"""simple docstring"""





from typing import TYPE_CHECKING

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


_UpperCamelCase     :		Any											=					{
    "configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		str											=					[
           "ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ErnieForCausalLM",
           "ErnieForMaskedLM",
           "ErnieForMultipleChoice",
           "ErnieForNextSentencePrediction",
           "ErnieForPreTraining",
           "ErnieForQuestionAnswering",
           "ErnieForSequenceClassification",
           "ErnieForTokenClassification",
           "ErnieModel",
           "ErniePreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_ernie import (
                  ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ErnieForCausalLM,
                  ErnieForMaskedLM,
                  ErnieForMultipleChoice,
                  ErnieForNextSentencePrediction,
                  ErnieForPreTraining,
                  ErnieForQuestionAnswering,
                  ErnieForSequenceClassification,
                  ErnieForTokenClassification,
                  ErnieModel,
                  ErniePreTrainedModel,
              )

else:
       import sys

       _UpperCamelCase     :		Union[str, Any]											=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


"""simple docstring"""





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


_UpperCamelCase     :		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_  (   _lowerCAmelCase						:				Optional[Any]=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):
      lowerCamelCase__	:					str										=						None
      lowerCamelCase__	:					Optional[Any]										=						None


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      List[Any]:
             with TemporaryDirectory() as tmp_dir:
                    lowercase__    :	List[str]     					=			dataset_module_factory(a	,    cache_dir=a							)

                    lowercase__    :	List[Any]     					=			import_main_class(dataset_module.module_path	,    dataset=a							)

                    lowercase__    :	DatasetBuilder     					=			builder_cls(
                        cache_dir=a	,    config_name=a	,    hash=dataset_module.hash	,    )

                    lowercase__    :	Union[str, Any]     					=			'/'.join(
                        [
                            HF_GCP_BASE_URL,
                            builder_instance._relative_data_dir(with_hash=a							).replace(os.sep	,    '/'							),
                            config.DATASET_INFO_FILENAME,
                        ]							)
                    lowercase__    :	Union[str, Any]     					=			cached_path(a	,    cache_dir=a							)
                    self.assertTrue(os.path.exists(a							)							)





@pytest.mark.integration
def        a_  (   _lowerCAmelCase						:				str  ):





       '''simple docstring'''

       lowercase__    :	Union[str, Any]     					=			tmp_path_factory.mktemp('test_hf_gcp'  ) / 'test_wikipedia_simple'
       lowercase__    :	int     					=			dataset_module_factory('wikipedia'     ,				cache_dir=_lowerCAmelCase  )
       lowercase__    :	Optional[int]     					=			import_main_class(dataset_module.module_path  )
       lowercase__    :	DatasetBuilder     					=			builder_cls(
           cache_dir=_lowerCAmelCase     ,				config_name='20220301.frr'     ,				hash=dataset_module.hash     ,				)
       # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
       lowercase__    :	Optional[int]     					=			None
       builder_instance.download_and_prepare()
       lowercase__    :	Optional[int]     					=			builder_instance.as_dataset()
       assert ds






@pytest.mark.integration
def        a_  (   _lowerCAmelCase						:				Optional[Any]  ):





       '''simple docstring'''

       lowercase__    :	Optional[int]     					=			dataset_module_factory('wikipedia'     ,				cache_dir=_lowerCAmelCase  )
       lowercase__    :	List[str]     					=			import_main_class(dataset_module.module_path     ,				dataset=_lowerCAmelCase  )
       lowercase__    :	DatasetBuilder     					=			builder_cls(
           cache_dir=_lowerCAmelCase     ,				config_name='20220301.frr'     ,				hash=dataset_module.hash     ,				)
       lowercase__    :	Union[str, Any]     					=			builder_instance.as_streaming_dataset()
       assert ds
       assert isinstance(_lowerCAmelCase     ,				_lowerCAmelCase  )
       assert "train" in ds
       assert isinstance(ds['train']     ,				_lowerCAmelCase  )
       assert next(iter(ds['train']  )  )

