
'''simple docstring'''







from typing import TYPE_CHECKING

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


_snake_case					: Any     			=					{
    """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
    """tokenization_flaubert""": ["""FlaubertTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case					: Union[str, Any]     			=					[
						    """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """FlaubertForMultipleChoice""",
						    """FlaubertForQuestionAnswering""",
						    """FlaubertForQuestionAnsweringSimple""",
						    """FlaubertForSequenceClassification""",
						    """FlaubertForTokenClassification""",
						    """FlaubertModel""",
						    """FlaubertWithLMHeadModel""",
						    """FlaubertPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case					: Optional[int]     			=					[
						    """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFFlaubertForMultipleChoice""",
						    """TFFlaubertForQuestionAnsweringSimple""",
						    """TFFlaubertForSequenceClassification""",
						    """TFFlaubertForTokenClassification""",
						    """TFFlaubertModel""",
						    """TFFlaubertPreTrainedModel""",
						    """TFFlaubertWithLMHeadModel""",
						]


if TYPE_CHECKING:
						from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
						from .tokenization_flaubert import FlaubertTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flaubert import (
												    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaubertForMultipleChoice,
												    FlaubertForQuestionAnswering,
												    FlaubertForQuestionAnsweringSimple,
												    FlaubertForSequenceClassification,
												    FlaubertForTokenClassification,
												    FlaubertModel,
												    FlaubertPreTrainedModel,
												    FlaubertWithLMHeadModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_flaubert import (
												    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFFlaubertForMultipleChoice,
												    TFFlaubertForQuestionAnsweringSimple,
												    TFFlaubertForSequenceClassification,
												    TFFlaubertForTokenClassification,
												    TFFlaubertModel,
												    TFFlaubertPreTrainedModel,
												    TFFlaubertWithLMHeadModel,
												)

else:
						import sys

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

'''simple docstring'''







from ..utils import DummyObject, requires_backends


class       lowerCAmelCase			(					metaclass=__UpperCAmelCase      ):
		a				:       Any							=       ["""torch""", """transformers""", """invisible_watermark"""]




		def __init__(					self       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(self       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )


class       lowerCAmelCase			(					metaclass=__UpperCAmelCase      ):
		a				:       Optional[Any]							=       ["""torch""", """transformers""", """invisible_watermark"""]




		def __init__(					self       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(self       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )




		@classmethod
		def      lowercase							(					cls       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								requires_backends(cls       ,    ["torch", "transformers", "invisible_watermark"]   )