"""This section describes unitxt loaders.
Loaders: Generators of Unitxt Multistreams from existing date sources
=====================================================================
Unitxt is all about readily preparing of any given data source for feeding into any given language model, and then,
post-processing the model's output, preparing it for any given evaluator.
Through that journey, the data advances in the form of Unitxt Multistream, undergoing a sequential application
of various off-the-shelf operators (i.e., picked from Unitxt catalog), or operators easily implemented by inheriting.
The journey starts by a Unitxt Loader bearing a Multistream from the given datasource.
A loader, therefore, is the first item on any Unitxt Recipe.
Unitxt catalog contains several loaders for the most popular datasource formats.
All these loaders inherit from Loader, and hence, implementing a loader to expand over a new type of datasource is
straightforward.
Available Loaders Overview:
- :class:`LoadHF <unitxt.loaders.LoadHF>` - Loads data from HuggingFace Datasets.
- :class:`LoadCSV <unitxt.loaders.LoadCSV>` - Imports data from CSV (Comma-Separated Values) files.
- :class:`LoadFromKaggle <unitxt.loaders.LoadFromKaggle>` - Retrieves datasets from the Kaggle community site.
- :class:`LoadFromIBMCloud <unitxt.loaders.LoadFromIBMCloud>` - Fetches datasets hosted on IBM Cloud.
- :class:`LoadFromSklearn <unitxt.loaders.LoadFromSklearn>` - Loads datasets available through the sklearn library.
- :class:`MultipleSourceLoader <unitxt.loaders.MultipleSourceLoader>` - Combines data from multiple different sources.
- :class:`LoadFromDictionary <unitxt.loaders.LoadFromDictionary>` - Loads data from a user-defined Python dictionary.
- :class:`LoadFromHFSpace <unitxt.loaders.LoadFromHFSpace>` - Downloads and loads data from HuggingFace Spaces.
------------------------
"""
import fnmatch
import itertools
import json
import os
import tempfile
import time
from abc import abstractmethod
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import (
Any,
Dict,
Generator,
Iterable,
List,
Literal,
Mapping,
Optional,
Sequence,
Union,
)
import pandas as pd
import requests
from datasets import (
DatasetDict,
DownloadConfig,
IterableDataset,
IterableDatasetDict,
get_dataset_split_names,
)
from datasets import load_dataset as _hf_load_dataset
from huggingface_hub import HfApi
from tqdm import tqdm
from .dataclass import NonPositionalField
from .error_utils import Documentation, UnitxtError, UnitxtWarning
from .fusion import FixedFusion
from .logging_utils import get_logger
from .operator import SourceOperator
from .operators import Set
from .settings_utils import get_settings
from .stream import DynamicStream, MultiStream
from .type_utils import isoftype
from .utils import LRUCache, recursive_copy
logger = get_logger()
settings = get_settings()
class UnitxtUnverifiedCodeError(UnitxtError):
def __init__(self, path):
super().__init__(f"Loader cannot load and run remote code from {path} in huggingface without setting unitxt.settings.allow_unverified_code=True or by setting environment variable: UNITXT_ALLOW_UNVERIFIED_CODE.", Documentation.SETTINGS)
def hf_load_dataset(path: str, *args, **kwargs):
if settings.hf_offline_datasets_path is not None:
path = os.path.join(settings.hf_offline_datasets_path, path)
try:
return _hf_load_dataset(
path,
*args, **kwargs,
download_config=DownloadConfig(
max_retries=settings.loaders_max_retries,
),
verification_mode="no_checks",
trust_remote_code=settings.allow_unverified_code,
download_mode= "force_redownload" if settings.disable_hf_datasets_cache else "reuse_dataset_if_exists"
)
except ValueError as e:
if "trust_remote_code" in str(e):
raise UnitxtUnverifiedCodeError(path) from e
class Loader(SourceOperator):
"""A base class for all loaders.
A loader is the first component in the Unitxt Recipe,
responsible for loading data from various sources and preparing it as a MultiStream for processing.
The loader_limit is an optional parameter used to control the maximum number of instances to load from the data source. It is applied for each split separately.
