



import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union

class    _SCREAMING_SNAKE_CASE						(enum.Enum			):
     lowerCAmelCase       =      """timm"""
     lowerCAmelCase       =      """transformers"""






def 						_lowerCAmelCase    (					__lowerCamelCase :  Optional[Iterable[str]]		,						__lowerCamelCase :  Optional[Iterable[int]]		,						__lowerCamelCase :  Optional[Iterable[str]]  ):



 """simple docstring"""





 if stage_names is None:
  raise ValueError("Stage_names must be set for transformers backbones"  )

 if out_features is not None:
  if not isinstance(__lowerCamelCase		,						(list,)  ):
   raise ValueError(F"""out_features must be a list {type(__lowerCamelCase  )}"""  )
  if any(feat not in stage_names for feat in out_features  ):
   raise ValueError(F"""out_features must be a subset of stage_names: {stage_names} got {out_features}"""  )

 if out_indices is not None:
  if not isinstance(__lowerCamelCase		,						(list, tuple)  ):
   raise ValueError(F"""out_indices must be a list or tuple, got {type(__lowerCamelCase  )}"""  )
  if any(idx >= len(__lowerCamelCase  ) for idx in out_indices  ):
   raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"  )

 if out_features is not None and out_indices is not None:
  if len(__lowerCamelCase  ) != len(__lowerCamelCase  ):
   raise ValueError("out_features and out_indices should have the same length if both are set"  )
  if out_features != [stage_names[idx] for idx in out_indices]:
   raise ValueError("out_features and out_indices should correspond to the same stages if both are set"  )






def 						_lowerCAmelCase    (					__lowerCamelCase :  Optional[List[str]]		,						__lowerCamelCase :  Optional[Union[List[int], Tuple[int]]]		,						__lowerCamelCase :  List[str]		,						):



 """simple docstring"""





 if out_indices is None and out_features is None:
  __SCREAMING_SNAKE_CASE							:     List[str]							=						[len(__lowerCamelCase  ) - 1]
  __SCREAMING_SNAKE_CASE							:     Dict							=						[stage_names[-1]]
 elif out_indices is None and out_features is not None:
  __SCREAMING_SNAKE_CASE							:     int							=						[stage_names.index(__lowerCamelCase  ) for layer in out_features]
 elif out_features is None and out_indices is not None:
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						[stage_names[idx] for idx in out_indices]
 return out_features, out_indices






def 						_lowerCAmelCase    (					__lowerCamelCase :  Optional[List[str]]		,						__lowerCamelCase :  Optional[Union[List[int], Tuple[int]]]		,						__lowerCamelCase :  List[str]		,						):



 """simple docstring"""





 verify_out_features_out_indices(out_features=__lowerCamelCase		,						out_indices=__lowerCamelCase		,						stage_names=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     int							=						_align_output_features_output_indices(
     out_features=__lowerCamelCase		,						out_indices=__lowerCamelCase		,						stage_names=__lowerCamelCase  )
 # Verify that the aligned out_features and out_indices are valid
 verify_out_features_out_indices(out_features=__lowerCamelCase		,						out_indices=__lowerCamelCase		,						stage_names=__lowerCamelCase  )
 return output_features, output_indices

class    _SCREAMING_SNAKE_CASE						:
     lowerCAmelCase       =      None





     def 						__snake_case     (							self			:				Optional[int]  ,	UpperCamelCase			:				Optional[int]					)->None:
      if getattr(self  ,	"_backbone"  ,	UpperCamelCase					) is None:
       raise ValueError("self._backbone must be set before calling _init_timm_backbone"					)

      # These will diagree with the defaults for the transformers models e.g. for resnet50
      # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
      # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
      __SCREAMING_SNAKE_CASE							:     Tuple							=						[stage["module"] for stage in self._backbone.feature_info.info]
      __SCREAMING_SNAKE_CASE							:     Any							=						[stage["num_chs"] for stage in self._backbone.feature_info.info]
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self._backbone.feature_info.out_indices
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self._backbone.feature_info.module_name()

      # We verify the out indices and out features are valid
      verify_out_features_out_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=self.stage_names					)
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[Any]							=						out_features, out_indices





     def 						__snake_case     (							self			:				Any  ,	UpperCamelCase			:				Tuple					)->None:
      __SCREAMING_SNAKE_CASE							:     Dict							=						getattr(UpperCamelCase  ,	"stage_names"					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						getattr(UpperCamelCase  ,	"out_features"  ,	UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						getattr(UpperCamelCase  ,	"out_indices"  ,	UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Dict							=						stage_names
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[int]							=						get_aligned_output_features_output_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=UpperCamelCase					)
      # Number of channels for each stage. This is set in the transformer backbone model init
      __SCREAMING_SNAKE_CASE							:     str							=						None





     def 						__snake_case     (							self			:				Dict  ,	UpperCamelCase			:				Optional[int]					)->None:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						config

