






"""simple docstring"""

from ..utils import DummyObject, requires_backends

class 						A_							(      metaclass=snake_case_  ):
				UpperCAmelCase__        =				['''torch''', '''transformers''', '''onnx''']







				def __init__(							self							:						Dict						,    *__lowerCamelCase							:						Tuple						,    **__lowerCamelCase							:						str	)				->					Tuple:
										requires_backends(self						,    ["torch", "transformers", "onnx"]	)







				@classmethod
				def     _snake_case					(							cls							:						Any						,    *__lowerCamelCase							:						Tuple						,    **__lowerCamelCase							:						Union[str, Any]	)				->					str:
										requires_backends(cls						,    ["torch", "transformers", "onnx"]	)







				@classmethod
				def     _snake_case					(							cls							:						str						,    *__lowerCamelCase							:						Union[str, Any]						,    **__lowerCamelCase							:						List[Any]	)				->					Tuple:
										requires_backends(cls						,    ["torch", "transformers", "onnx"]	)





class 						A_							(      metaclass=snake_case_  ):
				UpperCAmelCase__        =				['''torch''', '''transformers''', '''onnx''']







				def __init__(							self							:						List[str]						,    *__lowerCamelCase							:						Union[str, Any]						,    **__lowerCamelCase							:						Optional[Any]	)				->					List[str]:
										requires_backends(self						,    ["torch", "transformers", "onnx"]	)







				@classmethod
				def     _snake_case					(							cls							:						List[Any]						,    *__lowerCamelCase							:						str						,    **__lowerCamelCase							:						Any	)				->					Tuple:
										requires_backends(cls						,    ["torch", "transformers", "onnx"]	)







				@classmethod
				def     _snake_case					(							cls							:						Tuple						,    *__lowerCamelCase							:						List[str]						,    **__lowerCamelCase							:						int	)				->					List[str]:
										requires_backends(cls						,    ["torch", "transformers", "onnx"]	)





class 						A_							(      metaclass=snake_case_  ):
				UpperCAmelCase__        =				['''torch''', '''transformers''', '''onnx''']







				def __init__(							self							:						Dict						,    *__lowerCamelCase							:						List[Any]						,    **__lowerCamelCase							:						Optional[Any]	)				->					List[str]:
										requires_backends(self						,    ["torch", "transformers", "onnx"]	)







				@classmethod
				def     _snake_case					(							cls							:						Tuple						,    *__lowerCamelCase							:						List[str]						,    **__lowerCamelCase							:						Union[str, Any]	)				->					int:
										requires_backends(cls						,    ["torch", "transformers", "onnx"]	)







				@classmethod
				def     _snake_case					(							cls							:						int						,    *__lowerCamelCase							:						Tuple						,    **__lowerCamelCase							:						str	)				->					Optional[Any]:
										requires_backends(cls						,    ["torch", "transformers", "onnx"]	)





class 						A_							(      metaclass=snake_case_  ):
				UpperCAmelCase__        =				['''torch''', '''transformers''', '''onnx''']







				def __init__(							self							:						Tuple						,    *__lowerCamelCase							:						Tuple						,    **__lowerCamelCase							:						Any	)				->					Dict:
										requires_backends(self						,    ["torch", "transformers", "onnx"]	)







				@classmethod
				def     _snake_case					(							cls							:						int						,    *__lowerCamelCase							:						Union[str, Any]						,    **__lowerCamelCase							:						Dict	)				->					List[str]:
										requires_backends(cls						,    ["torch", "transformers", "onnx"]	)







				@classmethod
				def     _snake_case					(							cls							:						Optional[int]						,    *__lowerCamelCase							:						List[Any]						,    **__lowerCamelCase							:						Dict	)				->					Dict:
										requires_backends(cls						,    ["torch", "transformers", "onnx"]	)





class 						A_							(      metaclass=snake_case_  ):
				UpperCAmelCase__        =				['''torch''', '''transformers''', '''onnx''']







				def __init__(							self							:						List[str]						,    *__lowerCamelCase							:						str						,    **__lowerCamelCase							:						List[Any]	)				->					Optional[int]:
										requires_backends(self						,    ["torch", "transformers", "onnx"]	)







				@classmethod
				def     _snake_case					(							cls							:						Optional[Any]						,    *__lowerCamelCase							:						Optional[int]						,    **__lowerCamelCase							:						int	)				->					str:
										requires_backends(cls						,    ["torch", "transformers", "onnx"]	)







				@classmethod
				def     _snake_case					(							cls							:						Optional[int]						,    *__lowerCamelCase							:						int						,    **__lowerCamelCase							:						Tuple	)				->					Optional[int]:
										requires_backends(cls						,    ["torch", "transformers", "onnx"]	)





class 						A_							(      metaclass=snake_case_  ):
				UpperCAmelCase__        =				['''torch''', '''transformers''', '''onnx''']







				def __init__(							self							:						List[str]						,    *__lowerCamelCase							:						Optional[Any]						,    **__lowerCamelCase							:						Any	)				->					Any:
										requires_backends(self						,    ["torch", "transformers", "onnx"]	)







				@classmethod
				def     _snake_case					(							cls							:						Optional[int]						,    *__lowerCamelCase							:						Tuple						,    **__lowerCamelCase							:						Dict	)				->					Any:
										requires_backends(cls						,    ["torch", "transformers", "onnx"]	)







				@classmethod
				def     _snake_case					(							cls							:						int						,    *__lowerCamelCase							:						str						,    **__lowerCamelCase							:						Optional[int]	)				->					Optional[Any]:
										requires_backends(cls						,    ["torch", "transformers", "onnx"]	)











"""simple docstring"""

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

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

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase    			=       [
			    '''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    			=       [
			    '''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    			=       [
			    '''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    			=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



