<gh_stars>0
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2018 Alibaba Group Holding Ltd.
#
# 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 itertools
from operator import attrgetter, mul
from weakref import WeakKeyDictionary, ref
from collections import Iterable
import threading

import numpy as np

from ..core import Entity
from ..compat import builtins, reduce
from ..graph import DAG
from ..tiles import Tilesable, handler
from ..serialize import SerializableWithKey, ValueType, ProviderType, \
    ListField, TupleField, DictField, DataTypeField, KeyField, BoolField
from ..utils import AttributeDict, on_serialize_shape, on_deserialize_shape, tokenize
from .expressions.utils import get_chunk_slices


class ChunkData(SerializableWithKey):
    __slots__ = '__weakref__',

    # required fields
    _shape = TupleField('shape', ValueType.int64,
                        on_serialize=on_serialize_shape, on_deserialize=on_deserialize_shape)
    _op = KeyField('op')  # store key of operand here
    # optional fields
    _dtype = DataTypeField('dtype')
    _index = TupleField('index', ValueType.uint32)
    _cached = BoolField('cached')
    _composed = ListField('composed', ValueType.reference('self'))
    _params = DictField('params', key_type=ValueType.string, on_deserialize=AttributeDict)

    def __init__(self, *args, **kwargs):
        extras = AttributeDict((k, kwargs.pop(k)) for k in set(kwargs) - set(self.__slots__))
        kwargs['_params'] = kwargs.pop('_params', extras)
        super(ChunkData, self).__init__(*args, **kwargs)

    def __repr__(self):
        return 'Chunk <op={0}, key={1}>'.format(self.op.__class__.__name__, self.key)

    @classmethod
    def cls(cls, provider):
        if provider.type == ProviderType.protobuf:
            from ..serialize.protos.chunk_pb2 import ChunkDef
            return ChunkDef
        return super(ChunkData, cls).cls(provider)

    @property
    def shape(self):
        return getattr(self, '_shape', None)

    @property
    def ndim(self):
        return len(self.shape)

    @property
    def index(self):
        return getattr(self, '_index', None)

    @property
    def op(self):
        try:
            return self._op
        except AttributeError:
            return None

    @property
    def cached(self):
        return getattr(self, '_cached', None)

    @property
    def inputs(self):
        return self.op.inputs

    @inputs.setter
    def inputs(self, new_inputs):
        self.op.inputs = new_inputs

    @property
    def dtype(self):
        return getattr(self, '_dtype', None) or self.op.dtype

    @property
    def nbytes(self):
        return np.prod(self.shape) * self.dtype.itemsize

    @property
    def composed(self):
        return getattr(self, '_composed', None)

    @property
    def device(self):
        return self.op.device

    def is_sparse(self):
        return self.op.is_sparse()

    issparse = is_sparse

    def update_key(self):
        object.__setattr__(self, '_key', tokenize(
            type(self), *(getattr(self, k, None) for k in self.__slots__ if k != '_index')))


class Chunk(Entity):
    __slots__ = ()
    _allow_data_type_ = (ChunkData,)


class TensorData(SerializableWithKey, Tilesable):
    __slots__ = '__weakref__',

    # required fields
    _shape = TupleField('shape', ValueType.int64,
                        on_serialize=on_serialize_shape, on_deserialize=on_deserialize_shape)
    _dtype = DataTypeField('dtype')
    _op = KeyField('op')
    # optional fields
    # `nsplits` means the sizes of chunks for each dimension
    _nsplits = TupleField('nsplits', ValueType.tuple(ValueType.uint64))
    _chunks = ListField('chunks', ValueType.reference(Chunk))
    _params = DictField('params', key_type=ValueType.string, on_deserialize=AttributeDict)

    def __init__(self, *args, **kwargs):
        extras = AttributeDict((k, kwargs.pop(k)) for k in set(kwargs) - set(self.__slots__))
        kwargs['_params'] = kwargs.pop('_params', extras)
        if '_nsplits' in kwargs:
            kwargs['_nsplits'] = tuple(tuple(s) for s in kwargs['_nsplits'])

        super(TensorData, self).__init__(*args, **kwargs)

        if hasattr(self, '_chunks') and self._chunks:
            self._chunks = sorted(self._chunks, key=attrgetter('index'))

