


import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


_UpperCAmelCase	:     Optional[Any]   =pytest.mark.integration



@pytest.mark.parametrize('''path'''       ,      ['''paws''', '''csv''']       )
def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       )->       Any:
				inspect_dataset(snake_case__       ,      snake_case__       )
				lowerCAmelCase_       :					Optional[int]			 = path + '''.py'''
				assert script_name in os.listdir(snake_case__       )
				assert "__pycache__" not in os.listdir(snake_case__       )



@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning'''       )
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''       )
@pytest.mark.parametrize('''path'''       ,      ['''accuracy''']       )
def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       )->       Any:
				inspect_metric(snake_case__       ,      snake_case__       )
				lowerCAmelCase_       :					List[Any]			 = path + '''.py'''
				assert script_name in os.listdir(snake_case__       )
				assert "__pycache__" not in os.listdir(snake_case__       )



@pytest.mark.parametrize(
    '''path, config_name, expected_splits'''       ,      [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]       ,      )
def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_       )->       Union[str, Any]:
				lowerCAmelCase_       :					int			 = get_dataset_config_info(snake_case__       ,      config_name=snake_case__       )
				assert info.config_name == config_name
				assert list(info.splits.keys()       ) == expected_splits



@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''       ,      [
        ('''paws''', None, ValueError),
    ]       ,      )
def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_       )->       Optional[int]:
				with pytest.raises(snake_case__       ):
								get_dataset_config_info(snake_case__       ,      config_name=snake_case__       )



@pytest.mark.parametrize(
    '''path, expected'''       ,      [
        ('''squad''', '''plain_text'''),
        ('''acronym_identification''', '''default'''),
        ('''lhoestq/squad''', '''plain_text'''),
        ('''lhoestq/test''', '''default'''),
        ('''lhoestq/demo1''', '''lhoestq--demo1'''),
        ('''dalle-mini/wit''', '''dalle-mini--wit'''),
    ]       ,      )
def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       )->       List[str]:
				lowerCAmelCase_       :					Union[str, Any]			 = get_dataset_config_names(snake_case__       )
				assert expected in config_names



@pytest.mark.parametrize(
    '''path, expected_configs, expected_splits_in_first_config'''       ,      [
        ('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
        ('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
        ('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
    ]       ,      )
def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_       )->       Any:
				lowerCAmelCase_       :					int			 = get_dataset_infos(snake_case__       )
				assert list(infos.keys()       ) == expected_configs
				lowerCAmelCase_       :					List[Any]			 = expected_configs[0]
				assert expected_config in infos
				lowerCAmelCase_       :					Any			 = infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys()       ) == expected_splits_in_first_config



@pytest.mark.parametrize(
    '''path, expected_config, expected_splits'''       ,      [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]       ,      )
def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_       )->       Tuple:
				lowerCAmelCase_       :					Dict			 = get_dataset_infos(snake_case__       )
				assert expected_config in infos
				lowerCAmelCase_       :					str			 = infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys()       ) == expected_splits




@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''       ,      [
        ('''paws''', None, ValueError),
    ]       ,      )
def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_       )->       Tuple:
				with pytest.raises(snake_case__       ):
								get_dataset_split_names(snake_case__       ,      config_name=snake_case__       )

from typing import TYPE_CHECKING

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


__a  			=					{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RemBertForCausalLM',
						    'RemBertForMaskedLM',
						    'RemBertForMultipleChoice',
						    'RemBertForQuestionAnswering',
						    'RemBertForSequenceClassification',
						    'RemBertForTokenClassification',
						    'RemBertLayer',
						    'RemBertModel',
						    'RemBertPreTrainedModel',
						    'load_tf_weights_in_rembert',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFRemBertForCausalLM',
						    'TFRemBertForMaskedLM',
						    'TFRemBertForMultipleChoice',
						    'TFRemBertForQuestionAnswering',
						    'TFRemBertForSequenceClassification',
						    'TFRemBertForTokenClassification',
						    'TFRemBertLayer',
						    'TFRemBertModel',
						    'TFRemBertPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert import RemBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert_fast import RemBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rembert import (
												    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RemBertForCausalLM,
												    RemBertForMaskedLM,
												    RemBertForMultipleChoice,
												    RemBertForQuestionAnswering,
												    RemBertForSequenceClassification,
												    RemBertForTokenClassification,
												    RemBertLayer,
												    RemBertModel,
												    RemBertPreTrainedModel,
												    load_tf_weights_in_rembert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rembert import (
												    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRemBertForCausalLM,
												    TFRemBertForMaskedLM,
												    TFRemBertForMultipleChoice,
												    TFRemBertForQuestionAnswering,
												    TFRemBertForSequenceClassification,
												    TFRemBertForTokenClassification,
												    TFRemBertLayer,
												    TFRemBertModel,
												    TFRemBertPreTrainedModel,
												)


else:
						import sys

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




