





from typing import TYPE_CHECKING

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


lowercase_		:				Optional[Any]				=						{
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Any				=						['''XLMRobertaTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Optional[int]				=						['''XLMRobertaTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				int				=						[
		    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XLMRobertaForCausalLM''',
		    '''XLMRobertaForMaskedLM''',
		    '''XLMRobertaForMultipleChoice''',
		    '''XLMRobertaForQuestionAnswering''',
		    '''XLMRobertaForSequenceClassification''',
		    '''XLMRobertaForTokenClassification''',
		    '''XLMRobertaModel''',
		    '''XLMRobertaPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Optional[int]				=						[
		    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFXLMRobertaForCausalLM''',
		    '''TFXLMRobertaForMaskedLM''',
		    '''TFXLMRobertaForMultipleChoice''',
		    '''TFXLMRobertaForQuestionAnswering''',
		    '''TFXLMRobertaForSequenceClassification''',
		    '''TFXLMRobertaForTokenClassification''',
		    '''TFXLMRobertaModel''',
		    '''TFXLMRobertaPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				str				=						[
		    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''FlaxXLMRobertaForMaskedLM''',
		    '''FlaxXLMRobertaForCausalLM''',
		    '''FlaxXLMRobertaForMultipleChoice''',
		    '''FlaxXLMRobertaForQuestionAnswering''',
		    '''FlaxXLMRobertaForSequenceClassification''',
		    '''FlaxXLMRobertaForTokenClassification''',
		    '''FlaxXLMRobertaModel''',
		    '''FlaxXLMRobertaPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_xlm_roberta import (
		    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XLMRobertaConfig,
		    XLMRobertaOnnxConfig,
		)

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlm_roberta import XLMRobertaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xlm_roberta import (
				    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XLMRobertaForCausalLM,
				    XLMRobertaForMaskedLM,
				    XLMRobertaForMultipleChoice,
				    XLMRobertaForQuestionAnswering,
				    XLMRobertaForSequenceClassification,
				    XLMRobertaForTokenClassification,
				    XLMRobertaModel,
				    XLMRobertaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_xlm_roberta import (
				    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXLMRobertaForCausalLM,
				    TFXLMRobertaForMaskedLM,
				    TFXLMRobertaForMultipleChoice,
				    TFXLMRobertaForQuestionAnswering,
				    TFXLMRobertaForSequenceClassification,
				    TFXLMRobertaForTokenClassification,
				    TFXLMRobertaModel,
				    TFXLMRobertaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_xlm_roberta import (
				    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxXLMRobertaForCausalLM,
				    FlaxXLMRobertaForMaskedLM,
				    FlaxXLMRobertaForMultipleChoice,
				    FlaxXLMRobertaForQuestionAnswering,
				    FlaxXLMRobertaForSequenceClassification,
				    FlaxXLMRobertaForTokenClassification,
				    FlaxXLMRobertaModel,
				    FlaxXLMRobertaPreTrainedModel,
				)

else:
		import sys

		lowercase_		:				List[str]				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)













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,
)


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

@pytest.mark.parametrize('path'	,			['paws', 'csv'] )
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				inspect_dataset(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			str  						=   path + '.py'
				assert script_name in os.listdir(__lowerCAmelCase )
				assert "__pycache__" not in os.listdir(__lowerCAmelCase )

@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       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				inspect_metric(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			Tuple  						=   path + '.py'
				assert script_name in os.listdir(__lowerCAmelCase )
				assert "__pycache__" not in os.listdir(__lowerCAmelCase )

@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       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Union[str, Any]  						=   get_dataset_config_info(__lowerCAmelCase	,			config_name=__lowerCAmelCase )
				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       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				with pytest.raises(__lowerCAmelCase ):
								get_dataset_config_info(__lowerCAmelCase	,			config_name=__lowerCAmelCase )

@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       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Optional[Any]  						=   get_dataset_config_names(__lowerCAmelCase )
				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       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[Any]  						=   get_dataset_infos(__lowerCAmelCase )
				assert list(infos.keys() ) == expected_configs
				_snake_case :			Any  						=   expected_configs[0]
				assert expected_config in infos
				_snake_case :			str  						=   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       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Any  						=   get_dataset_infos(__lowerCAmelCase )
				assert expected_config in infos
				_snake_case :			Any  						=   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       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				with pytest.raises(__lowerCAmelCase ):
								get_dataset_split_names(__lowerCAmelCase	,			config_name=__lowerCAmelCase )