    @classmethod
    def cls(cls, provider):
        if provider.type == ProviderType.protobuf:
            from ..serialize.protos.tensor_pb2 import TensorDef
            return TensorDef
        return super(TensorData, cls).cls(provider)

    def __repr__(self):
        return 'Tensor <op={0}, key={1}>'.format(self.op.__class__.__name__, self.key)

    @property
    def shape(self):
        if hasattr(self, '_shape') and self._shape is not None:
            return self._shape
        if hasattr(self, '_nsplits') and self._nsplits is not None:
            self._shape = tuple(builtins.sum(nsplit) for nsplit in self._nsplits)
            return self._shape

    @property
    def ndim(self):
        return len(self.shape)

    def __len__(self):
        try:
            return self.shape[0]
        except IndexError:
            if build_mode().is_build_mode:
                return 0
            raise TypeError('len() of unsized object')

    @property
    def nbytes(self):
        return np.prod(self.shape) * self.dtype.itemsize

    @property
    def chunk_shape(self):
        if hasattr(self, '_nsplits') and self._nsplits is not None:
            return tuple(map(len, self._nsplits))

    @property
    def chunks(self):
        return getattr(self, '_chunks', None)

    @property
    def op(self):
        return getattr(self, '_op', None)

    @property
    def nsplits(self):
        return getattr(self, '_nsplits', None)

    @property
    def size(self):
        return np.prod(self.shape).item()

    @property
    def inputs(self):
        return self.op.inputs or []

    @inputs.setter
    def inputs(self, new_inputs):
        self.op.inputs = new_inputs

    @property
    def dtype(self):
        return getattr(self, '_dtype', None) or self.op.dtype

    @property
    def params(self):
        return self._params

    @property
    def real(self):
        from .expressions.arithmetic import real
        return real(self)

    @property
    def imag(self):
        from .expressions.arithmetic import imag
        return imag(self)

    def get_chunk_slices(self, idx):
        return get_chunk_slices(self.nsplits, idx)

    def is_coarse(self):
        return not hasattr(self, '_chunks') or self._chunks is None or len(self._chunks) == 0

    def is_scalar(self):
        return self.ndim == 0

    isscalar = is_scalar

    def is_sparse(self):
        return self.op.is_sparse()

    issparse = is_sparse

    def tosparse(self):
        if self.issparse():
            return self

        from .expressions.datasource import fromdense
        return fromdense(self)

    def todense(self):
        if not self.issparse():
            return self

        from .expressions.datasource import fromsparse
        return fromsparse(self)

    @property
    def cix(self):
        if self.ndim == 0:
            return ChunksIndexer(self)

        try:
            if self not in _tensor_to_chunk_indexer:
                _tensor_to_chunk_indexer[self] = ChunksIndexer(self)

            return _tensor_to_chunk_indexer[self]
        except (TypeError, ValueError):
            return ChunksIndexer(self)

    def tiles(self):
        return handler.tiles(self)

    def single_tiles(self):
        return handler.single_tiles(self)

    def build_graph(self, graph=None, cls=DAG, tiled=False, compose=True):
        if tiled and self.is_coarse():
            self.tiles()

        graph = graph if graph is not None else cls()
        keys = None

        if tiled:
            nodes = list(c.data for c in self.chunks)
            keys = list(c.key for c in self.chunks)
        else:
            nodes = list(self.op.outputs)
        visited = set()
        while len(nodes) > 0:
            chunk = nodes.pop()
            visited.add(chunk)
            if not graph.contains(chunk):
                graph.add_node(chunk)
            children = chunk.inputs or []
            for c in children:
                if not graph.contains(c):
                    graph.add_node(c)
                if not graph.has_successor(c, chunk):
                    graph.add_edge(c, chunk)
            nodes.extend([c for c in itertools.chain(*[inp.op.outputs for inp in chunk.inputs or []])
                          if c not in visited])
        if tiled and compose:
            graph.compose(keys=keys)
        return graph

    def transpose(self, *axes):
        """
        Returns a view of the tensor with axes transposed.

