


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


__a     =			[
    {"""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   _UpperCamelCase				(						lowerCAmelCase_=True   )						->Optional[int]:
   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=__snake_case  )  )
class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       None
      UpperCamelCase								=       None





      def        _lowercase				(		self						:					List[Any]					,       __lowerCamelCase						:					List[Any]					,       __lowerCamelCase						:					Dict       )    ->   Tuple:






         """simple docstring"""







         with TemporaryDirectory() as tmp_dir:
            UpperCAmelCase							=     dataset_module_factory(__lowerCamelCase					,       cache_dir=__lowerCamelCase       )

            UpperCAmelCase							=     import_main_class(dataset_module.module_path					,       dataset=__lowerCamelCase       )

            UpperCAmelCase							=     builder_cls(
                cache_dir=__lowerCamelCase					,       config_name=__lowerCamelCase					,       hash=dataset_module.hash					,       )

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

@pytest.mark.integration
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Dict:
   UpperCAmelCase							=     tmp_path_factory.mktemp("""test_hf_gcp"""   ) / """test_wikipedia_simple"""
   UpperCAmelCase							=     dataset_module_factory("""wikipedia"""  ,						cache_dir=lowerCAmelCase_   )
   UpperCAmelCase							=     import_main_class(dataset_module.module_path   )
   UpperCAmelCase							=     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
   UpperCAmelCase							=     None
   builder_instance.download_and_prepare()
   UpperCAmelCase							=     builder_instance.as_dataset()
   assert ds







@pytest.mark.integration
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Tuple:
   UpperCAmelCase							=     dataset_module_factory("""wikipedia"""  ,						cache_dir=lowerCAmelCase_   )
   UpperCAmelCase							=     import_main_class(dataset_module.module_path  ,						dataset=lowerCAmelCase_   )
   UpperCAmelCase							=     builder_cls(
       cache_dir=lowerCAmelCase_  ,						config_name="""20220301.frr"""  ,						hash=dataset_module.hash  ,						)
   UpperCAmelCase							=     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"""]   )   )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a     =			{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a     =			[
     """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """IBertForMaskedLM""",
     """IBertForMultipleChoice""",
     """IBertForQuestionAnswering""",
     """IBertForSequenceClassification""",
     """IBertForTokenClassification""",
     """IBertModel""",
     """IBertPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ibert import (
      IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      IBertForMaskedLM,
      IBertForMultipleChoice,
      IBertForQuestionAnswering,
      IBertForSequenceClassification,
      IBertForTokenClassification,
      IBertModel,
      IBertPreTrainedModel,
  )

else:
 import sys

 __a     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