It is usually provided to the loader via the recipe (see standard.py)
The loader can use this value to limit the amount of data downloaded from the source
to reduce loading time. However, this may not always be possible, so the
loader may ignore this. In any case, the recipe, will limit the number of instances in the returned
stream, after load is complete.
Args:
loader_limit: Optional integer to specify a limit on the number of records to load.
streaming: Bool indicating if streaming should be used.
num_proc: Optional integer to specify the number of processes to use for parallel dataset loading. Adjust the value according to the number of CPU cores available and the specific needs of your processing task.
"""
loader_limit: int = None
streaming: bool = False
num_proc: int = None
# class level shared cache:
_loader_cache = LRUCache(max_size=settings.loader_cache_size)
def get_limit(self) -> int:
if settings.global_loader_limit is not None and self.loader_limit is not None:
return min(int(settings.global_loader_limit), self.loader_limit)
if settings.global_loader_limit is not None:
return int(settings.global_loader_limit)
return self.loader_limit
def get_limiter(self):
if settings.global_loader_limit is not None and self.loader_limit is not None:
if int(settings.global_loader_limit) > self.loader_limit:
return f"{self.__class__.__name__}.loader_limit"
return "unitxt.settings.global_loader_limit"
if settings.global_loader_limit is not None:
return "unitxt.settings.global_loader_limit"
return f"{self.__class__.__name__}.loader_limit"
def log_limited_loading(self):
if not hasattr(self, "_already_logged_limited_loading") or not self._already_logged_limited_loading:
self._already_logged_limited_loading = True
logger.info(
f"\nLoading limited to {self.get_limit()} instances by setting {self.get_limiter()};"
)
def add_data_classification(self, multi_stream: MultiStream) -> MultiStream:
if self.data_classification_policy is None:
get_logger().warning(
f"The {self.get_pretty_print_name()} loader does not set the `data_classification_policy`. "
f"This may lead to sending of undesired data to external services.\n"
f"Set it to a list of classification identifiers. \n"
f"For example:\n"
f"data_classification_policy = ['public']\n"
f" or \n"
f"data_classification_policy =['confidential','pii'])\n"
)
operator = Set(
fields={"data_classification_policy": self.data_classification_policy}
)
return operator(multi_stream)
def set_default_data_classification(
self, default_data_classification_policy, additional_info
):
if self.data_classification_policy is None:
if additional_info is not None:
logger.info(
f"{self.get_pretty_print_name()} sets 'data_classification_policy' to "
f"{default_data_classification_policy} by default {additional_info}.\n"
"To use a different value or remove this message, explicitly set the "
"`data_classification_policy` attribute of the loader.\n"
)
self.data_classification_policy = default_data_classification_policy
@abstractmethod
def load_iterables(self) -> Dict[str, Iterable]:
pass
def _maybe_set_classification_policy(self):
pass
def load_data(self) -> MultiStream:
try:
iterables = self.load_iterables()
except Exception as e:
raise UnitxtError(f"Error in loader:\n{self}") from e
if isoftype(iterables, MultiStream):
return iterables
return MultiStream.from_iterables(iterables, copying=True)
def process(self) -> MultiStream:
self._maybe_set_classification_policy()
return self.add_data_classification(self.load_data())
def get_splits(self):
return list(self().keys())
class LazyLoader(Loader):
split: Optional[str] = NonPositionalField(default=None)
@abstractmethod
def get_splits(self) -> List[str]:
pass
@abstractmethod
def split_generator(self, split: str) -> Generator:
pass
def load_iterables(self) -> Union[Dict[str, DynamicStream], IterableDatasetDict]:
if self.split is not None:
splits = [self.split]
else:
splits = self.get_splits()
return MultiStream({
split: DynamicStream(self.split_generator, gen_kwargs={"split": split})
for split in splits
})
class LoadHF(LazyLoader):
"""Loads datasets from the HuggingFace Hub.
It supports loading with or without streaming,
and it can filter datasets upon loading.
