




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     :		Optional[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 SCREAMING_SNAKE_CASE    (	lowercase_=True						)       ->	List[Any]:



       """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=UpperCAmelCase__					)					)
class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				None
    UpperCAmelCase__							  =				None


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     Union[str, Any])						->List[Any]:





           '''simple docstring'''

           with TemporaryDirectory() as tmp_dir:
                  A__							=   dataset_module_factory(UpperCAmelCase__ ,			cache_dir=UpperCAmelCase__)

                  A__							=   import_main_class(dataset_module.module_path ,			dataset=UpperCAmelCase__)

                  A__							=   builder_cls(
                      cache_dir=UpperCAmelCase__ ,			config_name=UpperCAmelCase__ ,			hash=dataset_module.hash ,			)

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







@pytest.mark.integration
def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Optional[int]:



       """simple docstring"""

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







@pytest.mark.integration
def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	int:



       """simple docstring"""

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase     :		Any								=       {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		str								=       [
      """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """NezhaForNextSentencePrediction""",
      """NezhaForMaskedLM""",
      """NezhaForPreTraining""",
      """NezhaForMultipleChoice""",
      """NezhaForQuestionAnswering""",
      """NezhaForSequenceClassification""",
      """NezhaForTokenClassification""",
      """NezhaModel""",
      """NezhaPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_nezha import (
        NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
        NezhaForMaskedLM,
        NezhaForMultipleChoice,
        NezhaForNextSentencePrediction,
        NezhaForPreTraining,
        NezhaForQuestionAnswering,
        NezhaForSequenceClassification,
        NezhaForTokenClassification,
        NezhaModel,
        NezhaPreTrainedModel,
    )


else:
  import sys

  _lowerCamelCase     :		List[str]								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


