [
'ECDSA_P256', 'ECDSA_P384', 'ECDSA_P384_SHA384', 'ECDSA_P521',
'ECDSA_P256_IEEE_P1363', 'ECDSA_P384_IEEE_P1363',
'ECDSA_P384_SHA384_IEEE_P1363', 'ECDSA_P521_IEEE_P1363'
],
'Ed25519PrivateKey': ['ED25519'],
'RsaSsaPkcs1PrivateKey': [
'RSA_SSA_PKCS1_3072_SHA256_F4', 'RSA_SSA_PKCS1_4096_SHA512_F4'
],
'RsaSsaPssPrivateKey': [
'RSA_SSA_PSS_3072_SHA256_SHA256_32_F4',
'RSA_SSA_PSS_4096_SHA512_SHA512_64_F4'
],
'AesCmacPrfKey': ['AES_CMAC_PRF'],
'HmacPrfKey': ['HMAC_PRF_SHA256', 'HMAC_PRF_SHA512'],
'HkdfPrfKey': ['HKDF_PRF_SHA256'],
}
# KeyTemplate (as Protobuf) for each KeyTemplate name.
KEY_TEMPLATE = {
'AES128_EAX':
aead.aead_key_templates.AES128_EAX,
'AES256_EAX':
aead.aead_key_templates.AES256_EAX,
'AES128_GCM':
aead.aead_key_templates.AES128_GCM,
'AES256_GCM':
aead.aead_key_templates.AES256_GCM,
'AES128_GCM_SIV':
aead.aead_key_templates.AES128_GCM_SIV,
'AES256_GCM_SIV':
aead.aead_key_templates.AES256_GCM_SIV,
'AES128_CTR_HMAC_SHA256':
aead.aead_key_templates.AES128_CTR_HMAC_SHA256,
'AES256_CTR_HMAC_SHA256':
aead.aead_key_templates.AES256_CTR_HMAC_SHA256,
'CHACHA20_POLY1305':
tink_pb2.KeyTemplate(
type_url=('type.googleapis.com/google.crypto.tink.' +
'ChaCha20Poly1305Key'),
output_prefix_type=tink_pb2.TINK),
'XCHACHA20_POLY1305':
aead.aead_key_templates.XCHACHA20_POLY1305,
'AES256_SIV':
daead.deterministic_aead_key_templates.AES256_SIV,
'AES128_CTR_HMAC_SHA256_4KB':
streaming_aead.streaming_aead_key_templates.AES128_CTR_HMAC_SHA256_4KB,
'AES256_CTR_HMAC_SHA256_4KB':
streaming_aead.streaming_aead_key_templates.AES256_CTR_HMAC_SHA256_4KB,
'AES128_GCM_HKDF_4KB':
streaming_aead.streaming_aead_key_templates.AES128_GCM_HKDF_4KB,
'AES256_GCM_HKDF_4KB':
streaming_aead.streaming_aead_key_templates.AES256_GCM_HKDF_4KB,
'AES256_GCM_HKDF_1MB':
streaming_aead.streaming_aead_key_templates.AES256_GCM_HKDF_1MB,
'ECIES_P256_HKDF_HMAC_SHA256_AES128_GCM':
hybrid.hybrid_key_templates.ECIES_P256_HKDF_HMAC_SHA256_AES128_GCM,
'ECIES_P256_HKDF_HMAC_SHA256_AES128_CTR_HMAC_SHA256':
hybrid.hybrid_key_templates
.ECIES_P256_HKDF_HMAC_SHA256_AES128_CTR_HMAC_SHA256,
'AES_CMAC':
mac.mac_key_templates.AES_CMAC,
'HMAC_SHA256_128BITTAG':
mac.mac_key_templates.HMAC_SHA256_128BITTAG,
'HMAC_SHA256_256BITTAG':
mac.mac_key_templates.HMAC_SHA256_256BITTAG,
'HMAC_SHA512_256BITTAG':
mac.mac_key_templates.HMAC_SHA512_256BITTAG,
'HMAC_SHA512_512BITTAG':
mac.mac_key_templates.HMAC_SHA512_512BITTAG,
'ECDSA_P256':
signature.signature_key_templates.ECDSA_P256,
'ECDSA_P384':
signature.signature_key_templates.ECDSA_P384,
'ECDSA_P384_SHA384':
signature.signature_key_templates.ECDSA_P384_SHA384,
'ECDSA_P521':
signature.signature_key_templates.ECDSA_P521,
'ECDSA_P256_IEEE_P1363':
signature.signature_key_templates.ECDSA_P256_IEEE_P1363,
'ECDSA_P384_IEEE_P1363':
signature.signature_key_templates.ECDSA_P384_IEEE_P1363,
'ECDSA_P384_SHA384_IEEE_P1363':
signature.signature_key_templates.ECDSA_P384_SHA384_IEEE_P1363,
'ECDSA_P521_IEEE_P1363':
signature.signature_key_templates.ECDSA_P521_IEEE_P1363,
'ED25519':
signature.signature_key_templates.ED25519,
'RSA_SSA_PKCS1_3072_SHA256_F4':
signature.signature_key_templates.RSA_SSA_PKCS1_3072_SHA256_F4,
'RSA_SSA_PKCS1_4096_SHA512_F4':
signature.signature_key_templates.RSA_SSA_PKCS1_4096_SHA512_F4,
'RSA_SSA_PSS_3072_SHA256_SHA256_32_F4':
signature.signature_key_templates.RSA_SSA_PSS_3072_SHA256_SHA256_32_F4,
'RSA_SSA_PSS_4096_SHA512_SHA512_64_F4':
signature.signature_key_templates.RSA_SSA_PSS_4096_SHA512_SHA512_64_F4,
'AES_CMAC_PRF':
prf.prf_key_templates.AES_CMAC,
'HMAC_PRF_SHA256':
prf.prf_key_templates.HMAC_SHA256,
'HMAC_PRF_SHA512':
prf.prf_key_templates.HMAC_SHA512,
'HKDF_PRF_SHA256':
prf.prf_key_templates.HKDF_SHA256,
}
SUPPORTED_LANGUAGES_BY_TEMPLATE_NAME = {
name: SUPPORTED_LANGUAGES[KEY_TYPE_FROM_URL[template.type_url]]
for name, template in KEY_TEMPLATE.items()
}
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] | 1.960314 | 4,334 |
#!/usr/bin/env python3
#########################################################
# Written by Carl Youngblood, [email protected]
# Copyright (c) 2018 Blockscale LLC
# released under the MIT license
#########################################################
from flask import Flask, request, Response, json, jsonify
from src.remote_signer import RemoteSigner
from os import path
import logging
logging.basicConfig(filename='./remote-signer.log', format='%(asctime)s %(message)s', level=logging.INFO)
app = Flask(__name__)
# sample config used for testing
config = {
'hsm_username': 'resigner',
'hsm_slot': 1,
'hsm_lib': '/opt/cloudhsm/lib/libcloudhsm_pkcs11.so',
'node_addr': 'http://node.internal:8732',
'keys': {
'tz3aTaJ3d7Rh4yXpereo4yBm21xrs4bnzQvW': {
'public_key': 'p2pk67jx4rEadFpbHdiPhsKxZ4KCoczLWqsEpNarWZ7WQ1SqKMf7JsS',
'private_handle': 7,
'public_handle': 9
}
}
}
logging.info('Opening keys.json')
if path.isfile('keys.json'):
logging.info('Found keys.json')
with open('keys.json', 'r') as myfile:
json_blob = myfile.read().replace('\n', '')
logging.info('Parsed keys.json successfully as JSON')
config = json.loads(json_blob)
logging.info('Config contains: {}'.format(json.dumps(config, indent=2)))
if __name__ == '__main__':
app.run(host='127.0.0.1', port=5000, debug=True)
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] | 2.442341 | 581 |
from binaryninja import log
from .utils import BinjaStruct, read_pe_header, split_bits, update_percentage
# https://msdn.microsoft.com/en-us/library/ft9x1kdx.aspx
RUNTIME_FUNCTION_t = BinjaStruct('<III', names = ('BeginAddress', 'EndAddress', 'UnwindData'))
UNWIND_INFO_t = BinjaStruct('<BBBB', names = ('VersionAndFlags', 'SizeOfProlog', 'CountOfCodes', 'FrameRegisterAndOffset'))
UNW_FLAG_NHANDLER = 0x0
UNW_FLAG_EHANDLER = 0x1
UNW_FLAG_UHANDLER = 0x2
UNW_FLAG_FHANDLER = 0x3
UNW_FLAG_CHAININFO = 0x4
UNWIND_CODE_t = BinjaStruct('<BB', names = ('CodeOffset', 'UnwindOpAndInfo'))
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] | 2.538793 | 232 |
"""
3D visualization primitives based on Plotly.
We might want to instead use a more powerful library like Open3D.
Plotly however supports animations, buttons and sliders.
1) Initialize a figure with `fig = init_figure()`
2) Plot points, cameras, lines, or create a slider animation.
3) Call `fig.show()` to render the figure.
"""
import plotly.graph_objects as go
import numpy as np
from ..pixlib.geometry.utils import to_homogeneous
def init_figure(height=800):
"""Initialize a 3D figure."""
fig = go.Figure()
fig.update_layout(
height=height,
scene_camera=dict(
eye=dict(x=0., y=-.1, z=-2), up=dict(x=0, y=-1., z=0)),
scene=dict(
xaxis=dict(showbackground=False),
yaxis=dict(showbackground=False),
aspectmode='data', dragmode='orbit'),
margin=dict(l=0, r=0, b=0, t=0, pad=0)) # noqa E741
return fig
def plot_points(fig, pts, color='rgba(255, 0, 0, 1)', ps=2):
"""Plot a set of 3D points."""
x, y, z = pts.T
tr = go.Scatter3d(
x=x, y=y, z=z, mode='markers', marker_size=ps,
marker_color=color, marker_line_width=.2)
fig.add_trace(tr)
def plot_camera(fig, R, t, K, color='rgb(0, 0, 255)'):
"""Plot a camera as a cone with camera frustum."""
x, y, z = t
u, v, w = R @ -np.array([0, 0, 1])
tr = go.Cone(
x=[x], y=[y], z=[z], u=[u], v=[v], w=[w], anchor='tip',
showscale=False, colorscale=[[0, color], [1, color]],
sizemode='absolute')
fig.add_trace(tr)
W, H = K[0, 2]*2, K[1, 2]*2
corners = np.array([[0, 0], [W, 0], [W, H], [0, H], [0, 0]])
corners = to_homogeneous(corners) @ np.linalg.inv(K).T
corners = (corners/2) @ R.T + t
x, y, z = corners.T
tr = go.Scatter3d(
x=x, y=y, z=z, line=dict(color='rgba(0, 0, 0, .5)'),
marker=dict(size=0.0001), showlegend=False)
fig.add_trace(tr)
def create_slider_animation(fig, traces):
"""Create a slider that animates a list of traces (e.g. 3D points)."""
slider = {'steps': []}
frames = []
fig.add_trace(traces[0])
idx = len(fig.data) - 1
for i, tr in enumerate(traces):
frames.append(go.Frame(name=str(i), traces=[idx], data=[tr]))
step = {"args": [
[str(i)],
{"frame": {"redraw": True},
"mode": "immediate"}],
"label": i,
"method": "animate"}
slider['steps'].append(step)
fig.frames = tuple(frames)
fig.layout.sliders = (slider,)
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] | 2.129195 | 1,192 |
import numpy as np
GRID_SIZE = 5
def check_victory(check_grids: np.ndarray) -> set[int]:
"""return empty set if no victory, else set of id of the wining grids"""
return set(np.where(check_grids.sum(axis=1).max(axis=1) == 5)[0]).union(
np.where(check_grids.sum(axis=2).max(axis=1) == 5)[0]
)
if __name__ == "__main__":
main()
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] | 2.339869 | 153 |
'''
US Population Pyramid Over Time
===============================
A population pyramid shows the distribution of age groups within a population.
It uses a slider widget that is bound to the year to visualize the age
distribution over time.
'''
# category: case studies
import altair as alt
from altair.expr import datum, if_
from vega_datasets import data
pop = data.population.url
slider = alt.binding_range(min=1850, max=2000, step=10)
select_year = alt.selection_single(name='year', fields=['year'], bind=slider)
base = alt.Chart(pop).add_selection(
select_year
).transform_filter(
select_year
).transform_calculate(
gender=if_(datum.sex == 1, 'Male', 'Female')
)
title = alt.Axis(title='population')
color_scale = alt.Scale(domain=['Male', 'Female'],
range=['#1f77b4', '#e377c2'])
left = base.transform_filter(
datum.gender == 'Female'
).encode(
y=alt.X('age:O', axis=None),
x=alt.X('sum(people):Q', axis=title, sort=alt.SortOrder('descending')),
color=alt.Color('gender:N', scale=color_scale, legend=None)
).mark_bar().properties(title='Female')
middle = base.encode(
y=alt.X('age:O', axis=None),
text=alt.Text('age:Q'),
).mark_text().properties(width=20)
right = base.transform_filter(
datum.gender == 'Male'
).encode(
y=alt.X('age:O', axis=None),
x=alt.X('sum(people):Q', axis=title),
color=alt.Color('gender:N', scale=color_scale, legend=None)
).mark_bar().properties(title='Male')
left | middle | right | [
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220,
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25,
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28,
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48310,
7,
7839,
11639,
25486,
11537,
198,
198,
9464,
930,
3504,
930,
826
] | 2.697297 | 555 |
from django.db import migrations, models
import django.utils.timezone
import model_utils.fields
| [
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13,
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628
] | 3.592593 | 27 |
from palm_tree import db
#
# class Logs(db.Model):
# id = db.Column(db.Integer, primary_key=True)
# uuid = db.Column(db.Integer)
# payload = db.Column(db.Text)
# datetime = db.Column(db.DateTime)
#
# def __init__(self, uuid, payload, datetime):
# self.uuid = uuid
# self.payload = payload
# self.datetime = datetime
#
# def __repr__(self):
# return '<Data %r>' % self.payload
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7,
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2,
220,
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27,
6601,
4064,
81,
29,
6,
4064,
2116,
13,
15577,
2220,
198
] | 2.117073 | 205 |
# Copyright 2013 Mirantis, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import functools
import re
import subprocess
import sys
from scapy import all as scapy
DHCP_OFFER_COLUMNS = ('iface', 'mac', 'server_ip', 'server_id', 'gateway',
'dport', 'message', 'yiaddr')
def command_util(*command):
"""object with stderr and stdout
"""
return subprocess.Popen(command, stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
def _check_vconfig():
"""Check vconfig installed or not
"""
return not command_util('which', 'vconfig').stderr.read()
def _iface_state(iface):
"""For a given iface return it's state
returns UP, DOWN, UNKNOWN
"""
state = command_util('ip', 'link', 'show', iface).stdout.read()
search_result = re.search(r'.*<(?P<state>.*)>.*', state)
if search_result:
state_list = search_result.groupdict().get('state', [])
if 'UP' in state_list:
return 'UP'
else:
return 'DOWN'
return 'UNKNOWN'
def check_iface_exist(iface):
"""Check provided interface exists
"""
return not command_util("ip", "link", "show", iface).stderr.read()
def get_item_properties(item, columns):
"""Get specified in columns properties, with preserved order.
Required for correct cli table generation
:param item: dict
:param columns: list with arbitrary keys
"""
properties = []
for key in columns:
properties.append(item.get(key, ''))
return properties
def format_options(options):
"""Util for serializing dhcp options
@options = [1,2,3]
>>> format_options([1, 2, 3])
'\x01\x02\x03'
"""
return "".join((chr(item) for item in options))
def single_format(func):
"""Manage format of dhcp response
"""
return formatter
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1441,
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] | 2.661093 | 897 |
from ma import ma
from models.johnson_scanner_data import JohnsonScannerDataModel
from schemas.brand import BrandSchema
from schemas.category import CategorySchema
from schemas.facts_in_data import FactsInDataSchema
from schemas.market import MarketSchema
from schemas.period import PeriodSchema
| [
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] | 3.75 | 80 |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