Args:
path:
The path or identifier of the dataset on the HuggingFace Hub.
name:
An optional dataset name.
data_dir:
Optional directory to store downloaded data.
split:
Optional specification of which split to load.
data_files:
Optional specification of particular data files to load.
revision:
Optional. The revision of the dataset. Often the commit id. Use in case you want to set the dataset version.
streaming (bool):
indicating if streaming should be used.
filtering_lambda (str, optional):
A lambda function for filtering the data after loading.
num_proc (int, optional):
Specifies the number of processes to use for parallel dataset loading.
Example:
Loading glue's mrpc dataset
.. code-block:: python
load_hf = LoadHF(path='glue', name='mrpc')
"""
path: str
name: Optional[str] = None
data_dir: Optional[str] = None
split: Optional[str] = None
data_files: Optional[
Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]]
] = None
revision: Optional[str] = None
streaming: bool = None
filtering_lambda: Optional[str] = None
num_proc: Optional[int] = None
splits: Optional[List[str]] = None
def filter_load(self, dataset: DatasetDict):
if not settings.allow_unverified_code:
raise ValueError(
f"{self.__class__.__name__} cannot run use filtering_lambda expression without setting unitxt.settings.allow_unverified_code=True or by setting environment variable: UNITXT_ALLOW_UNVERIFIED_CODE=True."
)
logger.info(f"\nLoading filtered by: {self.filtering_lambda};")
return dataset.filter(eval(self.filtering_lambda))
def is_streaming(self) -> bool:
if self.streaming is None:
return settings.stream_hf_datasets_by_default
return self.streaming
# returns Dict when split names are not known in advance, and just the the single split dataset - if known
def load_dataset(
self, split: str, streaming=None, disable_memory_caching=False
) -> Union[IterableDatasetDict, IterableDataset]:
dataset_id = str(self) + "_" + str(split)
dataset = self.__class__._loader_cache.get(dataset_id, None)
if dataset is None:
if streaming is None:
streaming = self.is_streaming()
dataset = hf_load_dataset(
self.path,
name=self.name,
data_dir=self.data_dir,
data_files=self.data_files,
revision=self.revision,
streaming=streaming,
split=split,
num_proc=self.num_proc,
)
self.__class__._loader_cache.max_size = settings.loader_cache_size
if not disable_memory_caching:
self.__class__._loader_cache[dataset_id] = dataset
return dataset
def _maybe_set_classification_policy(self):
if os.path.exists(self.path):
self.set_default_data_classification(
["proprietary"], "when loading from local files"
)
else:
self.set_default_data_classification(
["public"],
None, # No warning when loading from public hub
)
def get_splits(self):
if self.splits is not None:
return self.splits
try:
return get_dataset_split_names(
path=self.path,
config_name=self.name,
trust_remote_code=settings.allow_unverified_code,
download_config=DownloadConfig(
max_retries=settings.loaders_max_retries,
extract_on_the_fly=True,
),
)
except Exception as e:
if "trust_remote_code" in str(e):
raise UnitxtUnverifiedCodeError(self.path) from e
UnitxtWarning(
f'LoadHF(path="{self.path}", name="{self.name}") could not retrieve split names without loading the dataset. Consider defining "splits" in the LoadHF definition to improve loading time.'
)
try:
dataset = self.load_dataset(
split=None, disable_memory_caching=True, streaming=True
)
except (
NotImplementedError
): # streaming is not supported for zipped files so we load without streaming
dataset = self.load_dataset(split=None, streaming=False)
return list(dataset.keys())
def split_generator(self, split: str) -> Generator:
if self.get_limit() is not None:
self.log_limited_loading()
try:
dataset = self.load_dataset(split=split)
except (
NotImplementedError
): # streaming is not supported for zipped files so we load without streaming
dataset = self.load_dataset(split=split, streaming=False)
if self.filtering_lambda is not None:
dataset = self.filter_load(dataset)
limit = self.get_limit()
if limit is None:
yield from dataset
else:
for i, instance in enumerate(dataset):
yield instance
if i + 1 >= limit:
break
class LoadCSV(LazyLoader):
"""Loads data from CSV files.