        For a 1-D tensor, this has no effect. (To change between column and
        row vectors, first cast the 1-D tensor into a matrix object.)
        For a 2-D tensor, this is the usual matrix transpose.
        For an n-D tensor, if axes are given, their order indicates how the
        axes are permuted (see Examples). If axes are not provided and
        ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then
        ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.

        Parameters
        ----------
        axes : None, tuple of ints, or `n` ints

         * None or no argument: reverses the order of the axes.

         * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s
           `i`-th axis becomes `a.transpose()`'s `j`-th axis.

         * `n` ints: same as an n-tuple of the same ints (this form is
           intended simply as a "convenience" alternative to the tuple form)

        Returns
        -------
        out : Tensor
            View of `a`, with axes suitably permuted.

        See Also
        --------
        Tensor.T : Tensor property returning the tensor transposed.

        Examples
        --------
        >>> import mars.tensor as mt

        >>> a = mt.array([[1, 2], [3, 4]])
        >>> a.execute()
        array([[1, 2],
               [3, 4]])
        >>> a.transpose().execute()
        array([[1, 3],
               [2, 4]])
        >>> a.transpose((1, 0))
        array([[1, 3],
               [2, 4]])
        >>> a.transpose(1, 0).execute()
        array([[1, 3],
               [2, 4]])
        """
        from .expressions.base import transpose

        if len(axes) == 1 and isinstance(axes[0], Iterable):
            axes = axes[0]

        return transpose(self, axes)

    @property
    def T(self):
        """
        Same as self.transpose(), except that self is returned if
        self.ndim < 2.

        Examples
        --------
        >>> import mars.tensor as mt

        >>> x = mt.array([[1.,2.],[3.,4.]])
        >>> x.execute()
        array([[ 1.,  2.],
               [ 3.,  4.]])
        >>> x.T.execute()
        array([[ 1.,  3.],
               [ 2.,  4.]])
        >>> x = mt.array([1.,2.,3.,4.])
        >>> x.execute()
        array([ 1.,  2.,  3.,  4.])
        >>> x.T.execute()
        array([ 1.,  2.,  3.,  4.])
        """
        return self.transpose()

    def reshape(self, shape, *shapes):
        """
        Returns a tensor containing the same data with a new shape.

        Refer to `mt.reshape` for full documentation.

        See Also
        --------
        mt.reshape : equivalent function

        Notes
        -----
        Unlike the free function `mt.reshape`, this method on `Tensor` allows
        the elements of the shape parameter to be passed in as separate arguments.
        For example, ``a.reshape(10, 11)`` is equivalent to
        ``a.reshape((10, 11))``.
        """
        from .expressions.reshape import reshape

        if isinstance(shape, Iterable):
            shape = tuple(shape)
        else:
            shape = (shape,)
        shape += shapes

        return reshape(self, shape)

    def ravel(self):
        """
        Return a flattened tensor.

        Refer to `mt.ravel` for full documentation.

        See Also
        --------
        mt.ravel : equivalent function
        """
        from .expressions.base import ravel

        return ravel(self)

    flatten = ravel

    def _equals(self, o):
        return self is o

    def execute(self, session=None, **kw):
        from ..session import Session

        if session is None:
            session = Session.default_or_local()
        return session.run(self, **kw)

    def _set_execute_session(self, session):
        _cleaner.register(self, session)

    _execute_session = property(fset=_set_execute_session)

    def visualize(self, graph_attrs=None, node_attrs=None, **kw):
        from graphviz import Source

        g = self.build_graph(**kw)
        dot = g.to_dot(graph_attrs=graph_attrs, node_attrs=node_attrs)

        return Source(dot)


class ExecutableTuple(tuple):
    def execute(self, session=None, n_parallel=None):
        from ..session import Session

        if session is None:
            session = Session.default_or_local()
        return session.run(*self, n_parallel=n_parallel)


class ChunksIndexer(object):
    __slots__ = '_tensor',

    def __init__(self, tensor):
        self._tensor = tensor

    def __getitem__(self, item):
        if isinstance(item, tuple):
            if len(item) == 0 and self._tensor.is_scalar():
                return self._tensor.chunks[0]
            elif all(np.issubdtype(type(it), np.integer) for it in item):
                if len(item) != self._tensor.ndim:
                    raise ValueError('Cannot get tensor chunk by %s, expect length %d' % (
                        item, self._tensor.ndim))