################################################################################
# CSE 253: Programming Assignment 3
# Winter 2019
# Code author: Jenny Hamer (+ modifications by Tejash Desai)
#
# Filename: baseline_cnn.py
#
# Description:
#
# This file contains the starter code for the baseline architecture you will use
# to get a little practice with PyTorch and compare the results of with your
# improved architecture.
#
# Be sure to fill in the code in the areas marked #TODO.
################################################################################
# PyTorch and neural network imports
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as func
import torch.nn.init as torch_init
import torch.optim as optim
# Data utils and dataloader
import torchvision
from torchvision import transforms, utils
from xray_dataloader_zscored import ChestXrayDataset, create_split_loaders
import matplotlib.pyplot as plt
import numpy as np
import os
| [
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299,
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355,
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198,
11748,
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628,
628
] | 3.867647 | 272 |
import json
import urllib.request
import credentials
from datetime import datetime, timedelta
api = NewsAPI(credentials.NYT_API)
date_time_obj = datetime.now()
api.get_nyt_last_week_articles('election', date_time_obj) | [
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] | 3.027778 | 72 |
# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
from smexperiments import trial_component, api_types
import datetime
import pytest
import unittest.mock
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] | 3.710383 | 183 |
from cadnano.cnproxy import UndoCommand
from cadnano.strand import Strand
from cadnano import getBatch
import cadnano.preferences as prefs
import random
# end class
# end class | [
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33,
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82,
198,
11748,
4738,
198,
2,
886,
1398,
198,
2,
886,
1398
] | 3.259259 | 54 |
from w1thermsensor import W1ThermSensor
sensor = W1ThermSensor()
temperature_in_celsius = sensor.get_temperature()
temperature_in_fahrenheit = sensor.get_temperature(W1ThermSensor.DEGREES_F)
temperature_in_all_units = sensor.get_temperatures([W1ThermSensor.DEGREES_C, W1ThermSensor.DEGREES_F, W1ThermSensor.KELVIN])
print("Sensor id:" + sensor.id)
print(temperature_in_celsius)
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# Generated by Django 3.1.1 on 2020-09-01 17:04
from django.db import migrations, models
| [
2,
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] | 2.84375 | 32 |
#Copyright ReportLab Europe Ltd. 2000-2016
#see license.txt for license details
#history http://www.reportlab.co.uk/cgi-bin/viewcvs.cgi/public/reportlab/trunk/reportlab/graphics/charts/__init__.py
__version__='3.3.0'
__doc__='''Business charts'''
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# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
# pylint:disable=too-many-lines
import os
try:
from urllib.parse import urlparse
except ImportError:
from urlparse import urlparse # pylint: disable=import-error
from knack.log import get_logger
from knack.util import CLIError
from azure.cli.core.commands.validators import (
get_default_location_from_resource_group, validate_file_or_dict, validate_parameter_set, validate_tags)
from azure.cli.core.util import hash_string
from azure.cli.command_modules.vm._vm_utils import check_existence, get_target_network_api, get_storage_blob_uri
from azure.cli.command_modules.vm._template_builder import StorageProfile
import azure.cli.core.keys as keys
from ._client_factory import _compute_client_factory
from ._actions import _get_latest_image_version
logger = get_logger(__name__)
def _get_resource_group_from_vault_name(cli_ctx, vault_name):
"""
Fetch resource group from vault name
:param str vault_name: name of the key vault
:return: resource group name or None
:rtype: str
"""
from azure.cli.core.profiles import ResourceType
from azure.cli.core.commands.client_factory import get_mgmt_service_client
from msrestazure.tools import parse_resource_id
client = get_mgmt_service_client(cli_ctx, ResourceType.MGMT_KEYVAULT).vaults
for vault in client.list():
id_comps = parse_resource_id(vault.id)
if id_comps['name'] == vault_name:
return id_comps['resource_group']
return None
def _validate_secrets(secrets, os_type):
"""
Validates a parsed JSON array containing secrets for use in VM Creation
Secrets JSON structure
[{
"sourceVault": { "id": "value" },
"vaultCertificates": [{
"certificateUrl": "value",
"certificateStore": "cert store name (only on windows)"
}]
}]
:param dict secrets: Dict fitting the JSON description above
:param string os_type: the type of OS (linux or windows)
:return: errors if any were found
:rtype: list
"""
is_windows = os_type == 'windows'
errors = []
try:
loaded_secret = [validate_file_or_dict(secret) for secret in secrets]
except Exception as err:
raise CLIError('Error decoding secrets: {0}'.format(err))
for idx_arg, narg_secret in enumerate(loaded_secret):
for idx, secret in enumerate(narg_secret):
if 'sourceVault' not in secret:
errors.append(
'Secret is missing sourceVault key at index {0} in arg {1}'.format(
idx, idx_arg))
if 'sourceVault' in secret and 'id' not in secret['sourceVault']:
errors.append(
'Secret is missing sourceVault.id key at index {0} in arg {1}'.format(
idx, idx_arg))
if 'vaultCertificates' not in secret or not secret['vaultCertificates']:
err = 'Secret is missing vaultCertificates array or it is empty at index {0} in ' \
'arg {1} '
errors.append(err.format(idx, idx_arg))
else:
for jdx, cert in enumerate(secret['vaultCertificates']):
message = 'Secret is missing {0} within vaultCertificates array at secret ' \
'index {1} and vaultCertificate index {2} in arg {3}'
if 'certificateUrl' not in cert:
errors.append(message.format('certificateUrl', idx, jdx, idx_arg))
if is_windows and 'certificateStore' not in cert:
errors.append(message.format('certificateStore', idx, jdx, idx_arg))
if errors:
raise CLIError('\n'.join(errors))
# region VM Create Validators
def _parse_image_argument(cmd, namespace):
""" Systematically determines what type is supplied for the --image parameter. Updates the
namespace and returns the type for subsequent processing. """
from msrestazure.tools import is_valid_resource_id
from msrestazure.azure_exceptions import CloudError
import re
# 1 - check if a fully-qualified ID (assumes it is an image ID)
if is_valid_resource_id(namespace.image):
return 'image_id'
# 2 - attempt to match an URN pattern
urn_match = re.match('([^:]*):([^:]*):([^:]*):([^:]*)', namespace.image)
if urn_match:
namespace.os_publisher = urn_match.group(1)
namespace.os_offer = urn_match.group(2)
namespace.os_sku = urn_match.group(3)
namespace.os_version = urn_match.group(4)
if not any([namespace.plan_name, namespace.plan_product, namespace.plan_publisher]):
image_plan = _get_image_plan_info_if_exists(cmd, namespace)
if image_plan:
namespace.plan_name = image_plan.name
namespace.plan_product = image_plan.product
namespace.plan_publisher = image_plan.publisher
return 'urn'
# 3 - unmanaged vhd based images?
if urlparse(namespace.image).scheme:
return 'uri'
# 4 - attempt to match an URN alias (most likely)
from azure.cli.command_modules.vm._actions import load_images_from_aliases_doc
images = load_images_from_aliases_doc(cmd.cli_ctx)
matched = next((x for x in images if x['urnAlias'].lower() == namespace.image.lower()), None)
if matched:
namespace.os_publisher = matched['publisher']
namespace.os_offer = matched['offer']
namespace.os_sku = matched['sku']
namespace.os_version = matched['version']
return 'urn'
# 5 - check if an existing managed disk image resource
compute_client = _compute_client_factory(cmd.cli_ctx)
try:
compute_client.images.get(namespace.resource_group_name, namespace.image)
namespace.image = _get_resource_id(cmd.cli_ctx, namespace.image, namespace.resource_group_name,
'images', 'Microsoft.Compute')
return 'image_id'
except CloudError:
err = 'Invalid image "{}". Use a custom image name, id, or pick one from {}'
raise CLIError(err.format(namespace.image, [x['urnAlias'] for x in images]))
# pylint: disable=inconsistent-return-statements
# pylint: disable=too-many-branches, too-many-statements
# endregion
# region VMSS Create Validators
# endregion
# region disk, snapshot, image validators
# TODO move to its own command module https://github.com/Azure/azure-cli/issues/5105
# endregion
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] | 2.544177 | 2,705 |
import re
import httplib2
from bs4 import BeautifulSoup
from scraping.faqscrapperutil import stripExtra, removeDuplicates, removeBlackListedQuestions, getBlackListedQuestions, convertToJsonList, saveToMongo
from scraping.Constants import ENABLE_CUSTOM_QUESTIONS_FILTER, FAQ_LINKS, COLLECTION_NAME
# link = "https://transportation.oregonstate.edu/aabc/frequently-asked-questions"
# questions, answerList = getFaqOfLink(link)
if __name__== "__main__":
with open(FAQ_LINKS, 'r') as myfile:
FAQ_LINKS = myfile.read().split('\n')
faqJsonList = []
for i in range(0, len(FAQ_LINKS)):
link = FAQ_LINKS[i]
questions, answerList = getFaqOfLink(link)
jsonList = convertToJsonList(link, questions, answerList)
faqJsonList.extend(jsonList)
# saveJsonToFile(faqJsonList, "output.txt")
saveToMongo(faqJsonList, COLLECTION_NAME) | [
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] | 2.364341 | 387 |
"""Terminal utilities specific to message archives.
Creates colored text and helps write Messages output.
"""
from contextlib import contextmanager
import itertools
import readline
FG_COLORS = dict(itertools.chain(
zip(("black",
"red",
"green",
"yellow",
"blue",
"magenta",
"cyan",
"white",
), range(30, 38)),
zip(("bright_black",
"bright_red",
"bright_green",
"bright_yellow",
"bright_blue",
"bright_magenta",
"bright_cyan",
"bright_white",
), range(90, 98))))
BG_COLORS = dict((f"on_{key}", val + 10) for key, val in FG_COLORS.items())
ATTRIBUTES = dict(
zip(("bold",
"faint",
"italic",
"underline",
"slow_blink",
"rapid_blink",
"reverse",
"conceal",
"strikethrough",
), range(1, 10)))
def colored(text, color=None, on_color=None, attrs=None, escape=False):
"""Wraps text with ANSI escape codes to achieve the desired look.
Args:
color: The foreground color.
on_color: The background color.
attrs: A list of effects.
escape: True to escape invisibles (for readline); else False.
Returns:
A string with the original text wrapped by escape codes.
"""
codes = []
if color:
codes.append(FG_COLORS[color])
if on_color:
codes.append(BG_COLORS[on_color])
if attrs:
codes.extend(ATTRIBUTES[attr] for attr in attrs)
if not escape:
esc = lambda n: n
return "%s%s%s" % (esc(sgr(*codes)), text, esc(sgr(0)))
def confirm(text):
"""Presents a yes/no prompt to the user and handles replies.
Args:
text: A message string to present before confirmation.
Returns:
True if the user confirmed the prompt; else False.
"""
replies = {
"yes": True,
"no": False,
}
prompt = "%s (yes/no): " % colored("Are you sure?", "red",
attrs=["bold"], escape=True)
reply = ""
with readline_disabled():
print(text)
while reply not in replies:
try:
reply = input(prompt).casefold()
except (EOFError, KeyboardInterrupt):
reply = "no"
print(reply)
return replies[reply]
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] | 2.277555 | 998 |
# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import torch
import torch.nn as nn
from parameterized import parameterized
from monai.networks import eval_mode
from monai.networks.blocks import SubpixelUpsample
from monai.networks.layers.factories import Conv
TEST_CASE_SUBPIXEL = []
for inch in range(1, 5):
for dim in range(1, 4):
for factor in range(1, 3):
test_case = [
{"dimensions": dim, "in_channels": inch, "scale_factor": factor},
(2, inch, *([8] * dim)),
(2, inch, *([8 * factor] * dim)),
]
TEST_CASE_SUBPIXEL.append(test_case)
TEST_CASE_SUBPIXEL_2D_EXTRA = [
{"dimensions": 2, "in_channels": 2, "scale_factor": 3},
(2, 2, 8, 4), # different size for H and W
(2, 2, 24, 12),
]
TEST_CASE_SUBPIXEL_3D_EXTRA = [
{"dimensions": 3, "in_channels": 1, "scale_factor": 2},
(2, 1, 16, 8, 4), # different size for H, W and D
(2, 1, 32, 16, 8),
]
conv_block = nn.Sequential(
Conv[Conv.CONV, 3](1, 4, kernel_size=1), Conv[Conv.CONV, 3](4, 8, kernel_size=3, stride=1, padding=1)
)
TEST_CASE_SUBPIXEL_CONV_BLOCK_EXTRA = [
{"dimensions": 3, "in_channels": 1, "scale_factor": 2, "conv_block": conv_block},
(2, 1, 16, 8, 4), # different size for H, W and D
(2, 1, 32, 16, 8),
]
TEST_CASE_SUBPIXEL.append(TEST_CASE_SUBPIXEL_2D_EXTRA)
TEST_CASE_SUBPIXEL.append(TEST_CASE_SUBPIXEL_3D_EXTRA)
TEST_CASE_SUBPIXEL.append(TEST_CASE_SUBPIXEL_CONV_BLOCK_EXTRA)
# add every test back with the pad/pool sequential component omitted
for tests in list(TEST_CASE_SUBPIXEL):
args: dict = tests[0] # type: ignore
args = dict(args)
args["apply_pad_pool"] = False
TEST_CASE_SUBPIXEL.append([args, tests[1], tests[2]])
if __name__ == "__main__":
unittest.main()
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555,
715,
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] | 2.393909 | 985 |
from staticfg import CFGBuilder
userCfg = CFGBuilder().build_from_file('user.py', './auction/user.py')
bidCfg = CFGBuilder().build_from_file('bid.py', './auction/bid.py')
auctionCfg = CFGBuilder().build_from_file('auction.py','./auction/auction.py')
#auctionEventCfg = CFGBuilder().build_from_file('auction_event.py','./auction/auction_event.py')
bidCfg.build_visual('bidCfg', 'pdf')
auctionCfg.build_visual('auctionCfg', 'pdf')
#auctionEventCfg.build_visual('auctionEventCfg.pdf', 'pdf')
| [
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] | 2.497462 | 197 |
total_budget = 0
while True:
destination = input()
if destination == "End":
break
minimal_budget = float(input())
while True:
command = input()
if command == "End":
break
money = float(command)
total_budget += money
if total_budget >= minimal_budget:
print(f"Going to {destination}!")
total_budget = 0
break
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] | 2.075829 | 211 |
from footmark.regioninfo import RegionInfo
| [
6738,
2366,
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628
] | 4.4 | 10 |
from __future__ import absolute_import, division, print_function
import os
import json
from glue.core import Subset
DISPATCH = {}
def save_page(page, page_number, label, subset):
""" Convert a tab of a glue session into a D3PO page
:param page: Tuple of data viewers to save
:param label: Tab label
"""
result = {}
# layout settings
result['grid'] = {'nRows': 1, 'nColumns': len(page)}
result['name'] = str(label)
result['caption'] = 'Generated by Glue'
# style settings
d = page[0]._data[0]
unselected = dict(opacity=d.style.alpha,
size=d.style.markersize / 2,
color=d.style.color)
result['markerStyle'] = dict(unselected=unselected)
if subset is not None:
s = subset.style
selected = dict(opacity=s.alpha, size=s.markersize / 2, color=s.color)
result['markerStyle']['selected'] = selected
result['selection'] = {'type': 'booleanColumn',
'columnName': 'selection_%i' % page_number}
result['histogramStyle'] = result['markerStyle']
# save each plot
result['plots'] = list(map(save_plot, page, range(len(page))))
return result
def save_scatter(plot, index):
""" Convert a single glue scatter plot to a D3PO plot
:param plot: Glue scatter plot
:class:`~glue.viewers.scatter.qt.ScatterViewer`
:param index: 1D index of plot on the page
:type index: int
:rtype: json-serializable dict
"""
result = save_plot_base(plot, index)
result['type'] = 'scatter'
result['xAxis'] = dict(columnName=plot.state.x_att.label,
range=[float(plot.state.x_min), float(plot.state.x_max)])
result['yAxis'] = dict(columnName=plot.state.y_att.label,
range=[float(plot.state.y_min), float(plot.state.y_max)])
# XXX log scales
return result
def save_histogram(plot, index):
""" Convert a single histogram to a D3PO plot
:param plot: Glue histogram
:type plot: :class:`~glue.viewers.histogram.qt.HistogramViewer`
:param index: 1D index of plot on the page
:type index: int
:rtype: json-serializable dict
"""
result = save_plot_base(plot, index)
result['type'] = 'histogram'
result['xAxis'] = dict(columnName=plot.state.x_att.label,
bins=int(plot.state.hist_n_bin),
range=[float(plot.state.hist_x_min), float(plot.state.hist_x_max)])
# XXX normed, cumultive, log
return result
def stage_subsets(application):
"""
Return a tuple of the subset to use for each stage/tab,
or None if the tab has no subset
If more than one subset is used per stage/tab, returns None
"""
result = []
for page in application.viewers:
subset = None
for viewer in page:
for layer_artist in viewer.layers:
if not layer_artist.visible:
continue
s = layer_artist.layer
if not isinstance(s, Subset):
continue
if subset is not None and s is not subset:
return None
if subset is None:
subset = s
result.append(subset)
return tuple(result)
def can_save_d3po(application):
"""
Check whether an application can be exported to D3PO.
Raises an exception if not
"""
dc = application.session.data_collection
if len(dc) != 1:
raise ValueError("D3PO Export only supports a single dataset")
for tab in application.viewers:
for viewer in tab:
if not isinstance(viewer, tuple(DISPATCH.keys())):
raise ValueError("D3PO Export only supports scatter "
"and histogram plots")
if sum(len(tab) for tab in application.viewers) == 0:
raise ValueError("D3PO Export requires at least one scatterplot "
"or histogram")
if stage_subsets(application) is None:
raise ValueError("D3PO Export restricted to 0 or 1 subsets visible "
"in each tab")
def make_data_file(data, subsets, path):
"""
Create the data.csv file, given Data and tuple of subsets
"""
from astropy.table import Table, Column
data_path = os.path.join(path, 'data.csv')
t = Table([data[c] for c in data.components],
names=[c.label for c in data.components])
for i, subset in enumerate(subsets):
if subset is None:
continue
c = Column(data=subset.to_mask().astype('i'), name='selection_%i' % i)
t.add_column(c)
t.write(data_path, format='ascii', delimiter=',')
def save_d3po(application, path, launch=True):
"""Save a Glue session to a D3PO bundle.
Currently, this has the following restrictions:
- The Glue session must have only one dataset open, and 0 or 1 subsets
- Only scatter plots or histograms are present
- At least one plot is present
:param application: Glue appication to save
:param path: Path to directory to save in. Will be created if needed
"""
if os.path.exists(path) and not os.path.isdir(path):
os.unlink(path)
if not os.path.exists(path):
os.mkdir(path)
data = application.session.data_collection[0]
subsets = stage_subsets(application)
viewers = application.viewers
# data.csv
make_data_file(data, subsets, path)
# states.json
result = {}
result['filename'] = 'data.csv' # XXX don't think this is needed?
result['title'] = "Glue export of %s" % data.label
result['states'] = list(map(save_page, application.viewers,
range(len(viewers)),
application.tab_names,
subsets))
state_path = os.path.join(path, 'states.json')
with open(state_path, 'w') as outfile:
json.dump(result, outfile, indent=2, sort_keys=True)
# index.html
html_path = os.path.join(path, 'index.html')
with open(html_path, 'w') as outfile:
outfile.write(HTML)
# show the result
if launch:
launch_d3po(path)
def launch_d3po(path):
"""Start a server to view an exported D3PO bundle, and open a browser.