Supports streaming and can handle large files by loading them in chunks.
Args:
files (Dict[str, str]): A dictionary mapping names to file paths.
chunksize : Size of the chunks to load at a time.
loader_limit: Optional integer to specify a limit on the number of records to load.
streaming: Bool indicating if streaming should be used.
sep: String specifying the separator used in the CSV files.
Example:
Loading csv
.. code-block:: python
load_csv = LoadCSV(files={'train': 'path/to/train.csv'}, chunksize=100)
"""
files: Dict[str, str]
chunksize: int = 1000
loader_limit: Optional[int] = None
streaming: bool = True
sep: str = ","
compression: Optional[str] = None
lines: Optional[bool] = None
file_type: Literal["csv", "json"] = "csv"
def _maybe_set_classification_policy(self):
self.set_default_data_classification(
["proprietary"], "when loading from local files"
)
def get_reader(self):
if self.file_type == "csv":
return pd.read_csv
if self.file_type == "json":
return pd.read_json
raise ValueError()
def get_args(self):
args = {}
if self.file_type == "csv":
args["sep"] = self.sep
args["low_memory"] = self.streaming
if self.compression is not None:
args["compression"] = self.compression
if self.lines is not None:
args["lines"] = self.lines
if self.get_limit() is not None:
args["nrows"] = self.get_limit()
return args
def get_splits(self) -> List[str]:
return list(self.files.keys())
def split_generator(self, split: str) -> Generator:
dataset_id = str(self) + "_" + split
dataset = self.__class__._loader_cache.get(dataset_id, None)
if dataset is None:
if self.get_limit() is not None:
self.log_limited_loading()
for attempt in range(settings.loaders_max_retries):
try:
reader = self.get_reader()
if self.get_limit() is not None:
self.log_limited_loading()
try:
dataset = reader(self.files[split], **self.get_args()).to_dict(
"records"
)
break
except ValueError:
import fsspec
with fsspec.open(self.files[split], mode="rt") as f:
dataset = reader(f, **self.get_args()).to_dict("records")
break
except Exception as e:
logger.debug(f"Attempt csv load {attempt + 1} failed: {e}")
if attempt < settings.loaders_max_retries - 1:
time.sleep(2)
else:
raise e
self.__class__._loader_cache.max_size = settings.loader_cache_size
self.__class__._loader_cache[dataset_id] = dataset
for instance in self.__class__._loader_cache[dataset_id]:
yield recursive_copy(instance)
class LoadFromSklearn(LazyLoader):
"""Loads datasets from the sklearn library.
This loader does not support streaming and is intended for use with sklearn's dataset fetch functions.
Args:
dataset_name: The name of the sklearn dataset to fetch.
splits: A list of data splits to load, e.g., ['train', 'test'].
Example:
Loading form sklearn
.. code-block:: python
load_sklearn = LoadFromSklearn(dataset_name='iris', splits=['train', 'test'])
"""
dataset_name: str
splits: List[str] = ["train", "test"]
_requirements_list: List[str] = ["scikit-learn", "pandas"]
data_classification_policy = ["public"]
def verify(self):
super().verify()
if self.streaming:
raise NotImplementedError("LoadFromSklearn cannot load with streaming.")
def prepare(self):
super().prepare()
from sklearn import datasets as sklearn_datatasets
self.downloader = getattr(sklearn_datatasets, f"fetch_{self.dataset_name}")
def get_splits(self):
return self.splits
def split_generator(self, split: str) -> Generator:
dataset_id = str(self) + "_" + split
dataset = self.__class__._loader_cache.get(dataset_id, None)
if dataset is None:
split_data = self.downloader(subset=split)
targets = [split_data["target_names"][t] for t in split_data["target"]]
df = pd.DataFrame([split_data["data"], targets]).T
df.columns = ["data", "target"]
dataset = df.to_dict("records")
self.__class__._loader_cache.max_size = settings.loader_cache_size
self.__class__._loader_cache[dataset_id] = dataset
for instance in self.__class__._loader_cache[dataset_id]:
yield recursive_copy(instance)
class MissingKaggleCredentialsError(ValueError):
pass
class LoadFromKaggle(Loader):
"""Loads datasets from Kaggle.