                s = self._tensor.chunk_shape
                item = tuple(i if i >= 0 else i + s for i, s in zip(item, s))
                idx = sum(idx * reduce(mul, s[i+1:], 1) for i, idx
                          in zip(itertools.count(0), item))
                return self._tensor._chunks[idx]

        raise ValueError('Cannot get tensor chunk by {0}'.format(item))


class Tensor(Entity):
    __slots__ = ()
    _allow_data_type_ = (TensorData,)

    def __len__(self):
        return len(self._data)

    def copy(self):
        return Tensor(self._data)

    def tiles(self):
        return handler.tiles(self)

    def single_tiles(self):
        return handler.single_tiles(self)

    @property
    def shape(self):
        return self.data.shape

    @shape.setter
    def shape(self, new_shape):
        self._data = self._data.reshape(new_shape).data

    @property
    def real(self):
        return self.data.real

    @real.setter
    def real(self, new_real):
        from .expressions.arithmetic.setreal import set_real

        self._data = set_real(self._data, new_real).data

    @property
    def imag(self):
        return self.data.imag

    @imag.setter
    def imag(self, new_imag):
        from .expressions.arithmetic.setimag import set_imag

        self._data = set_imag(self._data, new_imag).data


class SparseTensor(Tensor):
    pass


TENSOR_TYPE = (Tensor, TensorData)
CHUNK_TYPE = (Chunk, ChunkData)

_tensor_to_chunk_indexer = WeakKeyDictionary()
_threading_local = threading.local()


class _TensorSession(object):
    def __init__(self, tensor, session):
        key = tensor.key, tensor.id

        def cb(_, sess=ref(session)):
            s = sess()
            if s:
                s.decref(key)
        self._tensor = ref(tensor, cb)


class _TensorCleaner(object):
    def __init__(self):
        self._tensor_to_sessions = WeakKeyDictionary()

    def register(self, tensor, session):
        with build_mode():
            self._tensor_to_sessions[tensor] = _TensorSession(tensor, session)


# we don't use __del__ to decref because a tensor holds an op,
# and op's outputs contains the tensor, so a circular references exists
_cleaner = _TensorCleaner()


class BuildMode(object):
    def __init__(self):
        self.is_build_mode = False
        self._old_mode = None

    def __enter__(self):
        if self._old_mode is None:
            # check to prevent nested enter and exit
            self._old_mode = self.is_build_mode
            self.is_build_mode = True

    def __exit__(self, *_):
        if self._old_mode is not None:
            self.is_build_mode = self._old_mode
            self._old_mode = None


def build_mode():
    ret = getattr(_threading_local, 'build_mode', None)
    if ret is None:
        ret = BuildMode()
        _threading_local.build_mode = ret

    return ret

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2018 Alibaba Group Holding Ltd.
#
# 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 itertools
from operator import attrgetter, mul
from weakref import WeakKeyDictionary, ref
from collections import Iterable
import threading

import numpy as np

from ..core import Entity
from ..compat import builtins, reduce
from ..graph import DAG
from ..tiles import Tilesable, handler
from ..serialize import SerializableWithKey, ValueType, ProviderType, \
    ListField, TupleField, DictField, DataTypeField, KeyField, BoolField
from ..utils import AttributeDict, on_serialize_shape, on_deserialize_shape, tokenize
from .expressions.utils import get_chunk_slices


class ChunkData(SerializableWithKey):
    __slots__ = '__weakref__',

    # required fields
    _shape = TupleField('shape', ValueType.int64,
                        on_serialize=on_serialize_shape, on_deserialize=on_deserialize_shape)
    _op = KeyField('op')  # store key of operand here
    # optional fields
    _dtype = DataTypeField('dtype')
    _index = TupleField('index', ValueType.uint32)
    _cached = BoolField('cached')
    _composed = ListField('composed', ValueType.reference('self'))
    _params = DictField('params', key_type=ValueType.string, on_deserialize=AttributeDict)

    def __init__(self, *args, **kwargs):
        extras = AttributeDict((k, kwargs.pop(k)) for k in set(kwargs) - set(self.__slots__))
        kwargs['_params'] = kwargs.pop('_params', extras)
        super(ChunkData, self).__init__(*args, **kwargs)

    def __repr__(self):
        return 'Chunk <op={0}, key={1}>'.format(self.op.__class__.__name__, self.key)