:param path: The TLD of the bundle
"""
from glue.external.six.moves.socketserver import TCPServer
from glue.external.six.moves.SimpleHTTPServer import SimpleHTTPRequestHandler
from random import randrange
from socket import error
import webbrowser
from threading import Thread
os.chdir(path)
while True:
try:
PORT = randrange(8000, 9000)
server = TCPServer(("", PORT), SimpleHTTPRequestHandler, False)
server.allow_reuse_address = True
server.server_bind()
break
except error: # port already taken
pass
print('Serving D3PO on port 0.0.0.0:%i' % PORT)
server.server_activate()
thread = Thread(target=server.serve_forever)
thread.setDaemon(True) # do not prevent shutdown
thread.start()
webbrowser.open('http://0.0.0.0:%i' % PORT)
HTML = """
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<link rel="stylesheet" type="text/css" href="http://d3po.org/static/css/style.css">
<link rel="stylesheet" type="text/css" href="http://d3po.org/static/css/d3po.css">
<link href='http://fonts.googleapis.com/css?family=Source+Sans+Pro:100,200,300,400,700' rel='stylesheet' type='text/css'>
<style>
#footer {
position: fixed;
bottom: 0;
right: 0;
}
</style>
<!-- not to be confused with Planet Telex -->
<!-- Javscript dependencies -->
<script src="http://d3js.org/d3.v3.min.js" charset="utf-8"></script>
<script src="http://d3po.org/static/js/util.js"></script>
<script src="//ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
<script src="http://d3po.org/static/js/d3po.js"></script>
<script src="http://d3po.org/static/js/d3po.init.js"></script>
</head>
<body>
<div id="svg"><svg></svg></div>
<div id="controls">
<ul class="navigation">
</ul>
</div>
<div id="caption"></div>
<div id="footer">
More information: <a href="http://d3po.org">d3po.org</a>
</div>
<script type="text/javascript">
$(document).ready(function() {
initialize('states.json', 'data.csv');
}
);
</script>
</body>
</html>
"""
try:
from glue.viewers.scatter.qt import ScatterViewer
from glue.viewers.histogram.qt import HistogramViewer
except ImportError:
pass
else:
DISPATCH[ScatterViewer] = save_scatter
DISPATCH[HistogramViewer] = save_histogram
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27,
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316,
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23,
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27,
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14,
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14,
7635,
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27,
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5239,
14,
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1,
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67,
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27,
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4023,
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13,
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13,
785,
14,
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30,
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28,
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50,
504,
10,
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25,
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11,
2167,
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13,
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271,
13,
785,
14,
1228,
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14,
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82,
14,
73,
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16,
13,
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14,
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220,
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1436,
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422,
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13,
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16341,
17267,
12331,
25,
198,
220,
220,
220,
1208,
198,
17772,
25,
198,
220,
220,
220,
13954,
47,
11417,
58,
3351,
1436,
7680,
263,
60,
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3613,
62,
1416,
1436,
198,
220,
220,
220,
13954,
47,
11417,
58,
13749,
21857,
7680,
263,
60,
796,
3613,
62,
10034,
21857,
198
] | 2.368608 | 3,657 |
print('Crie sua P.A. de 10 termos')
n1 = int(input('Digite o primeiro termo da P.A.: '))
r = int(input('Digite a razo: '))
termo = n1
c = 1
print('A P.A. (', end='')
while c <= 10:
print('{}'.format(termo), end='')
print(', ' if c < 10 else '', end='')
termo += r
c += 1
print(')')
| [
4798,
10786,
34,
5034,
424,
64,
350,
13,
32,
13,
390,
838,
3381,
418,
11537,
198,
198,
77,
16,
796,
493,
7,
15414,
10786,
19511,
578,
267,
6994,
7058,
3381,
78,
12379,
350,
13,
32,
11207,
705,
4008,
198,
81,
796,
493,
7,
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10786,
19511,
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198,
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32,
13,
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28,
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8,
198,
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269,
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838,
25,
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220,
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220,
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90,
92,
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18982,
7,
4354,
78,
828,
886,
28,
7061,
8,
198,
220,
220,
220,
3601,
7,
3256,
705,
611,
269,
1279,
838,
2073,
705,
3256,
886,
28,
7061,
8,
198,
220,
220,
220,
3381,
78,
15853,
374,
198,
220,
220,
220,
269,
15853,
352,
198,
4798,
10786,
8,
11537,
628
] | 2.119718 | 142 |
from __future__ import unicode_literals
import codecs
import logging
try:
from http.client import HTTPConnection, HTTPSConnection
except ImportError:
# pylint: disable=import-error
from httplib import HTTPConnection, HTTPSConnection
try:
from urllib.parse import urlparse
except ImportError:
# pylint: disable=import-error
from urlparse import urlparse
from .constants import (
UNLIMITED_REDIRECTS,
)
from .cursors import Cursor
from ._ephemeral import EphemeralRqlited as _EphemeralRqlited
from .extensions import PARSE_DECLTYPES, PARSE_COLNAMES
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price = float(input())
puzzles = int(input())
dolls = int(input())
bears = int(input())
minions = int(input())
trucks = int(input())
total_toys = puzzles + dolls + bears + minions + trucks
price_puzzles = puzzles * 2.6
price_dolls = dolls * 3
price_bears = bears * 4.1
price_minions = minions * 8.2
price_trucks = trucks * 2
total_price = price_puzzles + price_dolls + price_bears + price_minions + price_trucks
if total_toys >= 50:
total_price = total_price - (total_price * 0.25)
rent = total_price * 0.1
total_price = total_price - rent
if total_price >= price:
print(f"Yes! {(total_price - price):.2f} lv left.")
else:
print(f"Not enough money! {(price - total_price):.2f} lv needed.")
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] | 2.473154 | 298 |
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# -*- encoding: utf-8 -*-
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""
Utils for testing the API service.
"""
import datetime
import json
ADMIN_TOKEN = '4562138218392831'
MEMBER_TOKEN = '4562138218392832'
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705,
2231,
5237,
1485,
6469,
1507,
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2078,
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6,
628
] | 3.054475 | 257 |
import datetime
from flask_wtf import FlaskForm
from wtforms import (
StringField,
TextAreaField,
DateTimeField,
HiddenField,
PasswordField,
)
from wtforms.validators import DataRequired, ValidationError, Email, EqualTo
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] | 3.012346 | 81 |
# -*- coding: utf-8 -*-
"""Proiect.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1TR1Frf0EX4PtFZkLlVdGtMTINqhoQwRw
"""
# Importarea librariilor
import numpy as np
import pandas as pd # pandas pentru citirea fisierelor
from sklearn import preprocessing
from sklearn import svm # importarea modelului
from sklearn.feature_extraction.text import TfidfVectorizer # modelarea datelor pentru a obtine valori numerice din text
from sklearn.metrics import classification_report, confusion_matrix
# Incarcarea datelor
train_labels = pd.read_csv('train_labels.txt', sep='\t', header=None, engine='python')
train_labels = train_labels.to_numpy() # convertim data frame-ul intr-un vector
train_labels = train_labels[:,1] # pastram doar etichetele
train_samples = pd.read_csv('train_samples.txt', sep='\t', header=None, engine='python')
train_samples = train_samples.to_numpy()
train_samples = train_samples[:,1] # pastram doar cuvintele
validation_samples = pd.read_csv('validation_samples.txt', sep='\t', header=None, engine='python')
validation_samples = validation_samples.to_numpy()
validation_samples = validation_samples[:,1] # salvam cuvintele
validation_labels = pd.read_csv('validation_labels.txt', sep='\t', header=None, engine='python')
validation_labels = validation_labels.to_numpy()
validation_labels = validation_labels[:,1] # pastram doar etichetele
test_samples = pd.read_csv('test_samples.txt', sep='\t', header=None, engine='python')
test_samples = test_samples.to_numpy()
label = test_samples[:,0] # salvam etichetele
test_samples = test_samples[:,1] # salvam cuvintele
# Modelarea datelor
vectorizer = TfidfVectorizer()
training_features = vectorizer.fit_transform(train_samples)
validation_features = vectorizer.transform(validation_samples)
testing_features = vectorizer.transform(test_samples)
# Normalizarea datelor
norm_train, norm_test = normalize_data(training_features, testing_features)
norm_validation, _ = normalize_data(validation_features, validation_features)
# Aplicam modelul SVM
model_svm = svm.SVC(kernel='linear', C=23, gamma=110) # definim modelul
model_svm.fit(norm_train, train_labels) # procesul de invatare
test_predictions = model_svm.predict(norm_test) # predictie pe datele de test
print("Classification report: ")
print(classification_report(validation_labels, model_svm.predict(norm_validation)))
print("Confusion matrix: ")
print(confusion_matrix(validation_labels, model_svm.predict(norm_validation)))
# Exportarea datelor in format CSV
test_export = {'id':label,'label':test_predictions}
data_f = pd.DataFrame(test_export)
data_f.to_csv('test_submission.csv',index=False) | [
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] | 2.858351 | 946 |
from .logger import Logger
from .logger_supervised import LoggerSupervised
| [
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198
] | 3.571429 | 21 |
from __future__ import annotations
import collections
import inspect
from dataclasses import dataclass
from enum import Enum
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
from flytekit.common import constants as _common_constants
from flytekit.common.exceptions.user import FlyteValidationException, FlyteValueException
from flytekit.core.base_task import PythonTask
from flytekit.core.class_based_resolver import ClassStorageTaskResolver
from flytekit.core.condition import ConditionalSection
from flytekit.core.context_manager import (
BranchEvalMode,
CompilationState,
ExecutionState,
FlyteContext,
FlyteContextManager,
FlyteEntities,
)
from flytekit.core.interface import (
Interface,
transform_inputs_to_parameters,
transform_interface_to_typed_interface,
transform_signature_to_interface,
)
from flytekit.core.launch_plan import LaunchPlan
from flytekit.core.node import Node
from flytekit.core.promise import (
NodeOutput,
Promise,
VoidPromise,
binding_from_python_std,
create_and_link_node,
create_native_named_tuple,
create_task_output,
translate_inputs_to_literals,
)
from flytekit.core.python_auto_container import PythonAutoContainerTask
from flytekit.core.reference_entity import ReferenceEntity, WorkflowReference
from flytekit.core.type_engine import TypeEngine
from flytekit.loggers import logger
from flytekit.models import interface as _interface_models
from flytekit.models import literals as _literal_models
from flytekit.models.core import workflow as _workflow_model
GLOBAL_START_NODE = Node(
id=_common_constants.GLOBAL_INPUT_NODE_ID,
metadata=None,
bindings=[],
upstream_nodes=[],
flyte_entity=None,
)
def construct_input_promises(inputs: List[str]):
return {
input_name: Promise(var=input_name, val=NodeOutput(node=GLOBAL_START_NODE, var=input_name))
for input_name in inputs
}
def get_promise(binding_data: _literal_models.BindingData, outputs_cache: Dict[Node, Dict[str, Promise]]) -> Promise:
"""
This is a helper function that will turn a binding into a Promise object, using a lookup map. Please see
get_promise_map for the rest of the details.
"""
if binding_data.promise is not None:
if not isinstance(binding_data.promise, NodeOutput):
raise FlyteValidationException(
f"Binding data Promises have to be of the NodeOutput type {type(binding_data.promise)} found"
)
# b.var is the name of the input to the task
# binding_data.promise.var is the name of the upstream node's output we want
return outputs_cache[binding_data.promise.node][binding_data.promise.var]
elif binding_data.scalar is not None:
return Promise(var="placeholder", val=_literal_models.Literal(scalar=binding_data.scalar))
elif binding_data.collection is not None:
literals = []
for bd in binding_data.collection.bindings:
p = get_promise(bd, outputs_cache)
literals.append(p.val)
return Promise(
var="placeholder",
val=_literal_models.Literal(collection=_literal_models.LiteralCollection(literals=literals)),
)
elif binding_data.map is not None:
literals = {}
for k, bd in binding_data.map.bindings.items():
p = get_promise(bd, outputs_cache)
literals[k] = p.val
return Promise(
var="placeholder", val=_literal_models.Literal(map=_literal_models.LiteralMap(literals=literals))
)
raise FlyteValidationException("Binding type unrecognized.")
def get_promise_map(
bindings: List[_literal_models.Binding], outputs_cache: Dict[Node, Dict[str, Promise]]
) -> Dict[str, Promise]:
"""
Local execution of imperatively defined workflows is done node by node. This function will fill in the node's
entity's input arguments, which are specified using the bindings list, and a map of nodes to its outputs.
Basically this takes the place of propeller in resolving bindings, pulling in outputs from previously completed
nodes and filling in the necessary inputs.
"""
entity_kwargs = {}
for b in bindings:
entity_kwargs[b.var] = get_promise(b.binding, outputs_cache)
return entity_kwargs
class WorkflowBase(object):
def __repr__(self):
return (
f"WorkflowBase - {self._name} && "
f"Inputs ({len(self._python_interface.inputs)}): {self._python_interface.inputs} && "
f"Outputs ({len(self._python_interface.outputs)}): {self._python_interface.outputs} && "
f"Output bindings: {self._output_bindings} && "
)
def __call__(self, *args, **kwargs):
"""
The call pattern for Workflows is close to, but not exactly, the call pattern for Tasks. For local execution,
it goes
__call__ -> _local_execute -> execute
From execute, different things happen for the two Workflow styles. For PythonFunctionWorkflows, the Python
function is run, for the ImperativeWorkflow, each node is run one at a time.
"""
if len(args) > 0:
raise AssertionError("Only Keyword Arguments are supported for Workflow executions")
ctx = FlyteContextManager.current_context()
# Get default agruements and override with kwargs passed in
input_kwargs = self.python_interface.default_inputs_as_kwargs
input_kwargs.update(kwargs)
# The first condition is compilation.
if ctx.compilation_state is not None:
return create_and_link_node(ctx, entity=self, interface=self.python_interface, **input_kwargs)
# This condition is hit when this workflow (self) is being called as part of a parent's workflow local run.
# The context specifying the local workflow execution has already been set.
elif (
ctx.execution_state is not None and ctx.execution_state.mode == ExecutionState.Mode.LOCAL_WORKFLOW_EXECUTION
):
if ctx.execution_state.branch_eval_mode == BranchEvalMode.BRANCH_SKIPPED:
if self.python_interface and self.python_interface.output_tuple_name:
variables = [k for k in self.python_interface.outputs.keys()]
output_tuple = collections.namedtuple(self.python_interface.output_tuple_name, variables)
nones = [None for _ in self.python_interface.outputs.keys()]
return output_tuple(*nones)
else:
return None
# We are already in a local execution, just continue the execution context
return self._local_execute(ctx, **input_kwargs)
# Last is starting a local workflow execution
else:
# Run some sanity checks
# Even though the _local_execute call generally expects inputs to be Promises, we don't have to do the
# conversion here in this loop. The reason is because we don't prevent users from specifying inputs
# as direct scalars, which means there's another Promise-generating loop inside _local_execute too
for k, v in input_kwargs.items():
if k not in self.interface.inputs:
raise ValueError(f"Received unexpected keyword argument {k}")
if isinstance(v, Promise):
raise ValueError(f"Received a promise for a workflow call, when expecting a native value for {k}")
with FlyteContextManager.with_context(
ctx.with_execution_state(
ctx.new_execution_state().with_params(mode=ExecutionState.Mode.LOCAL_WORKFLOW_EXECUTION)
)
) as child_ctx:
result = self._local_execute(child_ctx, **input_kwargs)
expected_outputs = len(self.python_interface.outputs)
if expected_outputs == 0:
if result is None or isinstance(result, VoidPromise):
return None
else:
raise Exception(f"Workflow local execution expected 0 outputs but something received {result}")
if (1 < expected_outputs == len(result)) or (result is not None and expected_outputs == 1):
return create_native_named_tuple(ctx, result, self.python_interface)
raise ValueError("expected outputs and actual outputs do not match")
def execute(self, **kwargs):
raise Exception("Should not be called")
def _local_execute(self, ctx: FlyteContext, **kwargs) -> Union[Tuple[Promise], Promise, VoidPromise]:
# This is done to support the invariant that Workflow local executions always work with Promise objects
# holding Flyte literal values. Even in a wf, a user can call a sub-workflow with a Python native value.
for k, v in kwargs.items():
if not isinstance(v, Promise):
t = self.python_interface.inputs[k]
kwargs[k] = Promise(var=k, val=TypeEngine.to_literal(ctx, v, t, self.interface.inputs[k].type))
# The output of this will always be a combination of Python native values and Promises containing Flyte
# Literals.
function_outputs = self.execute(**kwargs)
# First handle the empty return case.
# A workflow function may return a task that doesn't return anything
# def wf():
# return t1()
# or it may not return at all
# def wf():
# t1()
# In the former case we get the task's VoidPromise, in the latter we get None
if isinstance(function_outputs, VoidPromise) or function_outputs is None:
if len(self.python_interface.outputs) != 0:
raise FlyteValueException(
function_outputs,
f"{function_outputs} received but interface has {len(self.python_interface.outputs)} outputs.",
)
return VoidPromise(self.name)
# Because we should've already returned in the above check, we just raise an error here.
if len(self.python_interface.outputs) == 0:
raise FlyteValueException(
function_outputs, f"{function_outputs} received but should've been VoidPromise or None."
)
expected_output_names = list(self.python_interface.outputs.keys())
if len(expected_output_names) == 1:
# Here we have to handle the fact that the wf could've been declared with a typing.NamedTuple of
# length one. That convention is used for naming outputs - and single-length-NamedTuples are
# particularly troublesome but elegant handling of them is not a high priority
# Again, we're using the output_tuple_name as a proxy.
if self.python_interface.output_tuple_name and isinstance(function_outputs, tuple):
wf_outputs_as_map = {expected_output_names[0]: function_outputs[0]}
else:
wf_outputs_as_map = {expected_output_names[0]: function_outputs}
else:
wf_outputs_as_map = {expected_output_names[i]: function_outputs[i] for i, _ in enumerate(function_outputs)}
# Basically we need to repackage the promises coming from the tasks into Promises that match the workflow's
# interface. We do that by extracting out the literals, and creating new Promises
wf_outputs_as_literal_dict = translate_inputs_to_literals(
ctx,
wf_outputs_as_map,
flyte_interface_types=self.interface.outputs,
native_types=self.python_interface.outputs,
)
# Recreate new promises that use the workflow's output names.
new_promises = [Promise(var, wf_outputs_as_literal_dict[var]) for var in expected_output_names]
return create_task_output(new_promises, self.python_interface)
class ImperativeWorkflow(WorkflowBase):
def __repr__(self):
return super().__repr__() + f"Nodes ({len(self.compilation_state.nodes)}): {self.compilation_state.nodes}"
def execute(self, **kwargs):
"""
Called by _local_execute. This function is how local execution for imperative workflows runs. Because when an
entity is added using the add_entity function, all inputs to that entity should've been already declared, we
can just iterate through the nodes in order and we shouldn't run into any dependency issues. That is, we force
the user to declare entities already in a topological sort. To keep track of outputs, we create a map to
start things off, filled in only with the workflow inputs (if any). As things are run, their outputs are stored
in this map.
After all nodes are run, we fill in workflow level outputs the same way as any other previous node.