Requires Kaggle API credentials and does not support streaming.
Args:
url: URL to the Kaggle dataset.
Example:
Loading from kaggle
.. code-block:: python
load_kaggle = LoadFromKaggle(url='kaggle.com/dataset/example')
"""
url: str
_requirements_list: List[str] = ["opendatasets"]
data_classification_policy = ["public"]
def verify(self):
super().verify()
if not os.path.isfile("kaggle.json"):
raise MissingKaggleCredentialsError(
"Please obtain kaggle credentials https://christianjmills.com/posts/kaggle-obtain-api-key-tutorial/ and save them to local ./kaggle.json file"
)
if self.streaming:
raise NotImplementedError("LoadFromKaggle cannot load with streaming.")
def prepare(self):
super().prepare()
from opendatasets import download
self.downloader = download
def load_iterables(self):
with TemporaryDirectory() as temp_directory:
self.downloader(self.url, temp_directory)
return hf_load_dataset(temp_directory, streaming=False)
class LoadFromIBMCloud(Loader):
"""Loads data from IBM Cloud Object Storage.
Does not support streaming and requires AWS-style access keys.
data_dir Can be either:
1. a list of file names, the split of each file is determined by the file name pattern
2. Mapping: split -> file_name, e.g. {"test" : "test.json", "train": "train.json"}
3. Mapping: split -> file_names, e.g. {"test" : ["test1.json", "test2.json"], "train": ["train.json"]}
Args:
endpoint_url_env:
Environment variable name for the IBM Cloud endpoint URL.
aws_access_key_id_env:
Environment variable name for the AWS access key ID.
aws_secret_access_key_env:
Environment variable name for the AWS secret access key.
bucket_name:
Name of the S3 bucket from which to load data.
data_dir:
Optional directory path within the bucket.
data_files:
Union type allowing either a list of file names or a mapping of splits to file names.
data_field:
The dataset key for nested JSON file, i.e. when multiple datasets are nested in the same file
caching (bool):
indicating if caching is enabled to avoid re-downloading data.
Example:
Loading from IBM Cloud
.. code-block:: python
load_ibm_cloud = LoadFromIBMCloud(
endpoint_url_env='IBM_CLOUD_ENDPOINT',
aws_access_key_id_env='IBM_AWS_ACCESS_KEY_ID',
aws_secret_access_key_env='IBM_AWS_SECRET_ACCESS_KEY', # pragma: allowlist secret
bucket_name='my-bucket'
)
multi_stream = load_ibm_cloud.process()
"""
endpoint_url_env: str
aws_access_key_id_env: str
aws_secret_access_key_env: str
bucket_name: str
data_dir: str = None
data_files: Union[Sequence[str], Mapping[str, Union[str, Sequence[str]]]]
data_field: str = None
caching: bool = True
data_classification_policy = ["proprietary"]
_requirements_list: List[str] = ["ibm-cos-sdk"]
def _download_from_cos(self, cos, bucket_name, item_name, local_file):
logger.info(f"Downloading {item_name} from {bucket_name} COS")
try:
response = cos.Object(bucket_name, item_name).get()
size = response["ContentLength"]
body = response["Body"]
except Exception as e:
raise Exception(
f"Unabled to access {item_name} in {bucket_name} in COS", e
) from e
if self.get_limit() is not None:
if item_name.endswith(".jsonl"):
first_lines = list(
itertools.islice(body.iter_lines(), self.get_limit())
)
with open(local_file, "wb") as downloaded_file:
for line in first_lines:
downloaded_file.write(line)
downloaded_file.write(b"\n")
logger.info(
f"\nDownload successful limited to {self.get_limit()} lines"
)
return
progress_bar = tqdm(total=size, unit="iB", unit_scale=True)
def upload_progress(chunk):
progress_bar.update(chunk)
try:
cos.Bucket(bucket_name).download_file(
item_name, local_file, Callback=upload_progress
)
logger.info("\nDownload Successful")
except Exception as e:
raise Exception(
f"Unabled to download {item_name} in {bucket_name}", e
) from e
def prepare(self):
super().prepare()
self.endpoint_url = os.getenv(self.endpoint_url_env)
self.aws_access_key_id = os.getenv(self.aws_access_key_id_env)
self.aws_secret_access_key = os.getenv(self.aws_secret_access_key_env)
root_dir = os.getenv("UNITXT_IBM_COS_CACHE", None) or os.getcwd()
self.cache_dir = os.path.join(root_dir, "ibmcos_datasets")
if not os.path.exists(self.cache_dir):
Path(self.cache_dir).mkdir(parents=True, exist_ok=True)
self.verified = False
def lazy_verify(self):
super().verify()
assert (
self.endpoint_url is not None
), f"Please set the {self.endpoint_url_env} environmental variable"
assert (
self.aws_access_key_id is not None
), f"Please set {self.aws_access_key_id_env} environmental variable"
assert (
self.aws_secret_access_key is not None
), f"Please set {self.aws_secret_access_key_env} environmental variable"
if self.streaming:
raise NotImplementedError("LoadFromKaggle cannot load with streaming.")
