





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


__lowercase	: 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      a__			(       lowercase__=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   A							(						__a    ):
      _snake_case				  =None
      _snake_case				  =None



      def 						lowerCAmelCase__     (						self:    Optional[Any]		,	_lowerCAmelCase:    Any		,	_lowerCAmelCase:    Any							)    ->     Optional[Any]:
         '''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      a__			(       lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =tmp_path_factory.mktemp("test_hf_gcp"	) / "test_wikipedia_simple"
   UpperCAmelCase_           =dataset_module_factory("wikipedia"	,     cache_dir=lowercase__	)
   UpperCAmelCase_           =import_main_class(dataset_module.module_path	)
   UpperCAmelCase_           =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
   UpperCAmelCase_           =None
   builder_instance.download_and_prepare()
   UpperCAmelCase_           =builder_instance.as_dataset()
   assert ds





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






   '''simple docstring'''






   UpperCAmelCase_           =dataset_module_factory("wikipedia"	,     cache_dir=lowercase__	)
   UpperCAmelCase_           =import_main_class(dataset_module.module_path	,     dataset=lowercase__	)
   UpperCAmelCase_           =builder_cls(
       cache_dir=lowercase__	,     config_name="20220301.frr"	,     hash=dataset_module.hash	,     )
   UpperCAmelCase_           =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"]	)	)






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase							=				{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ViTMAEForPreTraining''',
					    '''ViTMAELayer''',
					    '''ViTMAEModel''',
					    '''ViTMAEPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''TFViTMAEForPreTraining''',
					    '''TFViTMAEModel''',
					    '''TFViTMAEPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

					_lowercase							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