"""
if not self.ready():
raise FlyteValidationException(f"Workflow not ready, wf is currently {self}")
# Create a map that holds the outputs of each node.
intermediate_node_outputs = {GLOBAL_START_NODE: {}} # type: Dict[Node, Dict[str, Promise]]
# Start things off with the outputs of the global input node, i.e. the inputs to the workflow.
# _local_execute should've already ensured that all the values in kwargs are Promise objects
for k, v in kwargs.items():
intermediate_node_outputs[GLOBAL_START_NODE][k] = v
# Next iterate through the nodes in order.
for node in self.compilation_state.nodes:
if node not in intermediate_node_outputs.keys():
intermediate_node_outputs[node] = {}
# Retrieve the entity from the node, and call it by looking up the promises the node's bindings require,
# and then fill them in using the node output tracker map we have.
entity = node.flyte_entity
entity_kwargs = get_promise_map(node.bindings, intermediate_node_outputs)
# Handle the calling and outputs of each node's entity
results = entity(**entity_kwargs)
expected_output_names = list(entity.python_interface.outputs.keys())
if isinstance(results, VoidPromise) or results is None:
continue # pragma: no cover # Move along, nothing to assign
# Because we should've already returned in the above check, we just raise an Exception here.
if len(entity.python_interface.outputs) == 0:
raise FlyteValueException(results, f"{results} received but should've been VoidPromise or None.")
# if there's only one output,
if len(expected_output_names) == 1:
if entity.python_interface.output_tuple_name and isinstance(results, tuple):
intermediate_node_outputs[node][expected_output_names[0]] = results[0]
else:
intermediate_node_outputs[node][expected_output_names[0]] = results
else:
if len(results) != len(expected_output_names):
raise FlyteValueException(results, f"Different lengths {results} {expected_output_names}")
for idx, r in enumerate(results):
intermediate_node_outputs[node][expected_output_names[idx]] = r
# The rest of this function looks like the above but now we're doing it for the workflow as a whole rather
# than just one node at a time.
if len(self.python_interface.outputs) == 0:
return VoidPromise(self.name)
# The values that we return below from the output have to be pulled by fulfilling all of the
# workflow's output bindings.
# The return style here has to match what 1) what the workflow would've returned had it been declared
# functionally, and 2) what a user would return in mock function. That is, if it's a tuple, then it
# should be a tuple here, if it's a one element named tuple, then we do a one-element non-named tuple,
# if it's a single element then we return a single element
if len(self.output_bindings) == 1:
# Again use presence of output_tuple_name to understand that we're dealing with a one-element
# named tuple
if self.python_interface.output_tuple_name:
return (get_promise(self.output_bindings[0].binding, intermediate_node_outputs),)
# Just a normal single element
return get_promise(self.output_bindings[0].binding, intermediate_node_outputs)
return tuple([get_promise(b.binding, intermediate_node_outputs) for b in self.output_bindings])
def add_entity(self, entity: Union[PythonTask, LaunchPlan, WorkflowBase], **kwargs) -> Node:
"""
Anytime you add an entity, all the inputs to the entity must be bound.
"""
# circular import
from flytekit.core.node_creation import create_node
ctx = FlyteContext.current_context()
if ctx.compilation_state is not None:
raise Exception("Can't already be compiling")
with FlyteContextManager.with_context(ctx.with_compilation_state(self.compilation_state)) as ctx:
n = create_node(entity=entity, **kwargs)
# Every time an entity is added, mark it as used. The above function though will gather all the input
# values but we're only interested in the ones that are Promises so let's filter for those.
# There's probably a way to clean this up, maybe key off of the name instead of value?
all_input_values = get_input_values(kwargs)
for input_value in filter(lambda x: isinstance(x, Promise), all_input_values):
if input_value in self._unbound_inputs:
self._unbound_inputs.remove(input_value)
return n
def add_workflow_input(self, input_name: str, python_type: Type) -> Interface:
"""
Adds an input to the workflow.
"""
if input_name in self._inputs:
raise FlyteValidationException(f"Input {input_name} has already been specified for wf {self.name}.")
self._python_interface = self._python_interface.with_inputs(extra_inputs={input_name: python_type})
self._interface = transform_interface_to_typed_interface(self._python_interface)
self._inputs[input_name] = Promise(var=input_name, val=NodeOutput(node=GLOBAL_START_NODE, var=input_name))
self._unbound_inputs.add(self._inputs[input_name])
return self._inputs[input_name]
def add_workflow_output(
self, output_name: str, p: Union[Promise, List[Promise], Dict[str, Promise]], python_type: Optional[Type] = None
):
"""
Add an output with the given name from the given node output.
"""
if output_name in self._python_interface.outputs:
raise FlyteValidationException(f"Output {output_name} already exists in workflow {self.name}")
if python_type is None:
if type(p) == list or type(p) == dict:
raise FlyteValidationException(
f"If specifying a list or dict of Promises, you must specify the python_type type for {output_name}"
f" starting with the container type (e.g. List[int]"
)
python_type = p.ref.node.flyte_entity.python_interface.outputs[p.var]
logger.debug(f"Inferring python type for wf output {output_name} from Promise provided {python_type}")
flyte_type = TypeEngine.to_literal_type(python_type=python_type)
ctx = FlyteContext.current_context()
if ctx.compilation_state is not None:
raise Exception("Can't already be compiling")
with FlyteContextManager.with_context(ctx.with_compilation_state(self.compilation_state)) as ctx:
b = binding_from_python_std(
ctx, output_name, expected_literal_type=flyte_type, t_value=p, t_value_type=python_type
)
self._output_bindings.append(b)
self._python_interface = self._python_interface.with_outputs(extra_outputs={output_name: python_type})
self._interface = transform_interface_to_typed_interface(self._python_interface)
def add_task(self, task: PythonTask, **kwargs) -> Node:
return self.add_entity(task, **kwargs)
def add_launch_plan(self, launch_plan: LaunchPlan, **kwargs) -> Node:
return self.add_entity(launch_plan, **kwargs)
def add_subwf(self, sub_wf: WorkflowBase, **kwargs) -> Node:
return self.add_entity(sub_wf, **kwargs)
def ready(self) -> bool:
"""
This function returns whether or not the workflow is in a ready state, which means
* Has at least one node
* All workflow inputs are bound
These conditions assume that all nodes and workflow i/o changes were done with the functions above, which
do additional checking.
"""
if len(self.compilation_state.nodes) == 0:
return False
if len(self._unbound_inputs) > 0:
return False
return True
class PythonFunctionWorkflow(WorkflowBase, ClassStorageTaskResolver):
"""
Please read :std:ref:`flyte:divedeep-workflows` first for a high-level understanding of what workflows are in Flyte.
This Python object represents a workflow defined by a function and decorated with the
:py:func:`@workflow <flytekit.workflow>` decorator. Please see notes on that object for additional information.
"""
def compile(self, **kwargs):
"""
Supply static Python native values in the kwargs if you want them to be used in the compilation. This mimics
a 'closure' in the traditional sense of the word.
"""
ctx = FlyteContextManager.current_context()
self._input_parameters = transform_inputs_to_parameters(ctx, self.python_interface)
all_nodes = []
prefix = f"{ctx.compilation_state.prefix}-{self.short_name}-" if ctx.compilation_state is not None else ""
with FlyteContextManager.with_context(
ctx.with_compilation_state(CompilationState(prefix=prefix, task_resolver=self))
) as comp_ctx:
# Construct the default input promise bindings, but then override with the provided inputs, if any
input_kwargs = construct_input_promises([k for k in self.interface.inputs.keys()])
input_kwargs.update(kwargs)
workflow_outputs = self._workflow_function(**input_kwargs)
all_nodes.extend(comp_ctx.compilation_state.nodes)
# This little loop was added as part of the task resolver change. The task resolver interface itself is
# more or less stateless (the future-proofing get_all_tasks function notwithstanding). However the
# implementation of the TaskResolverMixin that this workflow class inherits from (ClassStorageTaskResolver)
# does store state. This loop adds Tasks that are defined within the body of the workflow to the workflow
# object itself.
for n in comp_ctx.compilation_state.nodes:
if isinstance(n.flyte_entity, PythonAutoContainerTask) and n.flyte_entity.task_resolver == self:
logger.debug(f"WF {self.name} saving task {n.flyte_entity.name}")
self.add(n.flyte_entity)
# Iterate through the workflow outputs
bindings = []
output_names = list(self.interface.outputs.keys())
# The reason the length 1 case is separate is because the one output might be a list. We don't want to
# iterate through the list here, instead we should let the binding creation unwrap it and make a binding
# collection/map out of it.
if len(output_names) == 1:
if isinstance(workflow_outputs, tuple):
if len(workflow_outputs) != 1:
raise AssertionError(
f"The Workflow specification indicates only one return value, received {len(workflow_outputs)}"
)
if self.python_interface.output_tuple_name is None:
raise AssertionError(
"Outputs specification for Workflow does not define a tuple, but return value is a tuple"
)
workflow_outputs = workflow_outputs[0]
t = self.python_interface.outputs[output_names[0]]
b = binding_from_python_std(
ctx,
output_names[0],
self.interface.outputs[output_names[0]].type,
workflow_outputs,
t,
)
bindings.append(b)
elif len(output_names) > 1:
if not isinstance(workflow_outputs, tuple):
raise AssertionError("The Workflow specification indicates multiple return values, received only one")
if len(output_names) != len(workflow_outputs):
raise Exception(f"Length mismatch {len(output_names)} vs {len(workflow_outputs)}")
for i, out in enumerate(output_names):
if isinstance(workflow_outputs[i], ConditionalSection):
raise AssertionError("A Conditional block (if-else) should always end with an `else_()` clause")
t = self.python_interface.outputs[out]
b = binding_from_python_std(
ctx,
out,
self.interface.outputs[out].type,
workflow_outputs[i],
t,
)
bindings.append(b)
# Save all the things necessary to create an SdkWorkflow, except for the missing project and domain
self._nodes = all_nodes
self._output_bindings = bindings
if not output_names:
return None
if len(output_names) == 1:
return bindings[0]
return tuple(bindings)
def execute(self, **kwargs):
"""
This function is here only to try to streamline the pattern between workflows and tasks. Since tasks
call execute from dispatch_execute which is in _local_execute, workflows should also call an execute inside
_local_execute. This makes mocking cleaner.
"""
return self._workflow_function(**kwargs)
def workflow(
_workflow_function=None,
failure_policy: Optional[WorkflowFailurePolicy] = None,
interruptible: Optional[bool] = False,
):
"""
This decorator declares a function to be a Flyte workflow. Workflows are declarative entities that construct a DAG
of tasks using the data flow between tasks.
Unlike a task, the function body of a workflow is evaluated at serialization-time (aka compile-time). This is because
while we can determine the entire structure of a task by looking at the function's signature,
workflows need to run through the function itself because the body of the function is what expresses the workflow structure.
It's also important to note that, local execution notwithstanding, it is not evaluated again when the workflow runs on Flyte.
That is, workflows should not call non-Flyte entities since they are only run once (again, this is with respect to
the platform, local runs notwithstanding).
Please see the :std:doc:`cookbook:sphx_glr_auto_core_flyte_basics_basic_workflow.py` for more usage examples.
:param _workflow_function: This argument is implicitly passed and represents the decorated function.
:param failure_policy: Use the options in flytekit.WorkflowFailurePolicy
:param interruptible: Whether or not tasks launched from this workflow are by default interruptible
"""
if _workflow_function:
return wrapper(_workflow_function)
else:
return wrapper
class ReferenceWorkflow(ReferenceEntity, PythonFunctionWorkflow):
"""
A reference workflow is a pointer to a workflow that already exists on your Flyte installation. This
object will not initiate a network call to Admin, which is why the user is asked to provide the expected interface.
If at registration time the interface provided causes an issue with compilation, an error will be returned.
"""
def reference_workflow(
project: str,
domain: str,
name: str,
version: str,
) -> Callable[[Callable[..., Any]], ReferenceWorkflow]:
"""
A reference workflow is a pointer to a workflow that already exists on your Flyte installation. This
object will not initiate a network call to Admin, which is why the user is asked to provide the expected interface.
If at registration time the interface provided causes an issue with compilation, an error will be returned.
"""
return wrapper
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
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] | 2.592108 | 11,150 |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Copyright 2018-2019, Mingkun Huang
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from torch.autograd import Function, Variable
from torch.nn import Module
def forward_pass(log_probs, labels, blank):
"""
Computes probability of the forward variable alpha.
Args:
log_probs: Tensor of shape [T, U, V+1]
labels: Labels of shape [B, U]
blank: Index of the blank token.
Returns:
A tuple of the forward variable probabilities - alpha of shape [T, U]
and the log likelihood of this forward step.
"""
T, U, _ = log_probs.shape
alphas = np.zeros((T, U), dtype='f')
for t in range(1, T):
alphas[t, 0] = alphas[t - 1, 0] + log_probs[t - 1, 0, blank]
for u in range(1, U):
alphas[0, u] = alphas[0, u - 1] + log_probs[0, u - 1, labels[u - 1]]
for t in range(1, T):
for u in range(1, U):
no_emit = alphas[t - 1, u] + log_probs[t - 1, u, blank]
emit = alphas[t, u - 1] + log_probs[t, u - 1, labels[u - 1]]
alphas[t, u] = np.logaddexp(emit, no_emit)
loglike = alphas[T - 1, U - 1] + log_probs[T - 1, U - 1, blank]
return alphas, loglike
def backward_pass(log_probs, labels, blank):
"""
Computes probability of the backward variable beta.
Args:
log_probs: Tensor of shape [T, U, V+1]
labels: Labels of shape [B, U]
blank: Index of the blank token.
Returns:
A tuple of the backward variable probabilities - beta of shape [T, U]
and the log likelihood of this backward step.
"""
T, U, _ = log_probs.shape
betas = np.zeros((T, U), dtype='f')
betas[T - 1, U - 1] = log_probs[T - 1, U - 1, blank]
for t in reversed(range(T - 1)):
betas[t, U - 1] = betas[t + 1, U - 1] + log_probs[t, U - 1, blank]
for u in reversed(range(U - 1)):
betas[T - 1, u] = betas[T - 1, u + 1] + log_probs[T - 1, u, labels[u]]
for t in reversed(range(T - 1)):
for u in reversed(range(U - 1)):
no_emit = betas[t + 1, u] + log_probs[t, u, blank]
emit = betas[t, u + 1] + log_probs[t, u, labels[u]]
betas[t, u] = np.logaddexp(emit, no_emit)
return betas, betas[0, 0]
def compute_gradient(log_probs, alphas, betas, labels, blank, fastemit_lambda):
"""
Computes the gradients of the log_probs with respect to the log probability of this step occuring.
Args:
Args:
log_probs: Tensor of shape [T, U, V+1]
alphas: Tensor of shape [T, U] which represents the forward variable.
betas: Tensor of shape [T, U] which represents the backward variable.
labels: Labels of shape [B, U]
blank: Index of the blank token.
Returns:
Gradients of shape [T, U, V+1] with respect to the forward log probability
"""
T, U, _ = log_probs.shape
grads = np.full(log_probs.shape, -float("inf"))
log_like = betas[0, 0] # == alphas[T - 1, U - 1] + betas[T - 1, U - 1]
# // grad to last blank transition
grads[T - 1, U - 1, blank] = alphas[T - 1, U - 1]
grads[: T - 1, :, blank] = alphas[: T - 1, :] + betas[1:, :]
# // grad to label transition
for u, l in enumerate(labels):
grads[:, u, l] = alphas[:, u] + betas[:, u + 1]
grads = -np.exp(grads + log_probs - log_like)
if fastemit_lambda > 0.0:
for u, l in enumerate(labels):
grads[:, u, l] = (1.0 + fastemit_lambda) * grads[:, u, l]
return grads
def fastemit_regularization(log_probs, labels, alphas, betas, blank, fastemit_lambda):
"""
Describes the computation of FastEmit regularization from the paper -
[FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization](https://arxiv.org/abs/2010.11148)
Args:
log_probs: Tensor of shape [T, U, V+1]
labels: Unused. Labels of shape [B, U]
alphas: Tensor of shape [T, U] which represents the forward variable.
betas: Unused. Tensor of shape [T, U] which represents the backward variable.
blank: Index of the blank token.
fastemit_lambda: Float scaling factor for FastEmit regularization.
Returns:
The regularized negative log likelihood - lambda * P(At, u|x)
"""
# General calculation of the fastemit regularization alignments
T, U, _ = log_probs.shape
# alignment = np.zeros((T, U), dtype='float32')
#
# for t in range(0, T):
# alignment[t, U - 1] = alphas[t, U - 1] + betas[t, U - 1]
#
# for t in range(0, T):
# for u in range(0, U - 1):
# emit = alphas[t, u] + log_probs[t, u, labels[u]] + betas[t, u + 1]
# alignment[t, u] = emit
# reg = fastemit_lambda * (alignment[T - 1, U - 1])
# The above is equivalent to below, without need of computing above
# reg = fastemit_lambda * (alphas[T - 1, U - 1] + betas[T - 1, U - 1])
# The above is also equivalent to below, without need of computing the betas alignment matrix
reg = fastemit_lambda * (alphas[T - 1, U - 1] + log_probs[T - 1, U - 1, blank])
return -reg
def transduce(log_probs, labels, blank=0, fastemit_lambda=0.0):
"""
Args:
log_probs: 3D array with shape
[input len, output len + 1, vocab size]
labels: 1D array with shape [output time steps]
blank: Index of the blank token.
fastemit_lambda: Float scaling factor for FastEmit regularization.
Returns:
float: The negative log-likelihood
3D array: Gradients with respect to the
unnormalized input actications
2d arrays: Alphas matrix (TxU)
2d array: Betas matrix (TxU)
"""
alphas, ll_forward = forward_pass(log_probs, labels, blank)
betas, ll_backward = backward_pass(log_probs, labels, blank)
grads = compute_gradient(log_probs, alphas, betas, labels, blank, fastemit_lambda)
return -ll_forward, grads, alphas, betas
def transduce_batch(log_probs, labels, flen, glen, blank=0, fastemit_lambda=0.0):
"""
Compute the transducer loss of the batch.
Args:
log_probs: [B, T, U, V+1]. Activation matrix normalized with log-softmax.
labels: [B, U+1] - ground truth labels with <SOS> padded as blank token in the beginning.
flen: Length vector of the acoustic sequence.
glen: Length vector of the target sequence.
blank: Id of the blank token.
fastemit_lambda: Float scaling factor for FastEmit regularization.
Returns:
Batch of transducer forward log probabilities (loss) and the gradients of the activation matrix.
