





"""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


__magic_name__       :					List[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      UpperCamelCase							(SCREAMING_SNAKE_CASE=True     ):
						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=__SCREAMING_SNAKE_CASE	)	)
class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				List[Any]										=  None
			__lowerCAmelCase     :				List[str]										=  None







			def   _a      (      self						,   _A						,   _A    ):






									'''simple docstring'''







									with TemporaryDirectory() as tmp_dir:
															UpperCamelCase				:		Optional[int]				   =			dataset_module_factory(_A						,   cache_dir=_A    )

															UpperCamelCase				:		Optional[Any]				   =			import_main_class(dataset_module.module_path						,   dataset=_A    )

															UpperCamelCase				:		DatasetBuilder				   =			builder_cls(
															    cache_dir=_A						,   config_name=_A						,   hash=dataset_module.hash						,   )

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


@pytest.mark.integration
def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		str				   =			tmp_path_factory.mktemp("""test_hf_gcp"""     ) / """test_wikipedia_simple"""
						UpperCamelCase				:		Dict				   =			dataset_module_factory("""wikipedia"""							,     cache_dir=SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		str				   =			import_main_class(dataset_module.module_path     )
						UpperCamelCase				:		DatasetBuilder				   =			builder_cls(
						    cache_dir=SCREAMING_SNAKE_CASE							,     config_name="""20220301.frr"""							,     hash=dataset_module.hash							,     )
						# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
						UpperCamelCase				:		int				   =			None
						builder_instance.download_and_prepare()
						UpperCamelCase				:		str				   =			builder_instance.as_dataset()
						assert ds

@pytest.mark.integration
def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Any				   =			dataset_module_factory("""wikipedia"""							,     cache_dir=SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Any				   =			import_main_class(dataset_module.module_path							,     dataset=SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		DatasetBuilder				   =			builder_cls(
						    cache_dir=SCREAMING_SNAKE_CASE							,     config_name="""20220301.frr"""							,     hash=dataset_module.hash							,     )
						UpperCamelCase				:		List[Any]				   =			builder_instance.as_streaming_dataset()
						assert ds
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						assert "train" in ds
						assert isinstance(ds["""train"""]							,     SCREAMING_SNAKE_CASE     )
						assert next(iter(ds["""train"""]     )     )










"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__       :					Dict						=			{
    """configuration_layoutlmv3""": [
        """LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LayoutLMv3Config""",
        """LayoutLMv3OnnxConfig""",
    ],
    """processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
    """tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Optional[Any]						=			["""LayoutLMv3TokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					int						=			[
		    """LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LayoutLMv3ForQuestionAnswering""",
		    """LayoutLMv3ForSequenceClassification""",
		    """LayoutLMv3ForTokenClassification""",
		    """LayoutLMv3Model""",
		    """LayoutLMv3PreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Tuple						=			[
		    """TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFLayoutLMv3ForQuestionAnswering""",
		    """TFLayoutLMv3ForSequenceClassification""",
		    """TFLayoutLMv3ForTokenClassification""",
		    """TFLayoutLMv3Model""",
		    """TFLayoutLMv3PreTrainedModel""",
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Union[str, Any]						=			["""LayoutLMv3FeatureExtractor"""]
		__magic_name__       :					Any						=			["""LayoutLMv3ImageProcessor"""]


if TYPE_CHECKING:
		from .configuration_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LayoutLMvaConfig,
		    LayoutLMvaOnnxConfig,
		)
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_layoutlmva import (
				    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMvaForQuestionAnswering,
				    TFLayoutLMvaForSequenceClassification,
				    TFLayoutLMvaForTokenClassification,
				    TFLayoutLMvaModel,
				    TFLayoutLMvaPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
				from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
		import sys

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



