
'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase    					=   logging.get_logger(__name__)




class 						a   (				UpperCamelCase_    ):
				_snake_case		    = '''timm_backbone'''




				def __init__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Any=None,    SCREAMING_SNAKE_CASE_	:				str=3,    SCREAMING_SNAKE_CASE_	:				List[str]=True,    SCREAMING_SNAKE_CASE_	:				str=True,    SCREAMING_SNAKE_CASE_	:				List[str]=None,    **SCREAMING_SNAKE_CASE_	:				List[str],    ):
										super().__init__(**UpperCamelCase__      )
										snake_case       :     Optional[Any] =    backbone
										snake_case       :     str =    num_channels
										snake_case       :     Dict =    features_only
										snake_case       :     Dict =    use_pretrained_backbone
										snake_case       :     List[str] =    True
										snake_case       :     int =    out_indices if out_indices is not None else (-1,)



'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCAmelCase    					=   {
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   ["ConvBertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ConvBertForMaskedLM",
						    "ConvBertForMultipleChoice",
						    "ConvBertForQuestionAnswering",
						    "ConvBertForSequenceClassification",
						    "ConvBertForTokenClassification",
						    "ConvBertLayer",
						    "ConvBertModel",
						    "ConvBertPreTrainedModel",
						    "load_tf_weights_in_convbert",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase    					=   [
						    "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFConvBertForMaskedLM",
						    "TFConvBertForMultipleChoice",
						    "TFConvBertForQuestionAnswering",
						    "TFConvBertForSequenceClassification",
						    "TFConvBertForTokenClassification",
						    "TFConvBertLayer",
						    "TFConvBertModel",
						    "TFConvBertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
						from .tokenization_convbert import ConvBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_convbert_fast import ConvBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_convbert import (
												    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConvBertForMaskedLM,
												    ConvBertForMultipleChoice,
												    ConvBertForQuestionAnswering,
												    ConvBertForSequenceClassification,
												    ConvBertForTokenClassification,
												    ConvBertLayer,
												    ConvBertModel,
												    ConvBertPreTrainedModel,
												    load_tf_weights_in_convbert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_convbert import (
												    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFConvBertForMaskedLM,
												    TFConvBertForMultipleChoice,
												    TFConvBertForQuestionAnswering,
												    TFConvBertForSequenceClassification,
												    TFConvBertForTokenClassification,
												    TFConvBertLayer,
												    TFConvBertModel,
												    TFConvBertPreTrainedModel,
												)


else:
						import sys

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