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						getattr(UpperCamelCase  ,	"use_timm_backbone"  ,	UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     int							=						BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

      if self.backbone_type == BackboneType.TIMM:
       self._init_timm_backbone(UpperCamelCase					)
      elif self.backbone_type == BackboneType.TRANSFORMERS:
       self._init_transformers_backbone(UpperCamelCase					)
      else:
       raise ValueError(F"""backbone_type {self.backbone_type} not supported."""					)





     @property
     def 						__snake_case     (							self			:				str					)->List[Any]:
      return self._out_features





     @out_features.setter
     def 						__snake_case     (							self			:				List[str]  ,	UpperCamelCase			:				List[str]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						get_aligned_output_features_output_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=self.stage_names					)





     @property
     def 						__snake_case     (							self			:				Optional[Any]					)->int:
      return self._out_indices





     @out_indices.setter
     def 						__snake_case     (							self			:				Union[str, Any]  ,	UpperCamelCase			:				Union[Tuple[int], List[int]]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[Any]							=						get_aligned_output_features_output_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=self.stage_names					)





     @property
     def 						__snake_case     (							self			:				Union[str, Any]					)->int:
      # the current backbones will output the number of channels for each stage
      # even if that stage is not in the out_features list.
      return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names					)}





     @property
     def 						__snake_case     (							self			:				Tuple					)->Optional[Any]:
      return [self.out_feature_channels[name] for name in self.out_features]





     def 						__snake_case     (							self			:				Optional[int]  ,	*UpperCamelCase			:				str  ,	**UpperCamelCase			:				Union[str, Any]					)->str:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						dict(inspect.signature(self.forward					).parameters					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						{k: v for k, v in kwargs.items() if k in signature}
      return self(*UpperCamelCase  ,	**UpperCamelCase					)





     def 						__snake_case     (							self			:				int  ,	UpperCamelCase			:				Optional[Any]  ,	UpperCamelCase			:				Optional[bool] = None  ,	UpperCamelCase			:				Optional[bool] = None  ,	UpperCamelCase			:				Optional[bool] = None  ,	)->Dict:
      raise NotImplementedError("This method should be implemented by the derived class."					)






     def 						__snake_case     (							self			:				List[str]					)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						super().to_dict()
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						output.pop("_out_features"					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						output.pop("_out_indices"					)
      return output






class    _SCREAMING_SNAKE_CASE						:





     @property
     def 						__snake_case     (							self			:				List[str]					)->Any:
      return self._out_features





     @out_features.setter
     def 						__snake_case     (							self			:				Optional[Any]  ,	UpperCamelCase			:				List[str]					)->str:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[int]							=						get_aligned_output_features_output_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=self.stage_names					)





     @property
     def 						__snake_case     (							self			:				Dict					)->List[str]:
      return self._out_indices





     @out_indices.setter
     def 						__snake_case     (							self			:				str  ,	UpperCamelCase			:				Union[Tuple[int], List[int]]					)->str:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[Any]							=						get_aligned_output_features_output_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=self.stage_names					)






     def 						__snake_case     (							self			:				int					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						super().to_dict()
      __SCREAMING_SNAKE_CASE							:     Tuple							=						output.pop("_out_features"					)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						output.pop("_out_indices"					)
      return output













"""simple docstring"""




from typing import TYPE_CHECKING

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


lowerCamelCase_	:		Union[str, Any]         =   {
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

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

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		int         =   ['CamembertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		Optional[Any]         =   [
       'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'CamembertForCausalLM',
       'CamembertForMaskedLM',
       'CamembertForMultipleChoice',
       'CamembertForQuestionAnswering',
       'CamembertForSequenceClassification',
       'CamembertForTokenClassification',
       'CamembertModel',
       'CamembertPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_	:		int         =   [
       'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFCamembertForCausalLM',
       'TFCamembertForMaskedLM',
       'TFCamembertForMultipleChoice',
       'TFCamembertForQuestionAnswering',
       'TFCamembertForSequenceClassification',
       'TFCamembertForTokenClassification',
       'TFCamembertModel',
       'TFCamembertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert import CamembertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_camembert_fast import CamembertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_camembert import (
          CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CamembertForCausalLM,
          CamembertForMaskedLM,
          CamembertForMultipleChoice,
          CamembertForQuestionAnswering,
          CamembertForSequenceClassification,
          CamembertForTokenClassification,
          CamembertModel,
          CamembertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_camembert import (
          TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFCamembertForCausalLM,
          TFCamembertForMaskedLM,
          TFCamembertForMultipleChoice,
          TFCamembertForQuestionAnswering,
          TFCamembertForSequenceClassification,
          TFCamembertForTokenClassification,
          TFCamembertModel,
          TFCamembertPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_	:		int         =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)