def _maybe_set_classification_policy(self):
self.set_default_data_classification(
["proprietary"], "when loading from IBM COS"
)
def load_iterables(self):
if not self.verified:
self.lazy_verify()
self.verified = True
import ibm_boto3
cos = ibm_boto3.resource(
"s3",
aws_access_key_id=self.aws_access_key_id,
aws_secret_access_key=self.aws_secret_access_key,
endpoint_url=self.endpoint_url,
)
local_dir = os.path.join(
self.cache_dir,
self.bucket_name,
self.data_dir or "", # data_dir can be None
f"loader_limit_{self.get_limit()}",
)
if not os.path.exists(local_dir):
Path(local_dir).mkdir(parents=True, exist_ok=True)
if isinstance(self.data_files, Mapping):
data_files_names = list(self.data_files.values())
if not isinstance(data_files_names[0], str):
data_files_names = list(itertools.chain(*data_files_names))
else:
data_files_names = self.data_files
for data_file in data_files_names:
local_file = os.path.join(local_dir, data_file)
if not self.caching or not os.path.exists(local_file):
# Build object key based on parameters. Slash character is not
# allowed to be part of object key in IBM COS.
object_key = (
self.data_dir + "/" + data_file
if self.data_dir is not None
else data_file
)
with tempfile.NamedTemporaryFile() as temp_file:
# Download to a temporary file in same file partition, and then do an atomic move
self._download_from_cos(
cos,
self.bucket_name,
object_key,
local_dir + "/" + os.path.basename(temp_file.name),
)
os.renames(
local_dir + "/" + os.path.basename(temp_file.name),
local_dir + "/" + data_file,
)
if isinstance(self.data_files, list):
dataset = hf_load_dataset(local_dir, streaming=False, field=self.data_field)
else:
dataset = hf_load_dataset(
local_dir,
streaming=False,
data_files=self.data_files,
field=self.data_field,
)
return dataset
class MultipleSourceLoader(LazyLoader):
"""Allows loading data from multiple sources, potentially mixing different types of loaders.
Args:
sources: A list of loaders that will be combined to form a unified dataset.
Examples:
1) Loading the train split from a HuggingFace Hub and the test set from a local file:
.. code-block:: python
MultipleSourceLoader(sources = [ LoadHF(path="public/data",split="train"), LoadCSV({"test": "mytest.csv"}) ])
2) Loading a test set combined from two files
.. code-block:: python
MultipleSourceLoader(sources = [ LoadCSV({"test": "mytest1.csv"}, LoadCSV({"test": "mytest2.csv"}) ])
"""
sources: List[Loader]
def add_data_classification(self, multi_stream: MultiStream) -> MultiStream:
if self.data_classification_policy is None:
return multi_stream
return super().add_data_classification(multi_stream)
def get_splits(self):
splits = []
for loader in self.sources:
splits.extend(loader.get_splits())
return list(set(splits))
def split_generator(self, split: str) -> Generator[Any, None, None]:
yield from FixedFusion(
subsets=self.sources,
max_instances_per_subset=self.get_limit(),
include_splits=[split],
)()[split]
class LoadFromDictionary(Loader):
"""Allows loading data from a dictionary of constants.