"""
grads = np.zeros_like(log_probs)
costs = []
for b in range(log_probs.shape[0]):
t = int(flen[b])
u = int(glen[b]) + 1
ll, g, alphas, betas = transduce(log_probs[b, :t, :u, :], labels[b, : u - 1], blank, fastemit_lambda)
grads[b, :t, :u, :] = g
reg = fastemit_regularization(
log_probs[b, :t, :u, :], labels[b, : u - 1], alphas, betas, blank, fastemit_lambda
)
ll += reg
costs.append(ll)
return costs, grads
if __name__ == '__main__':
loss = RNNTLoss(fastemit_lambda=0.01)
torch.manual_seed(0)
acts = torch.randn(1, 2, 5, 3)
labels = torch.tensor([[0, 2, 1, 2]], dtype=torch.int32)
act_lens = torch.tensor([2], dtype=torch.int32)
label_lens = torch.tensor([len(labels[0])], dtype=torch.int32)
loss_val = loss(acts, labels, act_lens, label_lens)
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62,
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13,
83,
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11925,
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4357,
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11,
719,
62,
75,
641,
11,
6167,
62,
75,
641,
8,
198
] | 2.423562 | 3,565 |
import numpy as np
import math
from dataset_specifications.dataset import Dataset
| [
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] | 3.4 | 25 |
#!/usr/bin/env python
import time
from package.oss.cov_json import get_json_report
from package.oss.init import initialization
from package.tool.summarize_jsons import summarize_jsons
from package.util.setting import TestPlatform
if __name__ == "__main__":
report_coverage()
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] | 3.252874 | 87 |
import numpy as np
from sklearn.linear_model import LogisticRegression
from .models import User
from .twitter import vectorize_tweet
def predict_user(user1_name, user2_name, tweet_text):
"""
Determine and return which user is more likely to say a given Tweet.
Example: predict_user('ausen', 'elonmusk', 'Lambda School Rocks!')
Returns 1 corresponding to 1st user passed in, or 0 for second.
"""
user1 = User.query.filter(User.name == user1_name).one()
user2 = User.query.filter(User.name == user2_name).one()
user1_vect = np.array([tweet.vect for tweet in user1.tweets])
user2_vect = np.array([tweet.vect for tweet in user2.tweets])
vects = np.vstack([user1_vect, user2_vect])
labels = np.concatenate([np.ones(len(user1.tweets)),
np.zeros(len(user2.tweets))])
log_reg = LogisticRegression().fit(vects, labels)
# We've done the model fitting, now to predict...
hypo_tweet_vect = vectorize_tweet(tweet_text)
return log_reg.predict(np.array(hypo_tweet_vect).reshape(1,-1))
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62,
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310,
737,
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1758,
7,
16,
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16,
4008,
198
] | 2.477958 | 431 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# This file is part of groupthink.
# https://github.com/emanuelfeld/groupthink
# This project is in the public domain within the United States.
# Additionally, the Government of the District of Columbia waives
# copyright and related rights in the work worldwide through the CC0 1.0
# Universal public domain dedication.
__version__ = '1.0.0' # NOQA
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15,
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6,
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1303,
8005,
48,
32,
198
] | 3.398305 | 118 |
# sys
import os
import sys
import numpy as np
import random
import pickle
import json
# torch
import torch
import torch.nn as nn
from torchvision import datasets, transforms
# operation
from . import tools
| [
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] | 3.714286 | 56 |
# Generated by Django 4.0.2 on 2022-03-02 03:29
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
| [
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] | 3.019231 | 52 |
import os
import subprocess
import time
import grpc
import tests.rpc.proto.schema_registry_pb2 as pb2
import tests.rpc.proto.schema_registry_pb2_grpc as pb2_grpc
from tests.common.postgres import PostgresConfig
EXE = os.getenv('SCHEMA_REGISTRY_EXE') or 'schema-registry'
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] | 2.584906 | 106 |
"""
When a routing policy is set with an empty condition, it should be loaded correctly and should route all
the requests to a correct backend.
"""
from urllib.parse import urlparse
import pytest
from packaging.version import Version # noqa # pylint: disable=unused-import
from testsuite import TESTED_VERSION, rawobj # noqa # pylint: disable=unused-import
from testsuite.echoed_request import EchoedRequest
pytestmark = [
pytest.mark.skipif("TESTED_VERSION < Version('2.11')"),
pytest.mark.issue("https://issues.redhat.com/browse/THREESCALE-6415")]
def test_routing_policy_without_header(api_client, private_base_url):
"""
Sends a request and asserts, that the routing policy is active and the
requests is routed to the correct backend (httpbin)
"""
parsed_url = urlparse(private_base_url("httpbin"))
response = api_client().get("/get")
assert response.status_code == 200
echoed_request = EchoedRequest.create(response)
assert echoed_request.headers["Host"] == parsed_url.hostname
| [
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14,
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198,
220,
220,
220,
6818,
2882,
13,
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62,
8189,
6624,
939,
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220,
220,
220,
22211,
62,
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796,
21455,
276,
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13,
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7,
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8,
198,
220,
220,
220,
6818,
22211,
62,
25927,
13,
50145,
14692,
17932,
8973,
6624,
44267,
62,
6371,
13,
4774,
3672,
198
] | 3.145897 | 329 |
# Copyright (c) 2014 Mirantis Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from oslo.config import cfg
import oslo.messaging
from ceilometer import plugin
from ceilometer import sample
OPTS = [
cfg.StrOpt('sahara_control_exchange',
default='sahara',
help="Exchange name for Data Processing notifications."),
]
cfg.CONF.register_opts(OPTS)
SERVICE = 'sahara'
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] | 3.190141 | 284 |
import os
import time
from selenium.webdriver.support.select import Select
from Data.parameters import Data
from get_dir import pwd
from reuse_func import GetData
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] | 3.574468 | 47 |
import contextlib
import ctypes
import json
import os
import shutil
import struct
import subprocess
import sys
import tempfile
from datetime import datetime, timedelta, timezone
from enum import Enum, auto
from hashlib import pbkdf2_hmac
from .aes import (
aes_cbc_decrypt_bytes,
aes_gcm_decrypt_and_verify_bytes,
unpad_pkcs7,
)
from .compat import compat_b64decode, compat_cookiejar_Cookie
from .minicurses import MultilinePrinter, QuietMultilinePrinter
from .utils import Popen, YoutubeDLCookieJar, error_to_str, expand_path
try:
import sqlite3
SQLITE_AVAILABLE = True
except ImportError:
# although sqlite3 is part of the standard library, it is possible to compile python without
# sqlite support. See: https://github.com/yt-dlp/yt-dlp/issues/544
SQLITE_AVAILABLE = False
try:
import secretstorage
SECRETSTORAGE_AVAILABLE = True
except ImportError:
SECRETSTORAGE_AVAILABLE = False
SECRETSTORAGE_UNAVAILABLE_REASON = (
'as the `secretstorage` module is not installed. '
'Please install by running `python3 -m pip install secretstorage`.')
except Exception as _err:
SECRETSTORAGE_AVAILABLE = False
SECRETSTORAGE_UNAVAILABLE_REASON = f'as the `secretstorage` module could not be initialized. {_err}'
CHROMIUM_BASED_BROWSERS = {'brave', 'chrome', 'chromium', 'edge', 'opera', 'vivaldi'}
SUPPORTED_BROWSERS = CHROMIUM_BASED_BROWSERS | {'firefox', 'safari'}
class MacChromeCookieDecryptor(ChromeCookieDecryptor):
def decrypt(self, encrypted_value):
version = encrypted_value[:3]
ciphertext = encrypted_value[3:]
if version == b'v10':
self._cookie_counts['v10'] += 1
if self._v10_key is None:
self._logger.warning('cannot decrypt v10 cookies: no key found', only_once=True)
return None
return _decrypt_aes_cbc(ciphertext, self._v10_key, self._logger)
else:
self._cookie_counts['other'] += 1
# other prefixes are considered 'old data' which were stored as plaintext
# https://chromium.googlesource.com/chromium/src/+/refs/heads/main/components/os_crypt/os_crypt_mac.mm
return encrypted_value
class WindowsChromeCookieDecryptor(ChromeCookieDecryptor):
def _extract_safari_cookies(profile, logger):
if profile is not None:
logger.error('safari does not support profiles')
if sys.platform != 'darwin':
raise ValueError(f'unsupported platform: {sys.platform}')
cookies_path = os.path.expanduser('~/Library/Cookies/Cookies.binarycookies')
if not os.path.isfile(cookies_path):
logger.debug('Trying secondary cookie location')
cookies_path = os.path.expanduser('~/Library/Containers/com.apple.Safari/Data/Library/Cookies/Cookies.binarycookies')
if not os.path.isfile(cookies_path):
raise FileNotFoundError('could not find safari cookies database')
with open(cookies_path, 'rb') as f:
cookies_data = f.read()
jar = parse_safari_cookies(cookies_data, logger=logger)
logger.info(f'Extracted {len(jar)} cookies from safari')
return jar
class ParserError(Exception):
pass
class DataParser:
def __init__(self, data, logger):
self._data = data
self.cursor = 0
self._logger = logger
def parse_safari_cookies(data, jar=None, logger=YDLLogger()):
"""
References:
- https://github.com/libyal/dtformats/blob/main/documentation/Safari%20Cookies.asciidoc
- this data appears to be out of date but the important parts of the database structure is the same
- there are a few bytes here and there which are skipped during parsing
"""
if jar is None:
jar = YoutubeDLCookieJar()
page_sizes, body_start = _parse_safari_cookies_header(data, logger)
p = DataParser(data[body_start:], logger)
for page_size in page_sizes:
_parse_safari_cookies_page(p.read_bytes(page_size), jar, logger)
p.skip_to_end('footer')
return jar
SUPPORTED_KEYRINGS = _LinuxKeyring.__members__.keys()
def _get_linux_desktop_environment(env):
"""
https://chromium.googlesource.com/chromium/src/+/refs/heads/main/base/nix/xdg_util.cc
GetDesktopEnvironment
"""
xdg_current_desktop = env.get('XDG_CURRENT_DESKTOP', None)
desktop_session = env.get('DESKTOP_SESSION', None)
if xdg_current_desktop is not None:
xdg_current_desktop = xdg_current_desktop.split(':')[0].strip()
if xdg_current_desktop == 'Unity':
if desktop_session is not None and 'gnome-fallback' in desktop_session:
return _LinuxDesktopEnvironment.GNOME
else:
return _LinuxDesktopEnvironment.UNITY
elif xdg_current_desktop == 'GNOME':
return _LinuxDesktopEnvironment.GNOME
elif xdg_current_desktop == 'X-Cinnamon':
return _LinuxDesktopEnvironment.CINNAMON
elif xdg_current_desktop == 'KDE':
return _LinuxDesktopEnvironment.KDE
elif xdg_current_desktop == 'Pantheon':
return _LinuxDesktopEnvironment.PANTHEON
elif xdg_current_desktop == 'XFCE':
return _LinuxDesktopEnvironment.XFCE
elif desktop_session is not None:
if desktop_session in ('mate', 'gnome'):
return _LinuxDesktopEnvironment.GNOME
elif 'kde' in desktop_session:
return _LinuxDesktopEnvironment.KDE
elif 'xfce' in desktop_session:
return _LinuxDesktopEnvironment.XFCE
else:
if 'GNOME_DESKTOP_SESSION_ID' in env:
return _LinuxDesktopEnvironment.GNOME
elif 'KDE_FULL_SESSION' in env:
return _LinuxDesktopEnvironment.KDE
return _LinuxDesktopEnvironment.OTHER
def _choose_linux_keyring(logger):
"""
https://chromium.googlesource.com/chromium/src/+/refs/heads/main/components/os_crypt/key_storage_util_linux.cc
SelectBackend
"""
desktop_environment = _get_linux_desktop_environment(os.environ)
logger.debug(f'detected desktop environment: {desktop_environment.name}')
if desktop_environment == _LinuxDesktopEnvironment.KDE:
linux_keyring = _LinuxKeyring.KWALLET
elif desktop_environment == _LinuxDesktopEnvironment.OTHER:
linux_keyring = _LinuxKeyring.BASICTEXT
else:
linux_keyring = _LinuxKeyring.GNOMEKEYRING
return linux_keyring
def _get_kwallet_network_wallet(logger):
""" The name of the wallet used to store network passwords.
https://chromium.googlesource.com/chromium/src/+/refs/heads/main/components/os_crypt/kwallet_dbus.cc
KWalletDBus::NetworkWallet
which does a dbus call to the following function:
https://api.kde.org/frameworks/kwallet/html/classKWallet_1_1Wallet.html
Wallet::NetworkWallet
"""
default_wallet = 'kdewallet'
try:
proc = Popen([
'dbus-send', '--session', '--print-reply=literal',
'--dest=org.kde.kwalletd5',
'/modules/kwalletd5',
'org.kde.KWallet.networkWallet'
], stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
stdout, stderr = proc.communicate_or_kill()
if proc.returncode != 0:
logger.warning('failed to read NetworkWallet')
return default_wallet
else:
network_wallet = stdout.decode('utf-8').strip()
logger.debug(f'NetworkWallet = "{network_wallet}"')
return network_wallet
except Exception as e:
logger.warning(f'exception while obtaining NetworkWallet: {e}')
return default_wallet
def _decrypt_windows_dpapi(ciphertext, logger):
"""
References:
- https://docs.microsoft.com/en-us/windows/win32/api/dpapi/nf-dpapi-cryptunprotectdata
"""
from ctypes.wintypes import DWORD
buffer = ctypes.create_string_buffer(ciphertext)
blob_in = DATA_BLOB(ctypes.sizeof(buffer), buffer)
blob_out = DATA_BLOB()
ret = ctypes.windll.crypt32.CryptUnprotectData(
ctypes.byref(blob_in), # pDataIn
None, # ppszDataDescr: human readable description of pDataIn
None, # pOptionalEntropy: salt?
None, # pvReserved: must be NULL
None, # pPromptStruct: information about prompts to display
0, # dwFlags
ctypes.byref(blob_out) # pDataOut
)
if not ret:
logger.warning('failed to decrypt with DPAPI', only_once=True)
return None
result = ctypes.string_at(blob_out.pbData, blob_out.cbData)
ctypes.windll.kernel32.LocalFree(blob_out.pbData)
return result
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] | 2.44234 | 3,538 |
import os
import pytest
from hgtools import managers
def touch(filename):
with open(filename, 'a'):
pass
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] | 2.659574 | 47 |
#coding:utf8
'''
Created on 2013-8-21
@author: lan (www.9miao.com)
'''
import itertools
import datetime
def safeunicode(obj, encoding='utf-8'):
r"""
Converts any given object to unicode string.
>>> safeunicode('hello')
u'hello'
>>> safeunicode(2)
u'2'
>>> safeunicode('\xe1\x88\xb4')
u'\u1234'
"""
t = type(obj)
if t is unicode:
return obj
elif t is str:
return obj.decode(encoding)
elif t in [int, float, bool]:
return unicode(obj)
elif hasattr(obj, '__unicode__') or isinstance(obj, unicode):
return unicode(obj)
else:
return str(obj).decode(encoding)
def safestr(obj, encoding='utf-8'):
r"""
Converts any given object to utf-8 encoded string.
>>> safestr('hello')
'hello'
>>> safestr(u'\u1234')
'\xe1\x88\xb4'
>>> safestr(2)
'2'
"""
if isinstance(obj, unicode):
return obj.encode(encoding)
elif isinstance(obj, str):
return obj
elif hasattr(obj, 'next'): # iterator
return itertools.imap(safestr, obj)
else:
return str(obj)
def sqlify(obj):
"""
converts `obj` to its proper SQL version
>>> sqlify(None)
'NULL'
>>> sqlify(True)
"'t'"
>>> sqlify(3)
'3'
"""
# because `1 == True and hash(1) == hash(True)`
# we have to do this the hard way...
if obj is None:
return 'NULL'
elif obj is True:
return "'t'"
elif obj is False:
return "'f'"
elif datetime and isinstance(obj, datetime.datetime):
return repr(obj.isoformat())
else:
if isinstance(obj, unicode): obj = obj.encode('utf8')
return repr(obj)
def sqllist(lst):
"""
Converts the arguments for use in something like a WHERE clause.
>>> sqllist(['a', 'b'])
'a, b'
>>> sqllist('a')
'a'
>>> sqllist(u'abc')
u'abc'
"""
if isinstance(lst, basestring):
return lst
else:
return ', '.join(lst)
def _sqllist(values):
"""
>>> _sqllist([1, 2, 3])
<sql: '(1, 2, 3)'>
"""
items = []
items.append('(')
for i, v in enumerate(values):
if i != 0:
items.append(', ')
items.append(sqlparam(v))
items.append(')')
return SQLQuery(items)
def sqlquote(a):
"""
Ensures `a` is quoted properly for use in a SQL query.
>>> 'WHERE x = ' + sqlquote(True) + ' AND y = ' + sqlquote(3)
<sql: "WHERE x = 't' AND y = 3">
>>> 'WHERE x = ' + sqlquote(True) + ' AND y IN ' + sqlquote([2, 3])
<sql: "WHERE x = 't' AND y IN (2, 3)">
"""
if isinstance(a, list):
return _sqllist(a)
else:
return sqlparam(a).sqlquery()
def _interpolate(sformat):
"""
Takes a format string and returns a list of 2-tuples of the form
(boolean, string) where boolean says whether string should be evaled
or not.
from <http://lfw.org/python/Itpl.py> (public domain, Ka-Ping Yee)
"""
from tokenize import tokenprog
tokenprog = tokenprog
namechars = "abcdefghijklmnopqrstuvwxyz" \
"ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_";
chunks = []
pos = 0
while 1:
dollar = sformat.find("$", pos)
if dollar < 0:
break
nextchar = sformat[dollar + 1]
if nextchar == "{":
chunks.append((0, sformat[pos:dollar]))
pos, level = dollar + 2, 1
while level:
match, pos = matchorfail(sformat, pos)
tstart, tend = match.regs[3]
token = sformat[tstart:tend]
if token == "{":
level = level + 1
elif token == "}":
level = level - 1
chunks.append((1, sformat[dollar + 2:pos - 1]))
elif nextchar in namechars:
chunks.append((0, sformat[pos:dollar]))
match, pos = matchorfail(sformat, dollar + 1)
while pos < len(sformat):
if sformat[pos] == "." and \
pos + 1 < len(sformat) and sformat[pos + 1] in namechars:
match, pos = matchorfail(sformat, pos + 1)
elif sformat[pos] in "([":
pos, level = pos + 1, 1
while level:
match, pos = matchorfail(sformat, pos)
tstart, tend = match.regs[3]
token = sformat[tstart:tend]
if token[0] in "([":
level = level + 1
elif token[0] in ")]":
level = level - 1
else:
break
chunks.append((1, sformat[dollar + 1:pos]))
else:
chunks.append((0, sformat[pos:dollar + 1]))
pos = dollar + 1 + (nextchar == "$")
if pos < len(sformat):
chunks.append((0, sformat[pos:]))
return chunks
def sqlwhere(dictionary, grouping=' AND '):
"""
Converts a `dictionary` to an SQL WHERE clause `SQLQuery`.