    @classmethod
    def cls(cls, provider):
        if provider.type == ProviderType.protobuf:
            from ..serialize.protos.chunk_pb2 import ChunkDef
            return ChunkDef
        return super(ChunkData, cls).cls(provider)

    @property
    def shape(self):
        return getattr(self, '_shape', None)

    @property
    def ndim(self):
        return len(self.shape)

    @property
    def index(self):
        return getattr(self, '_index', None)

    @property
    def op(self):
        try:
            return self._op
        except AttributeError:
            return None

    @property
    def cached(self):
        return getattr(self, '_cached', None)

    @property
    def inputs(self):
        return self.op.inputs

    @inputs.setter
    def inputs(self, new_inputs):
        self.op.inputs = new_inputs

    @property
    def dtype(self):
        return getattr(self, '_dtype', None) or self.op.dtype

    @property
    def nbytes(self):
        return np.prod(self.shape) * self.dtype.itemsize

    @property
    def composed(self):
        return getattr(self, '_composed', None)

    @property
    def device(self):
        return self.op.device

    def is_sparse(self):
        return self.op.is_sparse()

    issparse = is_sparse

    def update_key(self):
        object.__setattr__(self, '_key', tokenize(
            type(self), *(getattr(self, k, None) for k in self.__slots__ if k != '_index')))


class Chunk(Entity):
    __slots__ = ()
    _allow_data_type_ = (ChunkData,)


class TensorData(SerializableWithKey, Tilesable):
    __slots__ = '__weakref__',

    # required fields
    _shape = TupleField('shape', ValueType.int64,
                        on_serialize=on_serialize_shape, on_deserialize=on_deserialize_shape)
    _dtype = DataTypeField('dtype')
    _op = KeyField('op')
    # optional fields
    # `nsplits` means the sizes of chunks for each dimension
    _nsplits = TupleField('nsplits', ValueType.tuple(ValueType.uint64))
    _chunks = ListField('chunks', ValueType.reference(Chunk))
    _params = DictField('params', key_type=ValueType.string, on_deserialize=AttributeDict)

    def __init__(self, *args, **kwargs):
        extras = AttributeDict((k, kwargs.pop(k)) for k in set(kwargs) - set(self.__slots__))
        kwargs['_params'] = kwargs.pop('_params', extras)
        if '_nsplits' in kwargs:
            kwargs['_nsplits'] = tuple(tuple(s) for s in kwargs['_nsplits'])

        super(TensorData, self).__init__(*args, **kwargs)

        if hasattr(self, '_chunks') and self._chunks:
            self._chunks = sorted(self._chunks, key=attrgetter('index'))

    @classmethod
    def cls(cls, provider):
        if provider.type == ProviderType.protobuf:
            from ..serialize.protos.tensor_pb2 import TensorDef
            return TensorDef
        return super(TensorData, cls).cls(provider)

    def __repr__(self):
        return 'Tensor <op={0}, key={1}>'.format(self.op.__class__.__name__, self.key)

    @property
    def shape(self):
        if hasattr(self, '_shape') and self._shape is not None:
            return self._shape
        if hasattr(self, '_nsplits') and self._nsplits is not None:
            self._shape = tuple(builtins.sum(nsplit) for nsplit in self._nsplits)
            return self._shape

    @property
    def ndim(self):
        return len(self.shape)

    def __len__(self):
        try:
            return self.shape[0]
        except IndexError:
            if build_mode().is_build_mode:
                return 0
            raise TypeError('len() of unsized object')

    @property
    def nbytes(self):
        return np.prod(self.shape) * self.dtype.itemsize

    @property
    def chunk_shape(self):
        if hasattr(self, '_nsplits') and self._nsplits is not None:
            return tuple(map(len, self._nsplits))

    @property
    def chunks(self):
        return getattr(self, '_chunks', None)

    @property
    def op(self):
        return getattr(self, '_op', None)

