


import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version







def        SCREAMING_SNAKE_CASE_						(			*__A :			str  ,    __A :			Optional[Union[Dict, Any]] = None  ,    __A :			Tuple=True  ,    __A :			int=2				)	->		Optional[Any]:

       """simple docstring"""

       from .. import __version__

       a_					:		Dict							=  take_from
       a_					:		List[str]							=  ()
       if not isinstance(args[0]  ,    __A				):
              a_					:		Optional[Any]							=  (args,)

       for attribute, version_name, message in args:
              if version.parse(version.parse(__A				).base_version				) >= version.parse(__A				):
                     raise ValueError(
                         F"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
                         F""" version {__version__} is >= {version_name}"""				)

              a_					:		Optional[Any]							=  None
              if isinstance(__A  ,    __A				) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(__A				),)
                     a_					:		Optional[int]							=  F"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
              elif hasattr(__A  ,    __A				):
                     values += (getattr(__A  ,    __A				),)
                     a_					:		int							=  F"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
              elif deprecated_kwargs is None:
                     a_					:		Union[str, Any]							=  F"""`{attribute}` is deprecated and will be removed in version {version_name}."""

              if warning is not None:
                     a_					:		str							=  warning + ' ' if standard_warn else ''
                     warnings.warn(warning + message  ,    __A  ,    stacklevel=__A				)

       if isinstance(__A  ,    __A				) and len(__A				) > 0:
              a_					:		List[Any]							=  inspect.getouterframes(inspect.currentframe()				)[1]
              a_					:		Dict							=  call_frame.filename
              a_					:		Union[str, Any]							=  call_frame.lineno
              a_					:		Union[str, Any]							=  call_frame.function
              a_							,		a_					:		Dict							=  next(iter(deprecated_kwargs.items()				)				)
              raise TypeError(F"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"""				)

       if len(__A				) == 0:
              return
       elif len(__A				) == 1:
              return values[0]
       return values




import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


__lowerCAmelCase			:     Any           =   [
    {'dataset': 'wikipedia', 'config_name': '20220301.de'},
    {'dataset': 'wikipedia', 'config_name': '20220301.en'},
    {'dataset': 'wikipedia', 'config_name': '20220301.fr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.frr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.it'},
    {'dataset': 'wikipedia', 'config_name': '20220301.simple'},
    {'dataset': 'snli', 'config_name': 'plain_text'},
    {'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
    {'dataset': 'wiki40b', 'config_name': 'en'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
    {'dataset': 'natural_questions', 'config_name': 'default'},
]

def a__    (       A_=True					):






  '''simple docstring'''





  if with_config:
    return [
        {
            "testcase_name": d["dataset"] + "/" + d["config_name"],
            "dataset": d["dataset"],
            "config_name": d["config_name"],
        }
        for d in DATASETS_ON_HF_GCP
    ]
  else:
    return [
        {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
    ]




@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_A     )     )
class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		None
  a__           =		None
  def 							_lowercase							( self			: List[Any]			,							UpperCamelCase__			: int			,							UpperCamelCase__			: Union[str, Any]    )			->	Tuple:







    """simple docstring"""






    with TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       dataset_module_factory(UpperCamelCase__			,							cache_dir=UpperCamelCase__    )

      __magic_name__				     =       import_main_class(dataset_module.module_path			,							dataset=UpperCamelCase__    )

      __magic_name__				     =       builder_cls(
          cache_dir=UpperCamelCase__			,							config_name=UpperCamelCase__			,							hash=dataset_module.hash			,							)

      __magic_name__				     =       """/""".join(
          [
              HF_GCP_BASE_URL,
              builder_instance._relative_data_dir(with_hash=UpperCamelCase__    ).replace(os.sep			,							"""/"""    ),
              config.DATASET_INFO_FILENAME,
          ]    )
      __magic_name__				     =       cached_path(UpperCamelCase__			,							cache_dir=UpperCamelCase__    )
      self.assertTrue(os.path.exists(UpperCamelCase__    )    )







@pytest.mark.integration
def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       tmp_path_factory.mktemp("""test_hf_gcp"""					) / """test_wikipedia_simple"""
  __magic_name__				     =       dataset_module_factory("""wikipedia""",     cache_dir=A_					)
  __magic_name__				     =       import_main_class(dataset_module.module_path					)
  __magic_name__				     =       builder_cls(
      cache_dir=A_,     config_name="""20220301.frr""",     hash=dataset_module.hash,     )
  # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
  __magic_name__				     =       None
  builder_instance.download_and_prepare()
  __magic_name__				     =       builder_instance.as_dataset()
  assert ds





@pytest.mark.integration
def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       dataset_module_factory("""wikipedia""",     cache_dir=A_					)
  __magic_name__				     =       import_main_class(dataset_module.module_path,     dataset=A_					)
  __magic_name__				     =       builder_cls(
      cache_dir=A_,     config_name="""20220301.frr""",     hash=dataset_module.hash,     )
  __magic_name__				     =       builder_instance.as_streaming_dataset()
  assert ds
  assert isinstance(A_,     A_					)
  assert "train" in ds
  assert isinstance(ds["""train"""],     A_					)
  assert next(iter(ds["""train"""]					)					)