>>> sqlwhere({'cust_id': 2, 'order_id':3})
<sql: 'order_id = 3 AND cust_id = 2'>
>>> sqlwhere({'cust_id': 2, 'order_id':3}, grouping=', ')
<sql: 'order_id = 3, cust_id = 2'>
>>> sqlwhere({'a': 'a', 'b': 'b'}).query()
'a = %s AND b = %s'
"""
return SQLQuery.join([k + ' = ' + sqlparam(v) for k, v in dictionary.items()], grouping)
def reparam(string_, dictionary):
"""
Takes a string and a dictionary and interpolates the string
using values from the dictionary. Returns an `SQLQuery` for the result.
>>> reparam("s = $s", dict(s=True))
<sql: "s = 't'">
>>> reparam("s IN $s", dict(s=[1, 2]))
<sql: 's IN (1, 2)'>
"""
dictionary = dictionary.copy() # eval mucks with it
result = []
for live, chunk in _interpolate(string_):
if live:
v = eval(chunk, dictionary)
result.append(sqlquote(v))
else:
result.append(chunk)
return SQLQuery.join(result, '')
sqlparam = SQLParam
sqlproducer = SQLProducer()
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"""
Django settings for dddppp project.
Generated by 'django-admin startproject' using Django 1.8.2.
For more information on this file, see
https://docs.djangoproject.com/en/1.8/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/1.8/ref/settings/
"""
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
import os
import pkg_resources
import pwd
PROJECT_NAME = 'dddppp'
# Enforce a valid POSIX environment
# Get missing environment variables via call to pwd.getpwuid(...)
_PW_CACHE = None
_PW_MAP = {
'LOGNAME': 'pw_name',
'USER': 'pw_name',
'USERNAME': 'pw_name',
'UID': 'pw_uid',
'GID': 'pw_gid',
'HOME': 'pw_dir',
'SHELL': 'pw_shell',
}
for _missing_env in set(_PW_MAP).difference(os.environ):
if _PW_CACHE is None:
_PW_CACHE = pwd.getpwuid(os.getuid())
os.environ[_missing_env] = str(getattr(_PW_CACHE, _PW_MAP[_missing_env]))
del _PW_CACHE, _PW_MAP, pwd
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/1.8/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'nfd_lvt=&k#h#$a^_l09j#5%s=mg+0aw=@t84ry$&rps43c33+'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = [
'localhost',
]
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'dddp',
'dddp.server',
'dddp.accounts',
'dddppp.slides',
]
for (requirement, pth) in [
('django-extensions', 'django_extensions'),
]:
try:
pkg_resources.get_distribution(requirement)
except (
pkg_resources.DistributionNotFound,
pkg_resources.VersionConflict,
):
continue
INSTALLED_APPS.append(pth)
MIDDLEWARE_CLASSES = [
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.auth.middleware.SessionAuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
#'django.middleware.security.SecurityMiddleware',
]
ROOT_URLCONF = 'dddppp.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'dddppp.wsgi.application'
# Database
# https://docs.djangoproject.com/en/1.8/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql_psycopg2',
'NAME': os.environ.get('PGDATABASE', PROJECT_NAME),
'USER': os.environ.get('PGUSER', os.environ['LOGNAME']),
'PASSWORD': os.environ.get('DJANGO_DATABASE_PASSWORD', ''),
'HOST': os.environ.get('PGHOST', ''),
'PORT': os.environ.get('PGPORT', ''),
}
}
# Internationalization
# https://docs.djangoproject.com/en/1.8/topics/i18n/
LANGUAGE_CODE = 'en-au'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/1.8/howto/static-files/
STATIC_URL = '/static/'
STATIC_ROOT = os.path.join(BASE_DIR, 'static')
STATICFILES_STORAGE = 'whitenoise.django.GzipManifestStaticFilesStorage'
# django-secure
# see: https://github.com/carljm/django-secure/ for more options
SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')
#SECURE_SSL_REDIRECT = True
SECURE_CONTENT_TYPE_NOSNIFF = True
SECURE_FRAME_DENY = True
SESSION_COOKIE_SECURE = True
SESSION_COOKIE_HTTPONLY = True
DDDPPP_CONTENT_TYPES = []
PROJ_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
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] | 2.267116 | 1,928 |
#!/usr/bin/env python
from setuptools import setup
import os
__doc__ = """
Command line tool and library wrappers around iwlist and
/etc/network/interfaces.
"""
install_requires = [
'setuptools',
'pbkdf2',
]
try:
import argparse
except:
install_requires.append('argparse')
version = '1.0.0'
setup(
name='wifi',
version=version,
author='Rocky Meza, Gavin Wahl',
author_email='[email protected]',
description=__doc__,
long_description=read('README.rst'),
packages=['wifi'],
scripts=['bin/wifi'],
test_suite='tests',
platforms=["Debian"],
license='BSD',
install_requires=install_requires,
classifiers=[
"License :: OSI Approved :: BSD License",
"Topic :: System :: Networking",
"Operating System :: POSIX :: Linux",
"Environment :: Console",
"Programming Language :: Python",
"Programming Language :: Python :: 2.6",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3.3",
],
data_files=[
('/etc/bash_completion.d/', ['extras/wifi-completion.bash']),
]
)
| [
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] | 2.479303 | 459 |
# Copyright 2019 Province of British Columbia
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""API endpoints for managing an Org resource."""
from flask import request
from flask_restplus import Namespace, Resource, cors
from auth_api import status as http_status
from auth_api.exceptions import BusinessException
from auth_api.jwt_wrapper import JWTWrapper
from auth_api.schemas import ProductSubscriptionSchema
from auth_api.schemas import utils as schema_utils
from auth_api.services import Product as ProductService
from auth_api.tracer import Tracer
from auth_api.utils.roles import Role
from auth_api.utils.util import cors_preflight
API = Namespace('products', description='Endpoints for products management')
TRACER = Tracer.get_instance()
_JWT = JWTWrapper.get_instance()
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62,
39098,
3419,
628
] | 3.698276 | 348 |
import logging
from collections import Counter
from django.core.management.base import BaseCommand
from django.db.models import Q
from TWLight.applications.models import Application
from TWLight.resources.models import Partner
from TWLight.applications.signals import Reminder
from TWLight.users.models import Editor
logger = logging.getLogger(__name__)
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8,
628
] | 3.83871 | 93 |
# pvtrace is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# pvtrace is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import numpy as np
from external.transformations import translation_matrix, rotation_matrix
import external.transformations as tf
from Trace import Photon
from Geometry import Box, Cylinder, FinitePlane, transform_point, transform_direction, rotation_matrix_from_vector_alignment, norm
from Materials import Spectrum
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] | 3.815261 | 249 |
"""Embedded DSL for assembling logic circuits.
Embedded domain-specific combinator library for
assembling abstract definitions of logic circuits
and synthesizing circuits from those definitions.
"""
from __future__ import annotations
from typing import Sequence
import doctest
from parts import parts
from circuit import op, gate, circuit, signature
def not_(self):
"""
>>> results = []
>>> for x in [0, 1]:
... bit.circuit(circuit())
... b = output(input(x).not_())
... results.append(int(b) == bit.circuit().evaluate([x])[0])
>>> all(results)
True
"""
return bit.operation(op.not_, self)
def __invert__(self):
"""
>>> results = []
>>> for x in [0, 1]:
... bit.circuit(circuit())
... b = output(~input(x))
... results.append(int(b) == bit.circuit().evaluate([x])[0])
>>> all(results)
True
"""
return bit.operation(op.not_, self)
def __rsub__(self, other):
"""
>>> results = []
>>> for x in [0, 1]:
... bit.circuit(circuit())
... b = output(1 - input(x))
... results.append(int(b) == bit.circuit().evaluate([x])[0])
>>> all(results)
True
>>> bit.circuit(circuit())
>>> 2 - input(0)
Traceback (most recent call last):
...
ValueError: can only subtract a bit from the integer 1
"""
if other == 1:
return bit.operation(op.not_, self)
raise ValueError('can only subtract a bit from the integer 1')
def and_(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).and_(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.and_, self, other)
def __and__(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x) & input(y))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.and_, self, other)
def __rand__(self, other):
"""
>>> bit.circuit(circuit())
>>> b = 0 & constant(1)
>>> b.value
0
"""
return self & (constant(other) if isinstance(other, int) else other)
def nimp(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).nimp(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.nimp_, self, other)
def nimp_(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).nimp_(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.nimp_, self, other)
def __gt__(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x) > input(y))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return self.nimp(other)
def nif(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).nif(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.nif_, self, other)
def nif_(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).nif_(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.nif_, self, other)
def __lt__(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x) < input(y))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return self.nif(other)
def xor(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).xor(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.xor_, self, other)
def xor_(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).xor_(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.xor_, self, other)
def __xor__(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x) ^ input(y))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.xor_, self, other)
def __rxor__(self, other):
"""
>>> bit.circuit(circuit())
>>> b = 1 ^ constant(0)
>>> b.value
1
"""
return self ^ (constant(other) if isinstance(other, int) else other)
def or_(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).or_(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.or_, self, other)
def __or__(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x) | input(y))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.or_, self, other)
def __ror__(self, other):
"""
>>> bit.circuit(circuit())
>>> b = 1 | constant(0)
>>> b.value
1
"""
return self | (constant(other) if isinstance(other, int) else other)
def nor(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).nor(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.nor_, self, other)
def nor_(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).nor_(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.nor_, self, other)
def __mod__(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x) % input(y))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.nor_, self, other)
def xnor(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).xnor(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.xnor_, self, other)
def xnor_(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).xnor_(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.xnor_, self, other)
def __eq__(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x) == input(y))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.xnor_, self, other)
def if_(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).if_(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.if_, self, other)
def __ge__(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x) >= input(y))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.if_, self, other)
def imp(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).imp(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.imp_, self, other)
def imp_(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).imp_(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.imp_, self, other)
def __le__(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x) <= input(y))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.imp_, self, other)
def nand(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).nand(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.nand_, self, other)
def nand_(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x).nand_(input(y)))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.nand_, self, other)
def __matmul__(self, other):
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... b = output(input(x) @ input(y))
... results.append(int(b) == bit.circuit().evaluate([x,y])[0])
>>> all(results)
True
"""
return bit.operation(op.nand_, self, other)
class constant(bit):
"""Bit that is designated as a constant input."""
class input(bit):
"""Bit that is designated as a variable input."""
class input_one(input):
"""Bit that is designated as a variable input from one source."""
class input_two(input):
"""Bit that is designated as a variable input from a second source."""
class output(bit):
"""
Bit that is designated an output.
>>> bit.circuit(circuit())
>>> b0 = output(input(1).not_())
>>> b1 = output(b0.not_())
>>> b2 = output(b0)
>>> [b0.value, b1.value, b2.value]
[0, 1, 0]
"""
class bits_type(int): # pylint: disable=R0903
"""
Class for representing an input or output type of a
function decorated for automated synthesis.
"""
class bits(list):
"""
Class for representing a vector of abstract bits.
"""
def __new__(cls, argument = None) -> bits:
"""
Return bits object given the supplied argument.
"""
return bits_type(argument)\
if isinstance(argument, int) else\
list.__new__(cls, argument)
def __int__(self: bits) -> int:
"""
>>> bit.circuit(circuit())
>>> xs = constants([0, 0, 0])
>>> ys = outputs(xs.not_())
>>> int(ys)
7
"""
return sum(int(b)*(2**i) for (i, b) in zip(range(len(self)), reversed(self)))
def not_(self: bits) -> bits:
"""
>>> results = []
>>> for x in [0, 1]:
... bit.circuit(circuit())
... xs = inputs([x, x, x])
... ys = outputs(xs.not_())
... ns = [int(y) for y in ys]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x]))
>>> all(results)
True
"""
return bits([x.not_() for x in self])
def __invert__(self: bits) -> bits:
"""
>>> results = []
>>> for x in [0, 1]:
... bit.circuit(circuit())
... xs = inputs([x, x, x])
... ys = outputs(~xs)
... ns = [int(y) for y in ys]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x]))
>>> all(results)
True
"""
return bits([x.not_() for x in self])
def and_(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.and_(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.and_(y) for (x, y) in zip(self, other)])
def __and__(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs & ys)
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.and_(y) for (x, y) in zip(self, other)])
def nimp(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.nimp(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.nimp_(y) for (x, y) in zip(self, other)])
def nimp_(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.nimp_(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.nimp_(y) for (x, y) in zip(self, other)])
def __gt__(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs > ys)
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.nimp_(y) for (x, y) in zip(self, other)])
def nif(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.nif(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.nif_(y) for (x, y) in zip(self, other)])
def nif_(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.nif_(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.nif_(y) for (x, y) in zip(self, other)])
def __lt__(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs < ys)
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.nif_(y) for (x, y) in zip(self, other)])
def xor(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.xor(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.xor_(y) for (x, y) in zip(self, other)])
def xor_(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.xor_(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.xor_(y) for (x, y) in zip(self, other)])
def __xor__(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs ^ ys)
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.xor_(y) for (x, y) in zip(self, other)])
def or_(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.or_(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.or_(y) for (x, y) in zip(self, other)])
def __or__(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs | ys)
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.or_(y) for (x, y) in zip(self, other)])
def nor(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.nor(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.nor_(y) for (x, y) in zip(self, other)])
def nor_(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.nor_(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.nor_(y) for (x, y) in zip(self, other)])
def __mod__(self, other) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs % ys)
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.nor_(y) for (x, y) in zip(self, other)])
def xnor(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.xnor(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.xnor_(y) for (x, y) in zip(self, other)])
def xnor_(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.xnor_(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.xnor_(y) for (x, y) in zip(self, other)])
def __eq__(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs == ys)
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.xnor_(y) for (x, y) in zip(self, other)])
def if_(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.if_(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.if_(y) for (x, y) in zip(self, other)])
def __ge__(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs >= ys)
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.if_(y) for (x, y) in zip(self, other)])
def imp(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.imp(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.imp_(y) for (x, y) in zip(self, other)])
def imp_(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.imp_(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.imp_(y) for (x, y) in zip(self, other)])
def __le__(self: bits, other: bits) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs <= ys)
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.imp_(y) for (x, y) in zip(self, other)])
def nand(self: bits, other) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.nand(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.nand_(y) for (x, y) in zip(self, other)])
def nand_(self: bits, other) -> bits:
"""
>>> results = []
>>> for (x, y) in [(0, 0), (0, 1), (1, 0), (1, 1)]:
... bit.circuit(circuit())
... (xs, ys) = (inputs([x, x, x]), inputs([y, y, y]))
... zs = outputs(xs.nand_(ys))
... ns = [int(z) for z in zs]
... c = bit.circuit()
... results.append(ns == c.evaluate([x, x, x, y, y, y]))
>>> all(results)
True
"""
return bits([x.nand_(y) for (x, y) in zip(self, other)])
def __rshift__(self: bits, other) -> bits:
"""
Overloaded operator: rotation and shift operations.
>>> bit.circuit(circuit())
>>> bs = bits(map(bit, [1,1,1,1,0,0,0,0]))
>>> bs = bs >> 3
>>> [b.value for b in bs]
[0, 0, 0, 1, 1, 1, 1, 0]
>>> bit.circuit(circuit())
>>> bs = bits(map(bit, [0,0,0,0,1,1,1,1]))
>>> bs = bs >> {3}
>>> [b.value for b in bs]
[1, 1, 1, 0, 0, 0, 0, 1]
"""
if isinstance(other, set) and isinstance(list(other)[0], int): # Rotation.
quantity = list(other)[0]
return bits(self[len(self)-quantity:]) ** bits(self[0:len(self)-quantity])
else: # Shift
return bits([constant(0)]*other) ** bits(self[0:len(self)-other])
def __lshift__(self: bits, other) -> bits:
"""
>>> bit.circuit(circuit())
>>> bs = bits(map(bit, [1,1,1,1,0,0,0,0]))
>>> bs = bs << 3
>>> [b.value for b in bs]
[1, 0, 0, 0, 0, 0, 0, 0]
"""
return bits(self[other:]) ** bits([constant(0) for _ in range(other)])
def __truediv__(self: bits, other) -> Sequence[bits]:
"""
>>> bit.circuit(circuit())
>>> bs = bits(map(bit, [1,1,1,1,0,0,0,0]))
>>> bss = list(bs / 2)
>>> ([b.value for b in bss[0]], [b.value for b in bss[1]])
([1, 1, 1, 1], [0, 0, 0, 0])
>>> bit.circuit(circuit())
>>> bs = bits(map(bit, [1,1,1,1,0,0,0,0]))
>>> bss = list(bs / {2})
>>> [[b.value for b in bs] for bs in bss]
[[1, 1], [1, 1], [0, 0], [0, 0]]
>>> bit.circuit(circuit())
>>> bs = bits(map(bit, [1,1,1,1,0,0,0,0]))
>>> bss = list(bs / [1, 3, 4])
>>> [[b.value for b in bs] for bs in bss]
[[1], [1, 1, 1], [0, 0, 0, 0]]
"""
if isinstance(other, list) and len(other) > 0 and isinstance(other[0], int):
return map(bits, parts(self, length=other)) # Sequence of lengths.
elif isinstance(other, set) and len(other) == 1 and isinstance(list(other)[0], int):
return self / (len(self)//list(other)[0]) # Parts of length `other`.
else:
return map(bits, parts(self, other)) # Number of parts is `other`.
def __add__(self: bits, other) -> bits:
"""Concatenation of bit vectors."""
result = list(self)
result.extend(list(other))
return bits(result)
def __pow__(self: bits, other) -> bits:
"""Concatenation of bit vectors."""
return self + other
def constants(l):
return bits(map(constant, l))
def inputs(l):
return bits(map(input, l))
def outputs(l):
return bits(map(output, l))
def synthesize(f):
"""
Decorator for automatically synthesizing a circuit from a
function that takes only `bit` and/or `bits` objects as its
arguments and returns an output of type `bit` or `bits`.