    @property
    def nsplits(self):
        return getattr(self, '_nsplits', None)

    @property
    def size(self):
        return np.prod(self.shape).item()

    @property
    def inputs(self):
        return self.op.inputs or []

    @inputs.setter
    def inputs(self, new_inputs):
        self.op.inputs = new_inputs

    @property
    def dtype(self):
        return getattr(self, '_dtype', None) or self.op.dtype

    @property
    def params(self):
        return self._params

    @property
    def real(self):
        from .expressions.arithmetic import real
        return real(self)

    @property
    def imag(self):
        from .expressions.arithmetic import imag
        return imag(self)

    def get_chunk_slices(self, idx):
        return get_chunk_slices(self.nsplits, idx)

    def is_coarse(self):
        return not hasattr(self, '_chunks') or self._chunks is None or len(self._chunks) == 0

    def is_scalar(self):
        return self.ndim == 0

    isscalar = is_scalar

    def is_sparse(self):
        return self.op.is_sparse()

    issparse = is_sparse

    def tosparse(self):
        if self.issparse():
            return self

        from .expressions.datasource import fromdense
        return fromdense(self)

    def todense(self):
        if not self.issparse():
            return self

        from .expressions.datasource import fromsparse
        return fromsparse(self)

    @property
    def cix(self):
        if self.ndim == 0:
            return ChunksIndexer(self)

        try:
            if self not in _tensor_to_chunk_indexer:
                _tensor_to_chunk_indexer[self] = ChunksIndexer(self)

            return _tensor_to_chunk_indexer[self]
        except (TypeError, ValueError):
            return ChunksIndexer(self)

    def tiles(self):
        return handler.tiles(self)

    def single_tiles(self):
        return handler.single_tiles(self)

    def build_graph(self, graph=None, cls=DAG, tiled=False, compose=True):
        if tiled and self.is_coarse():
            self.tiles()

        graph = graph if graph is not None else cls()
        keys = None

        if tiled:
            nodes = list(c.data for c in self.chunks)
            keys = list(c.key for c in self.chunks)
        else:
            nodes = list(self.op.outputs)
        visited = set()
        while len(nodes) > 0:
            chunk = nodes.pop()
            visited.add(chunk)
            if not graph.contains(chunk):
                graph.add_node(chunk)
            children = chunk.inputs or []
            for c in children:
                if not graph.contains(c):
                    graph.add_node(c)
                if not graph.has_successor(c, chunk):
                    graph.add_edge(c, chunk)
            nodes.extend([c for c in itertools.chain(*[inp.op.outputs for inp in chunk.inputs or []])
                          if c not in visited])
        if tiled and compose:
            graph.compose(keys=keys)
        return graph

    def transpose(self, *axes):
        """
        Returns a view of the tensor with axes transposed.

        For a 1-D tensor, this has no effect. (To change between column and
        row vectors, first cast the 1-D tensor into a matrix object.)
        For a 2-D tensor, this is the usual matrix transpose.
        For an n-D tensor, if axes are given, their order indicates how the
        axes are permuted (see Examples). If axes are not provided and
        ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then
        ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.

        Parameters
        ----------
        axes : None, tuple of ints, or `n` ints

         * None or no argument: reverses the order of the axes.

         * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s
           `i`-th axis becomes `a.transpose()`'s `j`-th axis.

         * `n` ints: same as an n-tuple of the same ints (this form is
           intended simply as a "convenience" alternative to the tuple form)

        Returns
        -------
        out : Tensor
            View of `a`, with axes suitably permuted.

        See Also
        --------
        Tensor.T : Tensor property returning the tensor transposed.

        Examples
        --------
        >>> import mars.tensor as mt

        >>> a = mt.array([[1, 2], [3, 4]])
        >>> a.execute()
        array([[1, 2],
               [3, 4]])
        >>> a.transpose().execute()
        array([[1, 3],
               [2, 4]])
        >>> a.transpose((1, 0))
        array([[1, 3],
               [2, 4]])
        >>> a.transpose(1, 0).execute()
        array([[1, 3],
               [2, 4]])
        """
        from .expressions.base import transpose

        if len(axes) == 1 and isinstance(axes[0], Iterable):
            axes = axes[0]

        return transpose(self, axes)

    @property
    def T(self):
        """
        Same as self.transpose(), except that self is returned if
        self.ndim < 2.

