





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


_SCREAMING_SNAKE_CASE 		=						[
    {"""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 							SCREAMING_SNAKE_CASE__  (      __a=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=snake_case_   )   )
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     List[Any]	 =    None
						__magic_name__:     List[Any]	 =    None






						def UpperCAmelCase_			( self : int						,				_A : Dict						,				_A : Any		)							->      List[str]:







							"""simple docstring"""


							with TemporaryDirectory() as tmp_dir:
								snake_case_						:     str     =		dataset_module_factory(_A						,				cache_dir=_A		)

								snake_case_						:     Union[str, Any]     =		import_main_class(dataset_module.module_path						,				dataset=_A		)

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

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


@pytest.mark.integration
def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Optional[int]     =		tmp_path_factory.mktemp('test_hf_gcp'				) / 'test_wikipedia_simple'
	snake_case_						:     Any     =		dataset_module_factory('wikipedia'				,	cache_dir=__a				)
	snake_case_						:     Union[str, Any]     =		import_main_class(dataset_module.module_path				)
	snake_case_						:     DatasetBuilder     =		builder_cls(
	    cache_dir=__a				,	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_						:     List[Any]     =		builder_instance.as_dataset()
	assert ds


@pytest.mark.integration
def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     int     =		dataset_module_factory('wikipedia'				,	cache_dir=__a				)
	snake_case_						:     Optional[int]     =		import_main_class(dataset_module.module_path				,	dataset=__a				)
	snake_case_						:     DatasetBuilder     =		builder_cls(
	    cache_dir=__a				,	config_name='20220301.frr'				,	hash=dataset_module.hash				,	)
	snake_case_						:     Dict     =		builder_instance.as_streaming_dataset()
	assert ds
	assert isinstance(__a				,	__a				)
	assert "train" in ds
	assert isinstance(ds['train']				,	__a				)
	assert next(iter(ds['train']				)				)








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """XLMForMultipleChoice""",
						    """XLMForQuestionAnswering""",
						    """XLMForQuestionAnsweringSimple""",
						    """XLMForSequenceClassification""",
						    """XLMForTokenClassification""",
						    """XLMModel""",
						    """XLMPreTrainedModel""",
						    """XLMWithLMHeadModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFXLMForMultipleChoice""",
						    """TFXLMForQuestionAnsweringSimple""",
						    """TFXLMForSequenceClassification""",
						    """TFXLMForTokenClassification""",
						    """TFXLMMainLayer""",
						    """TFXLMModel""",
						    """TFXLMPreTrainedModel""",
						    """TFXLMWithLMHeadModel""",
						]


if TYPE_CHECKING:
						from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
						from .tokenization_xlm import XLMTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm import (
												    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMForMultipleChoice,
												    XLMForQuestionAnswering,
												    XLMForQuestionAnsweringSimple,
												    XLMForSequenceClassification,
												    XLMForTokenClassification,
												    XLMModel,
												    XLMPreTrainedModel,
												    XLMWithLMHeadModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_xlm import (
												    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFXLMForMultipleChoice,
												    TFXLMForQuestionAnsweringSimple,
												    TFXLMForSequenceClassification,
												    TFXLMForTokenClassification,
												    TFXLMMainLayer,
												    TFXLMModel,
												    TFXLMPreTrainedModel,
												    TFXLMWithLMHeadModel,
												)

else:
						import sys

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