>>> @synthesize
... def equal(x: bit, y: bit) -> bit:
... return (x & y) | ((1 - x) & (1 - y))
>>> xys = [bits([x, y]) for x in (0, 1) for y in (0, 1)]
>>> [equal.circuit.evaluate(xy) for xy in xys]
[[1], [0], [0], [1]]
>>> @synthesize
... def conjunction(xy: bits(2)) -> bits(2):
... return (xy[0], xy[0] & xy[1])
>>> xys = [bits([x, y]) for x in (0, 1) for y in (0, 1)]
>>> [conjunction.circuit.evaluate(xy) for xy in xys]
[[0, 0], [0, 0], [1, 0], [1, 1]]
>>> @synthesize
... def equal(x, y):
... return x & y
Traceback (most recent call last):
...
RuntimeError: automated circuit synthesis failed
"""
# Functions for determining types/signature from
# the type annotation of the decorated function.
type_in = lambda a: input(0) if a is bit else inputs([0] * a)
type_out = lambda a: output if a is bit else outputs
# For forward-compatibility with PEP 563.
eval_ = lambda a: eval(a) if isinstance(a, str) else a # pylint: disable=W0123
try:
# Construct the circuit and add it to the function as an attribute.
bit.circuit(circuit())
args_in = {
k: type_in(eval_(a))
for (k, a) in f.__annotations__.items() if k != 'return'
}
type_out(eval_(f.__annotations__['return']))(f(**args_in))
f.circuit = bit.circuit()
except:
raise RuntimeError('automated circuit synthesis failed') from None
# Return the original function.
return f
if __name__ == "__main__":
doctest.testmod() # pragma: no cover
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import discord
import random
from datetime import datetime
import pandas as pd
import matplotlib.pyplot as plt
import csv
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4818,
8079,
201,
198,
11748,
19798,
292,
355,
279,
67,
201,
198,
11748,
2603,
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8019,
13,
9078,
29487,
355,
458,
83,
201,
198,
11748,
269,
21370,
201,
198,
201,
198
] | 3.023256 | 43 |
#!/usr/bin/env python3
import os
from setuptools import setup
setup(
name="fastnode2vec",
version="0.0.5",
author="Louis Abraham",
license="MIT",
author_email="[email protected]",
description="Fast implementation of node2vec",
long_description=read("README.md"),
long_description_content_type="text/markdown",
url="https://github.com/louisabraham/fastnode2vec",
packages=["fastnode2vec"],
install_requires=["numpy", "numba", "gensim", "click", "tqdm"],
python_requires=">=3.6",
entry_points={"console_scripts": ["fastnode2vec = fastnode2vec.cli:node2vec"]},
classifiers=["Topic :: Scientific/Engineering :: Artificial Intelligence"],
)
| [
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33221,
7904,
22060,
14,
13798,
1586,
7904,
35941,
9345,
33116,
198,
8,
198
] | 2.696154 | 260 |
import os
basedir = os.path.abspath(os.path.dirname(__file__))
config_by_name = dict(
dev=DevelopmentConfig,
test=TestingConfig,
prod=ProductionConfig
)
key = Config.SECRET_KEY
| [
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] | 2.545455 | 77 |
#!/usr/bin/env python
import pwd
from ansible.module_utils.basic import AnsibleModule
if __name__ == "__main__":
main()
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] | 2.73913 | 46 |
"""Methods for working with ontologies and the OLS."""
from urllib.parse import quote_plus
import requests
OLS_API_ROOT = "http://www.ebi.ac.uk/ols/api"
# Curie means something like CL:0000001
def _ontology_name(curie):
"""Get the name of the ontology from the curie, CL or UBERON for example."""
return curie.split(":")[0]
def _ontology_value(curie):
"""Get the id component of the curie, 0000001 from CL:0000001 for example."""
return curie.split(":")[1]
def _double_encode(url):
"""Double url encode a url. This is required by the OLS API."""
return quote_plus(quote_plus(url))
def _iri(curie):
"""Get the iri from a curie. This is a bit hopeful that they all map to purl.obolibrary.org"""
if _ontology_name(curie) == "EFO":
return f"http://www.ebi.ac.uk/efo/EFO_{_ontology_value(curie)}"
return f"http://purl.obolibrary.org/obo/{_ontology_name(curie)}_{_ontology_value(curie)}"
def _ontology_info_url(curie):
"""Get the to make a GET to to get information about an ontology term."""
# If the curie is empty, just return an empty string. This happens when there is no
# valid ontology value.
if not curie:
return ""
else:
return f"{OLS_API_ROOT}/ontologies/{_ontology_name(curie)}/terms/{_double_encode(_iri(curie))}"
def get_ontology_label(curie):
"""For a given curie like 'CL:1000413', get the label like 'endothelial cell of artery'"""
url = _ontology_info_url(curie)
if not url:
return ""
response = requests.get(url)
if not response.ok:
raise OntologyLookupError(
f"Curie {curie} lookup failed, got status code {response.status_code}: {response.text}"
)
return response.json()["label"]
def lookup_candidate_term(label, ontology="cl", method="select"):
"""Lookup candidate terms for a label. This is useful when there is an existing label in a
submitted dataset, and you want to find an appropriate ontology term.
Args:
label: the label to find ontology terms for
ontology: the ontology to search in, cl or uberon or efo for example
method: select or search. search provides much broader results
Returns:
list of (curie, label) tuples returned by OLS
"""
# using OLS REST API [https://www.ebi.ac.uk/ols/docs/api]
url = f"{OLS_API_ROOT}/{method}?q={quote_plus(label)}&ontology={ontology.lower()}"
response = requests.get(url)
if not response.ok:
raise OntologyLookupError(
f"Label {label} lookup failed, got status code {response.status_code}: {response.text}"
)
return [(r["obo_id"], r["label"]) for r in response.json()["response"]["docs"]]
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26209,
1,
7131,
1,
31628,
8973,
60,
198
] | 2.658489 | 1,019 |
# Copyright (c) 2020, DjaoDjin inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
# TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import logging, re
from collections import OrderedDict
from django.db.models import F
from django.http import Http404
from django.shortcuts import get_object_or_404
from extra_views.contrib.mixins import SearchableListMixin
from rest_framework import generics
from rest_framework.pagination import PageNumberPagination
from rest_framework import response as http
from ..compat import reverse
from ..mixins import MatrixMixin
from ..models import Answer, Matrix, EditableFilter
from ..utils import (get_account_model, get_account_serializer,
get_question_serializer)
from .serializers import EditableFilterSerializer, MatrixSerializer
LOGGER = logging.getLogger(__name__)
| [
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7,
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8,
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628,
628,
628,
628
] | 3.503448 | 580 |
from __future__ import print_function
import httplib2
import os
import sys
import pickle
from apiclient import discovery
from apiclient import errors
from oauth2client import client
from oauth2client import tools
from oauth2client.file import Storage
try:
import argparse
flags = argparse.ArgumentParser(parents=[tools.argparser]).parse_args()
except ImportError:
flags = None
# If modifying these scopes, delete your previously saved credentials
# at ~/.credentials/gmail-python-quickstart.json
SCOPES = 'https://www.googleapis.com/auth/gmail.labels'
CLIENT_SECRET_FILE = 'client_secret.json'
APPLICATION_NAME = 'Inbox Organize'
def get_credentials():
"""Gets valid user credentials from storage.
If nothing has been stored, or if the stored credentials are invalid,
the OAuth2 flow is completed to obtain the new credentials.
Returns:
Credentials, the obtained credential.
"""
home_dir = os.path.expanduser('~')
credential_dir = os.path.join(home_dir, '.credentials')
if not os.path.exists(credential_dir):
os.makedirs(credential_dir)
credential_path = os.path.join(credential_dir,
'gmail-python-quickstart.json')
store = Storage(credential_path)
credentials = store.get()
if not credentials or credentials.invalid:
flow = client.flow_from_clientsecrets(CLIENT_SECRET_FILE, SCOPES)
flow.user_agent = APPLICATION_NAME
if flags:
credentials = tools.run_flow(flow, store, flags)
else: # Needed only for compatibility with Python 2.6
credentials = tools.run(flow, store)
print('Storing credentials to ' + credential_path)
return credentials
if __name__ == '__main__':
main()
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] | 2.94148 | 581 |
import numpy as np
import sys
## ROCKSTAR ##
halostruct1 = np.dtype([('id',np.int64),
('pos',np.float32,(6,)),
('corevel',np.float32,(3,)),
('bulkvel',np.float32,(3,)),
('m',np.float32),
('r',np.float32),
('child_r',np.float32),
('vmax_r',np.float32),
('mgrav',np.float32),
('vmax',np.float32),
('rvmax',np.float32),
('rs',np.float32),
('klypin_rs',np.float32),
('vrms',np.float32),
('J',np.float32,(3,)),
('energy',np.float32),
('spin',np.float32),
('alt_m',np.float32,(4,)),
('Xoff',np.float32),
('Voff',np.float32),
('b_to_a',np.float32),
('c_to_a',np.float32),
('A',np.float32,(3,)),
('b_to_a2',np.float32),
('c_to_a2',np.float32),
('A2',np.float32,(3,)),
('bullock_spin',np.float32),
('kin_to_pot',np.float32),
('m_pe_b',np.float32),
('m_pe_d',np.float32),
('dummy1',np.float32), ## ALIGNMENT
('num_p',np.int64),
('num_child_particles',np.int64),
('p_start',np.int64),
('desc',np.int64),
('flags',np.int64),
('n_core',np.int64),
('dummy2',np.float32), ## ALIGNMENT
('min_pos_err',np.float32),
('min_vel_err',np.float32),
('min_bulkvel_err',np.float32)
])
halostruct2 = np.dtype([('id',np.int64),
('pos',np.float32,(6,)),
('corevel',np.float32,(3,)),
('bulkvel',np.float32,(3,)),
('m',np.float32),
('r',np.float32),
('child_r',np.float32),
('vmax_r',np.float32),
('mgrav',np.float32),
('vmax',np.float32),
('rvmax',np.float32),
('rs',np.float32),
('klypin_rs',np.float32),
('vrms',np.float32),
('J',np.float32,(3,)),
('energy',np.float32),
('spin',np.float32),
('alt_m',np.float32,(4,)),
('Xoff',np.float32),
('Voff',np.float32),
('b_to_a',np.float32),
('c_to_a',np.float32),
('A',np.float32,(3,)),
('b_to_a2',np.float32),
('c_to_a2',np.float32),
('A2',np.float32,(3,)),
('bullock_spin',np.float32),
('kin_to_pot',np.float32),
('m_pe_b',np.float32),
('m_pe_d',np.float32),
('halfmass_radius',np.float32),
#('dummy1',np.float32), ## ALIGNMENT
('num_p',np.int64),
('num_child_particles',np.int64),
('p_start',np.int64),
('desc',np.int64),
('flags',np.int64),
('n_core',np.int64),
('dummy2',np.float32), ## ALIGNMENT
('min_pos_err',np.float32),
('min_vel_err',np.float32),
('min_bulkvel_err',np.float32)
])
## ROCKSTAR-GALAXIES ##
halogalaxystruct1 = np.dtype([('id',np.int64),
('pos',np.float32,(6,)),
('corevel',np.float32,(3,)),
('bulkvel',np.float32,(3,)),
('m',np.float32),
('r',np.float32),
('child_r',np.float32),
('vmax_r',np.float32),
('mgrav',np.float32),
('vmax',np.float32),
('rvmax',np.float32),
('rs',np.float32),
('klypin_rs',np.float32),
('vrms',np.float32),
('J',np.float32,(3,)),
('energy',np.float32),
('spin',np.float32),
('alt_m',np.float32,(4,)),
('Xoff',np.float32),
('Voff',np.float32),
('b_to_a',np.float32),
('c_to_a',np.float32),
('A',np.float32,(3,)),
('b_to_a2',np.float32),
('c_to_a2',np.float32),
('A2',np.float32,(3,)),
('bullock_spin',np.float32),
('kin_to_pot',np.float32),
('m_pe_b',np.float32),
('m_pe_d',np.float32),
('dummy1',np.float32), ## ALIGNMENT
('num_p',np.int64),
('num_child_particles',np.int64),
('p_start',np.int64),
('desc',np.int64),
('flags',np.int64),
('n_core',np.int64),
('dummy2',np.float32), ## ALIGNMENT
('min_pos_err',np.float32),
('min_vel_err',np.float32),
('min_bulkvel_err',np.float32),
('type',np.int32),
('sm',np.float32),
('gas',np.float32),
('bh',np.float32),
('peak_density',np.float32),
('av_density',np.float32),
])
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] | 1.367942 | 4,941 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""ics2entropiawiki
Read an ics file with the entropia events and insert them in to the
entropia homepage wiki.
Example:
$ ics2entropiawiki.py --config /etc/ics2entropiawiki/config.ini
Inserts events not in the past to the "Termine" Wiki page and appends past
events to the "Vergangene_Termine" Site
"""
import locale
import configparser
import re
import requests
from argparse import ArgumentParser
from datetime import timedelta, datetime
from ics import Calendar
from mwclient import Site
from dateutil.tz import tzlocal
BOTWARNING = """
<!--
This text is automatically generated by the ics2entropiawiki bot, everything you write and everything you edit
WILL BE OVERWRITTEN
Dieser Text ist vom ics2entropiawiki bot automatisch generiert. Alles was hier manuell editiert, hinzugefgt wird
WIRD BERSCHRIEBEN
-->
"""
TABLE_HEADER = """
{| class="termine" border="1" cellspacing="0" cellpadding="5" width="100%" style="border-collapse:collapse;"
! style="width:250px;" | Datum !! style="width:50px;" | Zeit !! Ort !! Beschreibung\
"""
ARCHIVE_TABLE_HEADER = """
{| class="termine" border="1" cellspacing="0" cellpadding="5" style="border-collapse:collapse;" width="100%"
|width=15%|'''Datum'''
|width=6%|'''Zeit'''
|width=15%|'''Ort'''
|width=69%|'''Beschreibung'''
"""
TABLE_FOOTER = (
"|}",
"\n",
"Weitere Links: [[Vorlage:Termine|Termine]] ",
"([https://entropia.de/index.php?title=Vorlage:Termine&action=edit Bearbeiten]),",
" [[Vorlage:Vergangene_Termine|Vergangene Termine]], [[Anfahrt]]"
)
LINE_SEPARATOR = "|-\n"
try:
locale.setlocale(locale.LC_ALL, 'de_DE.utf8')
except locale.Error:
pass
def append_past_events(past_events, wiki_user, wiki_pw, wiki_archive):
"""
Append the "new" past events to the wiki archive page
:param past_events: the past events that were not added to the events page
:type past_events: list
:param wiki_user: bot user for the wiki
:type wiki_user: str
:param wiki_pw: password for the wiki user
:type wiki_pw: str
:param wiki_archive: archive page
:type wiki_archive: str
:return: None
:rtype: None
"""
site = Site('entropia.de', path='/')
site.login(wiki_user, wiki_pw)
page = site.pages[wiki_archive]
text = page.text().split('\n')
last_table_position = 0
for event in past_events:
year_header = "== {} ==".format(event.endtime.strftime('%Y'))
for index, txtline in enumerate(text):
if txtline == '|}':
last_table_position = index
if str(event) in text:
continue
if year_header in text:
append_list = (
'\n' +
LINE_SEPARATOR +
str(event)
)
text = text[:last_table_position]+[append_list, ]+text[last_table_position:]
else:
append_list = (
3 * '\n' +
year_header +
ARCHIVE_TABLE_HEADER +
'\n' +
LINE_SEPARATOR +
'\n' +
str(event) +
'\n|}'
)
text = text[:last_table_position+1]+[append_list, ]+text[last_table_position+1:]
page.save("\n".join(text))
def get_args():
"""
Retrieve arguments from the command line, the config file respectively
:return: Parsed arguments from command line, config file
:rtype: list
"""
parser = ArgumentParser()
parser.add_argument(
"-c", "--config",
default="/etc/ics2entropiawiki/config.ini",
dest="configfile",
help="Configuration file path",
metavar="CONFIG"
)
parser.add_argument(
"-u", "--url",
dest="ics_url",
help="The URL under which the ICS-file can be retrieved",
metavar="URL",
)
parser.add_argument(
"-f", "--file",
dest="local_file",
help="Local ics file",
metavar="FILE"
)
parser.add_argument(
"--wiki-user",
dest="wiki_user",
help="Wiki user",
metavar="WIKIUSER"
)
parser.add_argument(
"--wiki-password",
dest="wiki_pw",
help="Wiki user's password",
metavar="WIKIPW"
)
parser.add_argument(
"--wiki-page",
dest="wiki_page",
help='Wiki page',
metavar='WIKIPAGE'
)
parser.add_argument(
"--wiki-archive",
dest="wiki_archive",
help='Wiki archive',
metavar='WIKIARCHIVE'
)
parser.add_argument(
"-d", "--debug",
dest="debug",
action="store_true",
default=False
)
args = parser.parse_args()
configfile = args.configfile
ics_url = args.ics_url
file = args.local_file
wiki = {
'user': args.wiki_user,
'pass': args.wiki_pw,
'page': args.wiki_page,
'archive': args.wiki_archive,
}
debug = args.debug
if configfile:
config = configparser.ConfigParser()
config.read(configfile)
try:
ics_url = config["default"]["url"]
wiki = config["wiki"]
except KeyError as error:
print("Please have a look at the sample config provided with the package")
raise error
return ics_url, file, wiki, debug
def deradicalise_ical(ics):
"""
:param ics: input file
:type ics: str
:return: file with remove radicale_headers
"""
deradicalised = ""
for line in ics.splitlines():
if 'X-RADICALE-NAME:' not in line:
deradicalised += "\n"+line
return deradicalised
def main():
"""
:return: None
:rtype: None
"""
ics_url, file, wiki, debug = get_args()
event_strings = []
past_events = []
if file:
calendar = Calendar(deradicalise_ical(open(file).read()))
else:
ics_result = requests.get(ics_url)
ics_result.encoding = 'utf-8'
calendar = Calendar(deradicalise_ical(ics_result.text))
for event in sorted(calendar.events, key=lambda ev: ev.begin):
event = EntropiaEvent(event)
if not event.is_past_event:
event_strings.append(
"\n" +
LINE_SEPARATOR +
str(event)
)
else:
past_events.append(event)
append_past_events(past_events, wiki['user'], wiki['pass'], wiki['archive'])
termine = BOTWARNING + "\n" + TABLE_HEADER + "\n" + "".join(event_strings) + "\n" + "".join(TABLE_FOOTER)
if debug:
print(termine)
site = Site('entropia.de', path='/')
site.login(wiki['user'], wiki['pass'])
page = site.pages[wiki['page']]
if termine:
page.save(termine, "Terminbot was here")
page.purge()
if __name__ == '__main__':
main()
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] | 2.181761 | 3,158 |
#!/bin/python3
import math
import os
import random
import re
import sys
# Complete the rotLeft function below.
if __name__ == '__main__':
fptr = open(os.environ['OUTPUT_PATH'], 'w')
nd = input().split()
n = int(nd[0])
d = int(nd[1])
a = list(map(int, input().rstrip().split()))
result = rotLeft(a, d)
fptr.write(' '.join(map(str, result)))
fptr.write('\n')
fptr.close()
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] | 2.109524 | 210 |
import os
import cflearn
import platform
import unittest
from cfdata.tabular import TabularDataset
num_jobs = 0 if platform.system() == "Linux" else 2
logging_folder = "__test_zoo__"
if __name__ == "__main__":
unittest.main()
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] | 2.764706 | 85 |
#
# Data Structures: Linked List Merge Sort: The Conquer Step
# Python Techdegree
#
# Created by Dulio Denis on 3/24/19.
# Copyright (c) 2019 ddApps. All rights reserved.
# ------------------------------------------------
from linked_list import Node, LinkedList
def merge_sort(linked_list):
'''
Sorts a linked list in ascending order.