The loader can be used, for example, when debugging or working with small datasets.
Args:
data (Dict[str, List[Dict[str, Any]]]): a dictionary of constants from which the data will be loaded
Example:
Loading dictionary
.. code-block:: python
data = {
"train": [{"input": "SomeInput1", "output": "SomeResult1"},
{"input": "SomeInput2", "output": "SomeResult2"}],
"test": [{"input": "SomeInput3", "output": "SomeResult3"},
{"input": "SomeInput4", "output": "SomeResult4"}]
}
loader = LoadFromDictionary(data=data)
"""
data: Dict[str, List[Dict[str, Any]]]
def verify(self):
super().verify()
if not isoftype(self.data, Dict[str, List[Dict[str, Any]]]):
raise ValueError(
f"Passed data to LoadFromDictionary is not of type Dict[str, List[Dict[str, Any]]].\n"
f"Expected data should map between split name and list of instances.\n"
f"Received value: {self.data}\n"
)
for split in self.data.keys():
if len(self.data[split]) == 0:
raise ValueError(f"Split {split} has no instances.")
first_instance = self.data[split][0]
for instance in self.data[split]:
if instance.keys() != first_instance.keys():
raise ValueError(
f"Not all instances in split '{split}' have the same fields.\n"
f"instance {instance} has different fields different from {first_instance}"
)
def _maybe_set_classification_policy(self):
self.set_default_data_classification(
["proprietary"], "when loading from python dictionary"
)
def load_iterables(self) -> MultiStream:
return self.data
class LoadFromHFSpace(LazyLoader):
"""Used to load data from HuggingFace Spaces lazily.
Args:
space_name (str):
Name of the HuggingFace Space to be accessed.
data_files (str | Sequence[str] | Mapping[str, str | Sequence[str]]):
Relative paths to files within a given repository. If given as a mapping,
paths should be values, while keys should represent the type of respective files
(training, testing etc.).
path (str, optional):
Absolute path to a directory where data should be downloaded.
revision (str, optional):
ID of a Git branch or commit to be used. By default, it is set to None,
thus data is downloaded from the main branch of the accessed repository.
use_token (bool, optional):
Whether a token is used for authentication when accessing
the HuggingFace Space. If necessary, the token is read from the HuggingFace
config folder.
token_env (str, optional):
Key of an env variable which value will be used for
authentication when accessing the HuggingFace Space - if necessary.
"""
space_name: str
data_files: Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]]
path: Optional[str] = None
revision: Optional[str] = None
use_token: Optional[bool] = None
token_env: Optional[str] = None
requirements_list: List[str] = ["huggingface_hub"]
streaming: bool = True
def _get_token(self) -> Optional[Union[bool, str]]:
if self.token_env:
token = os.getenv(self.token_env)
if not token:
get_logger().warning(
f"The 'token_env' parameter was specified as '{self.token_env}', "
f"however, no environment variable under such a name was found. "
f"Therefore, the loader will not use any tokens for authentication."
)
return token
return self.use_token
@staticmethod
def _is_wildcard(path: str) -> bool:
wildcard_characters = ["*", "?", "[", "]"]
return any(char in path for char in wildcard_characters)
def _get_repo_files(self):
if not hasattr(self, "_repo_files") or self._repo_files is None:
api = HfApi()
self._repo_files = api.list_repo_files(
self.space_name, repo_type="space", revision=self.revision
)
return self._repo_files
def _get_sub_files(self, file: str) -> List[str]:
if self._is_wildcard(file):
return fnmatch.filter(self._get_repo_files(), file)
return [file]
def get_splits(self) -> List[str]:
if isinstance(self.data_files, Mapping):
return list(self.data_files.keys())
return ["train"] # Default to 'train' if not specified
def split_generator(self, split: str) -> Generator:
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError
token = self._get_token()
files = self.data_files.get(split, self.data_files) if isinstance(self.data_files, Mapping) else self.data_files
if isinstance(files, str):
files = [files]
limit = self.get_limit()
if limit is not None:
total = 0
self.log_limited_loading()
for file in files:
for sub_file in self._get_sub_files(file):
try:
file_path = hf_hub_download(
repo_id=self.space_name,
filename=sub_file,
repo_type="space",
token=token,
revision=self.revision,
local_dir=self.path,
)
except EntryNotFoundError as e:
raise ValueError(
f"The file '{file}' was not found in the space '{self.space_name}'. "
f"Please check if the filename is correct, or if it exists in that "
f"Huggingface space."
