





'''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


A   : int             =			[
    {'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       lowercase_      (		lowercase__=True						)   ->Dict:
						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 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    None
					_SCREAMING_SNAKE_CASE							   =    None


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ,     __snake_case	:  List[str]       ,     __snake_case	:  Any       ):





											'''simple docstring'''





											with TemporaryDirectory() as tmp_dir:
																	_snake_case:   List[str]  					=		dataset_module_factory(__snake_case       ,     cache_dir=__snake_case       )

																	_snake_case:   Dict  					=		import_main_class(dataset_module.module_path       ,     dataset=__snake_case       )

																	_snake_case:   DatasetBuilder  					=		builder_cls(
																	    cache_dir=__snake_case       ,     config_name=__snake_case       ,     hash=dataset_module.hash       ,     )

																	_snake_case:   List[str]  					=		'/'.join(
																	    [
																	        HF_GCP_BASE_URL,
																	        builder_instance._relative_data_dir(with_hash=__snake_case       ).replace(os.sep       ,     '/'       ),
																	        config.DATASET_INFO_FILENAME,
																	    ]       )
																	_snake_case:   int  					=		cached_path(__snake_case       ,     cache_dir=__snake_case       )
																	self.assertTrue(os.path.exists(__snake_case       )       )





@pytest.mark.integration
def       lowercase_      (		lowercase__						)   ->List[Any]:
						_snake_case:   Tuple  					=		tmp_path_factory.mktemp('test_hf_gcp'						) / 'test_wikipedia_simple'
						_snake_case:   Tuple  					=		dataset_module_factory('wikipedia'				,  cache_dir=lowercase__						)
						_snake_case:   List[str]  					=		import_main_class(dataset_module.module_path						)
						_snake_case:   DatasetBuilder  					=		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
						_snake_case:   int  					=		None
						builder_instance.download_and_prepare()
						_snake_case:   Optional[Any]  					=		builder_instance.as_dataset()
						assert ds





@pytest.mark.integration
def       lowercase_      (		lowercase__						)   ->Tuple:
						_snake_case:   List[Any]  					=		dataset_module_factory('wikipedia'				,  cache_dir=lowercase__						)
						_snake_case:   Optional[int]  					=		import_main_class(dataset_module.module_path				,  dataset=lowercase__						)
						_snake_case:   DatasetBuilder  					=		builder_cls(
						    cache_dir=lowercase__				,  config_name='20220301.frr'				,  hash=dataset_module.hash				,  )
						_snake_case:   str  					=		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_tf_available,
    is_torch_available,
    is_vision_available,
)


A   : Optional[Any]             =			{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : str             =			['DeiTFeatureExtractor']
		A   : Optional[int]             =			['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Optional[Any]             =			[
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : int             =			[
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

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