- Recuresively divide the linked list into sublists containing a single node
- Repeatedly merge the sublists to produce sorted swublists until one remains
Returns a sorted linked list.
Runs in O(kn log n) time.
'''
if linked_list.size() == 1:
return linked_list
elif linked_list.is_empty():
return linked_list
left_half, right_half = split(linked_list)
left = merge_sort(left_half)
right = merge_sort(right_half)
return merge(left, right)
def split(linked_list):
'''
Divide the unsorted list at the midpoint into sublists.
Takes O(k log n) quasilinear time.
'''
if linked_list == None or linked_list.head == None:
left_half = linked_list
right_half = None
return left_half, right_half
else: # non-empty linked lists
size = linked_list.size()
midpoint = size // 2
mid_node = linked_list.node_at_index(midpoint-1)
left_half = linked_list
right_half = LinkedList()
right_half = mid_node.next_node
mid_node.next_node = None
return left_half, right_half
def merge(left, right):
'''
Merges two linked lists, sorting by data in nodes.
Returns a new, merged list.
Runs in O(n) linear time.
'''
# Create a new linked list that contains nodes from
# merging left and right
merged = LinkedList()
# Add a fake head that is discarded later to simplify code
merged.add(0)
# Set current to the head of the linked list
current = merged.head
# Obtain head nodes for left and right linked lists
left_head = left.head
right_head = right.head
# Iterate over left and right until we reach the tail node
# of either
while left_head or right_head:
# If the head node of the left is None, we're past the tail
# Add the node from right to merged linkned list
if left_head is None:
current.next_node = right_head
# Call next on right to set loop condition to False
right_head = right_head.next_node
# If the head node of right is None, we're past the tail
# Add the tail node from left to merged linked list
elif right_head is None:
current.next_node = left_head
# Call next on left to set loop condition to False
left_head = left_head.next_node
else:
# Not at either tail node
# Obtain node data to perform comparison operations
left_data = left_head.data
right_data = right_head.data
# If data on left is less than right, set current to left node
if left_data < right_data:
current.next_node = left_head
# Move left head to next node
left_head = left_head.next_node
# If data on left is greater than right, set current to right node
else:
current.next_node = right_head
# Move right head to next node
right_head = right_head.next_node
# Move current to next node
current = current.next_node
# Discard fake head and set first merged node as head
head = merged.head.next_node
merged.head = head
return merged
l = LinkedList()
l.add(10)
l.add(2)
l.add(44)
l.add(15)
l.add(200)
print(l)
sorted_linked_list = merge_sort(l)
print(sorted_linked_list)
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] | 2.495088 | 1,527 |
# -*- coding: utf-8 -*-
# Copyright (c) 2013 Ole Krause-Sparmann
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
import numpy
import scipy
import unittest
import time
from nearpy import Engine
from nearpy.distances import CosineDistance
from nearpy.hashes import RandomBinaryProjections, HashPermutations, HashPermutationMapper
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] | 3.716667 | 360 |
from discord.ext import commands, tasks # Bot Commands Framework
import traceback #
import os
import discord
import r
TOKEN = os.environ['DISCORD_BOT_TOKEN']
prefix = os.environ['DISCORD_BOT_PREFIX'] #
#
INITIAL_EXTENSIONS = [
'cogs.eval',
'cogs.glchat',
'cogs.gladd',
'cogs.gldel'
]
# ClientBot
#MyBot
if __name__ == '__main__':
bot = MyBot(command_prefix=prefix,help_command=JapaneseHelpCommand()) # command_prefix e.g. !ping
bot.run(TOKEN) # Bot
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] | 2.392157 | 204 |
"""
=================================================================
Spectral Unmixing
=================================================================
Suhas Somnath, Rama K. Vasudevan, Stephen Jesse
* Institute for Functional Imaging of Materials
* Center for Nanophase Materials Sciences
Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
In this notebook we load some spectral data, and perform basic data analysis, including:
========================================================================================
* KMeans Clustering
* Non-negative Matrix Factorization
* Principal Component Analysis
Software Prerequisites:
=======================
* Standard distribution of **Anaconda** (includes numpy, scipy, matplotlib and sci-kit learn)
* **pycroscopy** : Though pycroscopy is mainly used here for plotting purposes only, it's true capabilities
are realized through the ability to seamlessly perform these analyses on any imaging dataset (regardless
of origin, size, complexity) and storing the results back into the same dataset among other things
"""
# Import packages
# Ensure that this code works on both python 2 and python 3
from __future__ import division, print_function, absolute_import, unicode_literals
# basic numeric computation:
import numpy as np
# The package used for creating and manipulating HDF5 files:
import h5py
# Plotting and visualization:
import matplotlib.pyplot as plt
# for downloading files:
import wget
import os
# multivariate analysis:
from sklearn.cluster import KMeans
from sklearn.decomposition import NMF
import subprocess
import sys
# Package for downloading online files:
# finally import pycroscopy:
try:
import pycroscopy as px
except ImportError:
print('pycroscopy not found. Will install with pip.')
import pip
install('pycroscopy')
import pycroscopy as px
from pycroscopy.viz import cluster_utils
#####################################################################################
# The Data
# ========
#
# In this example, we will work on a **Band Excitation Piezoresponse Force Microscopy (BE-PFM)** imaging dataset
# acquired from advanced atomic force microscopes. In this dataset, a spectra was collected for each position in a two
# dimensional grid of spatial locations. Thus, this is a three dimensional dataset that has been flattened to a two
# dimensional matrix in accordance with the pycroscopy data format.
#
# Fortunately, all statistical analysis, machine learning, spectral unmixing algorithms, etc. only accept data that is
# formatted in the same manner of [position x spectra] in a two dimensional matrix.
#
# We will be using an data file available on our GitHub project page by default. You are encouraged
# to download this document as a Jupyter Notebook (button at the bottom of the page) and use your own data instead.
# When using your own data, you can skip this cell and provide the path to your data using the variable - data_file_path
data_file_path = 'temp_um.h5'
# download the data file from Github:
url = 'https://raw.githubusercontent.com/pycroscopy/pycroscopy/master/data/BELine_0004.h5'
data_file_path = wget.download(url, data_file_path, bar=None)
h5_file = h5py.File(data_file_path, mode='r+')
print('Contents of data file:')
print('----------------------')
px.hdf_utils.print_tree(h5_file)
print('----------------------')
h5_meas_grp = h5_file['Measurement_000']
# Extracting some basic parameters:
num_rows = px.hdf_utils.get_attr(h5_meas_grp, 'grid_num_rows')
num_cols = px.hdf_utils.get_attr(h5_meas_grp, 'grid_num_cols')
# Getting a reference to the main dataset:
h5_main = px.PycroDataset(h5_meas_grp['Channel_000/Raw_Data'])
px.hdf_utils.write_simple_attrs(h5_main, {'quantity': 'Deflection', 'units': 'V'})
# Extracting the X axis - vector of frequencies
h5_spec_vals = px.hdf_utils.get_auxiliary_datasets(h5_main, 'Spectroscopic_Values')[-1]
freq_vec = np.squeeze(h5_spec_vals.value) * 1E-3
print('Data currently of shape:', h5_main.shape)
x_label = 'Frequency (kHz)'
y_label = 'Amplitude (a.u.)'
#####################################################################################
# 1. Singular Value Decomposition (SVD)
# =====================================
#
# SVD is an eigenvector decomposition that is defined statistically, and therefore typically produces
# non-physical eigenvectors. Consequently, the interpretation of eigenvectors and abundance maps from
# SVD requires care and caution in interpretation. Nonetheless, it is a good method for quickly
# visualizing the major trends in the dataset since the resultant eigenvectors are sorted in descending
# order of variance or importance. Furthermore, SVD is also very well suited for data cleaning through
# the reconstruction of the dataset using only the first N (most significant) components.
#
# SVD results in three matrices:
#
# * V - Eigenvectors sorted by variance in descending order
# * U - corresponding abundance maps
# * S - Variance or importance of each of these components
#
# Advantage of pycroscopy:
# ------------------------
# Notice that we are working with a complex valued dataset. Passing the complex values as is to SVD would result in
# complex valued eigenvectors / endmembers as well as abundance maps. Complex valued abundance maps are not physical.
# Thus, one would need to restructure the data such that it is real-valued only.
#
# One solution is to stack the real value followed by the magnitude of the imaginary component before passing to SVD.
# After SVD, the real-valued eigenvectors would need to be treated as the concatenation of the real and imaginary
# components. So, the eigenvectors would need to be restructured to get back the complex valued eigenvectors.
#
# **Pycroscopy handles all these data transformations (both for the source dataset and the eigenvectors)
# automatically.** In general, pycroscopy handles compound / complex valued datasets everywhere possible
#
# Furthermore, while it is not discussed in this example, pycroscopy also writes back the results from SVD back to
# the same source h5 file including all relevant links to the source dataset and other ancillary datasets
decomposer = px.processing.svd_utils.SVD(h5_main, num_components=100)
h5_svd_group = decomposer.compute()
h5_u = h5_svd_group['U']
h5_v = h5_svd_group['V']
h5_s = h5_svd_group['S']
# Since the two spatial dimensions (x, y) have been collapsed to one, we need to reshape the abundance maps:
abun_maps = np.reshape(h5_u[:, :25], (num_rows, num_cols, -1))
px.plot_utils.plot_map_stack(abun_maps, num_comps=9, title='SVD Abundance Maps', reverse_dims=True,
color_bar_mode='single', cmap='inferno', title_yoffset=0.95)
# Visualize the variance / statistical importance of each component:
px.plot_utils.plot_scree(h5_s, title='Note the exponential drop of variance with number of components')
# Visualize the eigenvectors:
_ = px.plot_utils.plot_complex_spectra(h5_v[:9, :], x_label=x_label, y_label=y_label,
title='SVD Eigenvectors', evenly_spaced=False)
#####################################################################################
# 2. KMeans Clustering
# ====================
#
# KMeans clustering is a quick and easy method to determine the types of spectral responses present in the
# data. It is not a decomposition method, but a basic clustering method. The user inputs the number of
# clusters (sets) to partition the data into. The algorithm proceeds to find the optimal labeling
# (ie., assignment of each spectra as belonging to the k<sup>th</sup> set) such that the within-cluster
# sum of squares is minimized.
#
# Set the number of clusters below
num_clusters = 4
estimator = px.processing.Cluster(h5_main, KMeans(n_clusters=num_clusters))
h5_kmeans_grp = estimator.compute(h5_main)
h5_kmeans_labels = h5_kmeans_grp['Labels']
h5_kmeans_mean_resp = h5_kmeans_grp['Mean_Response']
cluster_utils.plot_cluster_h5_group(h5_kmeans_grp)
#####################################################################################
# 3. Non-negative Matrix Factorization (NMF)
# ===========================================
#
# NMF, or non-negative matrix factorization, is a method that is useful towards unmixing of spectral
# data. It only works on data with positive real values. It operates by approximate determination of
# factors (matrices) W and H, given a matrix V, as shown below
#
# .. image:: https://upload.wikimedia.org/wikipedia/commons/f/f9/NMF.png
#
# Unlike SVD and k-Means that can be applied to complex-valued datasets, NMF only works on non-negative datasets.
# For illustrative purposes, we will only take the amplitude component of the spectral data
num_comps = 4
# get the non-negative portion of the dataset
data_mat = np.abs(h5_main)
model = NMF(n_components=num_comps, init='random', random_state=0)
model.fit(data_mat)
fig, axis = plt.subplots(figsize=(5.5, 5))
px.plot_utils.plot_line_family(axis, freq_vec, model.components_, label_prefix='NMF Component #')
axis.set_xlabel(x_label, fontsize=12)
axis.set_ylabel(y_label, fontsize=12)
axis.set_title('NMF Components', fontsize=14)
axis.legend(bbox_to_anchor=[1.0, 1.0], fontsize=12)
#####################################################################################
# Close and delete the h5_file
h5_file.close()
os.remove(data_file_path)
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] | 3.448859 | 2,718 |
import shutil
from pathlib import Path
from unittest import TestCase
from unittest.mock import Mock
from unittest.mock import patch
from foliant.config.downloadfile import download_file
from foliant.config.downloadfile import get_file_ext_from_url
from foliant.config.downloadfile import get_file_name_from_url
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] | 3.434783 | 92 |
import discord
client = discord.Client() #
#
#
# id
# Text
# 0
def getTargetChannelId() -> int:
# ()
target_channel = {'id': 0, 'position': 99999999}
# ***********************************************************
# ()
category_id = 711238137598181396 # id
target_category_name = client.get_channel(category_id).name
# ***********************************************************
# Text
all_channels = client.get_guild(602423784946925568).text_channels
# Text
for channel in all_channels:
#
if str(channel.category) == target_category_name:
# position
# position(99999999)
# position
if target_channel['position'] > int(channel.position):
target_channel['id'] = int(channel.id)
target_channel['position'] = int(channel.position)
# id
return target_channel['id']
# botDiscord(bot)
client.run('605042341715378176')
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] | 2.515464 | 388 |
import matplotlib.pyplot as plt
import numpy as np
from fears.utils import results_manager, plotter, dir_manager
import os
suffix = '07212021_0001'
data_folder = 'results_' + suffix
exp_info_file = 'experiment_info_' + suffix + '.p'
exp_folders,exp_info = results_manager.get_experiment_results(data_folder,
exp_info_file)
max_cells = exp_info.populations[0].max_cells
n_sims = exp_info.n_sims
k_abs = exp_info.slopes
exp_folders.reverse()
k_abs = np.flip(k_abs)
fig,ax = plt.subplots(nrows=2,ncols=2,figsize=(4,4))
pop = exp_info.populations[0]
ax = ax.reshape((len(k_abs),))
axnum = 0
tc_axes=[]
drug_axes=[]
for exp in exp_folders:
k_abs_t = exp[exp.find('=')+1:]
k_abs_t = float(k_abs_t)
num = np.argwhere(k_abs == k_abs_t)
num = num[0,0]
# generate timecourse axes
tcax = ax[axnum]
# da = tcax.twinx()
sim_files = os.listdir(path=exp)
sim_files = sorted(sim_files)
survive_count = 0
counts_total = None
k=0
while k < len(sim_files):
# for sim in sim_files:
sim = sim_files[k]
sim = exp + os.sep + sim
data = results_manager.get_data(sim)
dc = data[:,-1]
data = data[:,0:-1]
# data = data/np.max(data)
data_t = data[-1,:]
# check to see if any genotypes are at least 10% of the max cell count
if any(data_t >= 1):
survive_count += 1
if counts_total is None:
counts_total = data
else:
counts_total += data
# data = data/np.max(data)
# exp_info.populations[num].counts_log_scale = True
data = data/max_cells
if k==0:
drug_kwargs = {'alpha':0.7,
'color':'black',
'linewidth':2,
'label':'Drug Concentration ($\u03BC$M)'
}
tcax,drug_ax = plotter.plot_timecourse_to_axes(exp_info.populations[num],
data,
tcax,
drug_curve=dc,
drug_ax_sci_notation=True,
drug_kwargs=drug_kwargs,
legend_labels=False,
grayscale=True,
color='gray',
linewidth=1,
labelsize=12,
alpha=0.7
)
drug_ax.set_ylabel('')
drug_axes.append( drug_ax )
else:
tcax,da = plotter.plot_timecourse_to_axes(exp_info.populations[num],
data,
tcax,
grayscale=True,
color='gray',
legend_labels=False,
linewidth=2,
labelsize=12,
alpha=0.2
)
# drug_ax.set_ylim(0,10**4)
k+=1
if survive_count > 0:
counts_avg = counts_total/survive_count
# counts_avg = counts_avg/np.max(counts_avg)
# counts_avg = counts_total
counts_avg = counts_avg/np.max(counts_avg)
tcax,temp = plotter.plot_timecourse_to_axes(exp_info.populations[num],
counts_avg,
tcax,
labelsize=12)
# t = np.arange(len(dc))
# t = t*exp_info.populations[0].timestep_scale/24
# da.plot(t,dc)
tc_axes.append( tcax )
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] | 1.505858 | 2,902 |
from __future__ import absolute_import
import types
from collections import defaultdict
from rpython.tool.ansi_print import AnsiLogger
from rpython.tool.pairtype import pair
from rpython.tool.error import (format_blocked_annotation_error,
gather_error, source_lines)
from rpython.flowspace.model import Variable, Constant, checkgraph
from rpython.translator import simplify, transform
from rpython.annotator import model as annmodel, signature
from rpython.annotator.model import (
typeof, s_ImpossibleValue, SomeInstance, intersection, difference)
from rpython.annotator.bookkeeper import Bookkeeper
from rpython.rtyper.normalizecalls import perform_normalizations
log = AnsiLogger("annrpython")
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628,
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] | 3.325792 | 221 |
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
# Generated file, DO NOT EDIT
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------------------------
from msrest.serialization import Model
| [
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] | 5.971963 | 107 |
import abc
from utils import LogMixin
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] | 3.076923 | 13 |
load("//python:compile.bzl", "py_proto_compile", "py_grpc_compile")
load("@grpc_py_deps//:requirements.bzl", "all_requirements") | [
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] | 2.37037 | 54 |
Subsets and Splits