) from e
except RepositoryNotFoundError as e:
raise ValueError(
f"The Huggingface space '{self.space_name}' was not found. "
f"Please check if the name is correct and you have access to the space."
) from e
with open(file_path, encoding="utf-8") as f:
for line in f:
yield json.loads(line.strip())
if limit is not None:
total += 1
if total >= limit:
return
class LoadFromAPI(Loader):
"""Loads data from from API.
This loader is designed to fetch data from an API endpoint,
handling authentication through an API key. It supports
customizable chunk sizes and limits for data retrieval.
Args:
urls (Dict[str, str]):
A dictionary mapping split names to their respective API URLs.
chunksize (int, optional):
The size of data chunks to fetch in each request. Defaults to 100,000.
loader_limit (int, optional):
Limits the number of records to load. Applied per split. Defaults to None.
streaming (bool, optional):
Determines if data should be streamed. Defaults to False.
api_key_env_var (str, optional):
The name of the environment variable holding the API key.
Defaults to "SQL_API_KEY".
headers (Dict[str, Any], optional):
Additional headers to include in API requests. Defaults to None.
data_field (str, optional):
The name of the field in the API response that contains the data.
Defaults to "data".
method (str, optional):
The HTTP method to use for API requests. Defaults to "GET".
verify_cert (bool):
Apply verification of the SSL certificate
Defaults as True
"""
urls: Dict[str, str]
chunksize: int = 100000
loader_limit: Optional[int] = None
streaming: bool = False
api_key_env_var: str = "SQL_API_KEY"
headers: Optional[Dict[str, Any]] = None
data_field: str = "data"
method: str = "GET"
verify_cert: bool = True
# class level shared cache:
_loader_cache = LRUCache(max_size=settings.loader_cache_size)
def _maybe_set_classification_policy(self):
self.set_default_data_classification(["proprietary"], "when loading from API")
def load_iterables(self) -> Dict[str, Iterable]:
api_key = os.getenv(self.api_key_env_var, None)
if not api_key:
raise ValueError(
f"The environment variable '{self.api_key_env_var}' must be set to use the LoadFromAPI loader."
)
base_headers = {
"Content-Type": "application/json",
"accept": "application/json",
"Authorization": f"Bearer {api_key}",
}
if self.headers:
base_headers.update(self.headers)
iterables = {}
for split_name, url in self.urls.items():
if self.get_limit() is not None:
self.log_limited_loading()
if self.method == "GET":
response = requests.get(
url,
headers=base_headers,
verify=self.verify_cert,
)
elif self.method == "POST":
response = requests.post(
url,
headers=base_headers,
verify=self.verify_cert,
json={},
)
else:
raise ValueError(f"Method {self.method} not supported")
response.raise_for_status()
data = json.loads(response.text)
if self.data_field:
if self.data_field not in data:
raise ValueError(
f"Data field '{self.data_field}' not found in API response."
)
data = data[self.data_field]
if self.get_limit() is not None:
data = data[: self.get_limit()]
iterables[split_name] = data
return iterables
def process(self) -> MultiStream:
self._maybe_set_classification_policy()
iterables = self.__class__._loader_cache.get(str(self), None)
if iterables is None:
iterables = self.load_iterables()
self.__class__._loader_cache.max_size = settings.loader_cache_size
self.__class__._loader_cache[str(self)] = iterables
return MultiStream.from_iterables(iterables, copying=True)