        Examples
        --------
        >>> import mars.tensor as mt

        >>> x = mt.array([[1.,2.],[3.,4.]])
        >>> x.execute()
        array([[ 1.,  2.],
               [ 3.,  4.]])
        >>> x.T.execute()
        array([[ 1.,  3.],
               [ 2.,  4.]])
        >>> x = mt.array([1.,2.,3.,4.])
        >>> x.execute()
        array([ 1.,  2.,  3.,  4.])
        >>> x.T.execute()
        array([ 1.,  2.,  3.,  4.])
        """
        return self.transpose()

    def reshape(self, shape, *shapes):
        """
        Returns a tensor containing the same data with a new shape.

        Refer to `mt.reshape` for full documentation.

        See Also
        --------
        mt.reshape : equivalent function

        Notes
        -----
        Unlike the free function `mt.reshape`, this method on `Tensor` allows
        the elements of the shape parameter to be passed in as separate arguments.
        For example, ``a.reshape(10, 11)`` is equivalent to
        ``a.reshape((10, 11))``.
        """
        from .expressions.reshape import reshape

        if isinstance(shape, Iterable):
            shape = tuple(shape)
        else:
            shape = (shape,)
        shape += shapes

        return reshape(self, shape)

    def ravel(self):
        """
        Return a flattened tensor.

        Refer to `mt.ravel` for full documentation.

        See Also
        --------
        mt.ravel : equivalent function
        """
        from .expressions.base import ravel

        return ravel(self)

    flatten = ravel

    def _equals(self, o):
        return self is o

    def execute(self, session=None, **kw):
        from ..session import Session

        if session is None:
            session = Session.default_or_local()
        return session.run(self, **kw)

    def _set_execute_session(self, session):
        _cleaner.register(self, session)

    _execute_session = property(fset=_set_execute_session)

    def visualize(self, graph_attrs=None, node_attrs=None, **kw):
        from graphviz import Source

        g = self.build_graph(**kw)
        dot = g.to_dot(graph_attrs=graph_attrs, node_attrs=node_attrs)

        return Source(dot)


class ExecutableTuple(tuple):
    def execute(self, session=None, n_parallel=None):
        from ..session import Session

        if session is None:
            session = Session.default_or_local()
        return session.run(*self, n_parallel=n_parallel)


class ChunksIndexer(object):
    __slots__ = '_tensor',

    def __init__(self, tensor):
        self._tensor = tensor

    def __getitem__(self, item):
        if isinstance(item, tuple):
            if len(item) == 0 and self._tensor.is_scalar():
                return self._tensor.chunks[0]
            elif all(np.issubdtype(type(it), np.integer) for it in item):
                if len(item) != self._tensor.ndim:
                    raise ValueError('Cannot get tensor chunk by %s, expect length %d' % (
                        item, self._tensor.ndim))

                s = self._tensor.chunk_shape
                item = tuple(i if i >= 0 else i + s for i, s in zip(item, s))
                idx = sum(idx * reduce(mul, s[i+1:], 1) for i, idx
                          in zip(itertools.count(0), item))
                return self._tensor._chunks[idx]

        raise ValueError('Cannot get tensor chunk by {0}'.format(item))


class Tensor(Entity):
    __slots__ = ()
    _allow_data_type_ = (TensorData,)

    def __len__(self):
        return len(self._data)

    def copy(self):
        return Tensor(self._data)

    def tiles(self):
        return handler.tiles(self)

    def single_tiles(self):
        return handler.single_tiles(self)

    @property
    def shape(self):
        return self.data.shape

    @shape.setter
    def shape(self, new_shape):
        self._data = self._data.reshape(new_shape).data

    @property
    def real(self):
        return self.data.real

    @real.setter
    def real(self, new_real):
        from .expressions.arithmetic.setreal import set_real

        self._data = set_real(self._data, new_real).data

    @property
    def imag(self):
        return self.data.imag

    @imag.setter
    def imag(self, new_imag):
        from .expressions.arithmetic.setimag import set_imag

        self._data = set_imag(self._data, new_imag).data


class SparseTensor(Tensor):
    pass


TENSOR_TYPE = (Tensor, TensorData)
CHUNK_TYPE = (Chunk, ChunkData)

_tensor_to_chunk_indexer = WeakKeyDictionary()
_threading_local = threading.local()


class _TensorSession(object):
    def __init__(self, tensor, session):
        key = tensor.key, tensor.id

        def cb(_, sess=ref(session)):
            s = sess()
            if s:
                s.decref(key)
        self._tensor = ref(tensor, cb)


class _TensorCleaner(object):
    def __init__(self):
        self._tensor_to_sessions = WeakKeyDictionary()

    def register(self, tensor, session):
        with build_mode():
            self._tensor_to_sessions[tensor] = _TensorSession(tensor, session)


# we don't use __del__ to decref because a tensor holds an op,
# and op's outputs contains the tensor, so a circular references exists
_cleaner = _TensorCleaner()


class BuildMode(object):
    def __init__(self):
        self.is_build_mode = False
        self._old_mode = None

    def __enter__(self):
        if self._old_mode is None:
            # check to prevent nested enter and exit
            self._old_mode = self.is_build_mode
            self.is_build_mode = True

    def __exit__(self, *_):
        if self._old_mode is not None:
            self.is_build_mode = self._old_mode
            self._old_mode = None


def build_mode():
    ret = getattr(_threading_local, 'build_mode', None)
    if ret is None:
        ret = BuildMode()
        _threading_local.build_mode = ret

    return ret
#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 1999-2018 Alibaba Group Holding Ltd. # # 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. # required fields # store key of operand here # optional fields # required fields # optional fields # `nsplits` means the sizes of chunks for each dimension Returns a view of the tensor with axes transposed.          For a 1-D tensor, this has no effect. (To change between column and         row vectors, first cast the 1-D tensor into a matrix object.)         For a 2-D tensor, this is the usual matrix transpose.         For an n-D tensor, if axes are given, their order indicates how the         axes are permuted (see Examples). If axes are not provided and         ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then         ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.          Parameters         ----------         axes : None, tuple of ints, or `n` ints           * None or no argument: reverses the order of the axes.           * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s            `i`-th axis becomes `a.transpose()`'s `j`-th axis.           * `n` ints: same as an n-tuple of the same ints (this form is            intended simply as a "convenience" alternative to the tuple form)          Returns         -------         out : Tensor             View of `a`, with axes suitably permuted.          See Also         --------         Tensor.T : Tensor property returning the tensor transposed.          Examples         --------         >>> import mars.tensor as mt          >>> a = mt.array([[1, 2], [3, 4]])         >>> a.execute()         array([[1, 2],                [3, 4]])         >>> a.transpose().execute()         array([[1, 3],                [2, 4]])         >>> a.transpose((1, 0))         array([[1, 3],                [2, 4]])         >>> a.transpose(1, 0).execute()         array([[1, 3],                [2, 4]]) Same as self.transpose(), except that self is returned if         self.ndim < 2.          Examples         --------         >>> import mars.tensor as mt          >>> x = mt.array([[1.,2.],[3.,4.]])         >>> x.execute()         array([[ 1.,  2.],                [ 3.,  4.]])         >>> x.T.execute()         array([[ 1.,  3.],                [ 2.,  4.]])         >>> x = mt.array([1.,2.,3.,4.])         >>> x.execute()         array([ 1.,  2.,  3.,  4.])         >>> x.T.execute()         array([ 1.,  2.,  3.,  4.]) Returns a tensor containing the same data with a new shape.          Refer to `mt.reshape` for full documentation.          See Also         --------         mt.reshape : equivalent function          Notes         -----         Unlike the free function `mt.reshape`, this method on `Tensor` allows         the elements of the shape parameter to be passed in as separate arguments.         For example, ``a.reshape(10, 11)`` is equivalent to         ``a.reshape((10, 11))``. Return a flattened tensor.          Refer to `mt.ravel` for full documentation.          See Also         --------         mt.ravel : equivalent function # we don't use __del__ to decref because a tensor holds an op, # and op's outputs contains the tensor, so a circular references exists # check to prevent nested enter and exit