code: 
  
  
  
  
  
  import enum
  import inspect
  from typing import Iterable, List, Optional, Tuple, Union
  
  
  
  
  
  
  class 	UpperCamelCase_     (     enum.Enum       ):
  					_a			:	Dict							=				'''timm'''
  					_a			:	str							=				'''transformers'''
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	):
  
  
  
  
  
  
  		if stage_names is None:
  				raise ValueError('Stage_names must be set for transformers backbones'	)
  
  		if out_features is not None:
  				if not isinstance(UpperCamelCase__       ,(list,)	):
  						raise ValueError(F"out_features must be a list {type(UpperCamelCase__	)}"	)
  				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(UpperCamelCase__       ,(list, tuple)	):
  						raise ValueError(F"out_indices must be a list or tuple, got {type(UpperCamelCase__	)}"	)
  				if any(idx >= len(UpperCamelCase__	) 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(UpperCamelCase__	) != len(UpperCamelCase__	):
  						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 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,):
  
  
  
  
  
  
  		if out_indices is None and out_features is None:
  				lowerCamelCase_  :    Tuple   	=			[len(UpperCamelCase__	) - 1]
  				lowerCamelCase_  :    Union[str, Any]   	=			[stage_names[-1]]
  		elif out_indices is None and out_features is not None:
  				lowerCamelCase_  :    Tuple   	=			[stage_names.index(UpperCamelCase__	) for layer in out_features]
  		elif out_features is None and out_indices is not None:
  				lowerCamelCase_  :    Tuple   	=			[stage_names[idx] for idx in out_indices]
  		return out_features, out_indices
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,):
  		verify_out_features_out_indices(out_features=UpperCamelCase__       ,out_indices=UpperCamelCase__       ,stage_names=UpperCamelCase__	)
  		lowerCamelCase_	,			lowerCamelCase_  :    Tuple   	=			_align_output_features_output_indices(
  		    out_features=UpperCamelCase__       ,out_indices=UpperCamelCase__       ,stage_names=UpperCamelCase__	)
  		# Verify that the aligned out_features and out_indices are valid
  		verify_out_features_out_indices(out_features=UpperCamelCase__       ,out_indices=UpperCamelCase__       ,stage_names=UpperCamelCase__	)
  		return output_features, output_indices
  
  
  
  
  
  
  class 	UpperCamelCase_     :
  					_a			:	Optional[BackboneType]							=				None
  
  
  
  					def      __a    (		self    :    str					,			lowerCamelCase    :    Optional[Any]	):
  							if getattr(self					,			'_backbone'					,			a_	) 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']
  							lowerCamelCase_  :    Any   	=			[stage['module'] for stage in self._backbone.feature_info.info]
  							lowerCamelCase_  :    str   	=			[stage['num_chs'] for stage in self._backbone.feature_info.info]
  							lowerCamelCase_  :    int   	=			self._backbone.feature_info.out_indices
  							lowerCamelCase_  :    Tuple   	=			self._backbone.feature_info.module_name()
  
  							# We verify the out indices and out features are valid
  							verify_out_features_out_indices(
  							    out_features=a_					,			out_indices=a_					,			stage_names=self.stage_names	)
  							lowerCamelCase_	,			lowerCamelCase_  :    List[str]   	=			out_features, out_indices
  
  
  
  					def      __a    (		self    :    Dict					,			lowerCamelCase    :    int	):
  							lowerCamelCase_  :    List[Any]   	=			getattr(a_					,			'stage_names'	)
  							lowerCamelCase_  :    List[Any]   	=			getattr(a_					,			'out_features'					,			a_	)
  							lowerCamelCase_  :    Tuple   	=			getattr(a_					,			'out_indices'					,			a_	)
  
  							lowerCamelCase_  :    Any   	=			stage_names
  							lowerCamelCase_	,			lowerCamelCase_  :    Optional[int]   	=			get_aligned_output_features_output_indices(
  							    out_features=a_					,			out_indices=a_					,			stage_names=a_	)
  							# Number of channels for each stage. This is set in the transformer backbone model init
  							lowerCamelCase_  :    int   	=			None
  
  
  
  					def      __a    (		self    :    Dict					,			lowerCamelCase    :    Optional[Any]	):
  							lowerCamelCase_  :    Optional[int]   	=			config
  
  							lowerCamelCase_  :    Tuple   	=			getattr(a_					,			'use_timm_backbone'					,			a_	)
  							lowerCamelCase_  :    Union[str, Any]   	=			BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS
  
  							if self.backbone_type == BackboneType.TIMM:
  									self._init_timm_backbone(a_	)
  							elif self.backbone_type == BackboneType.TRANSFORMERS:
  									self._init_transformers_backbone(a_	)
  							else:
  									raise ValueError(F"backbone_type {self.backbone_type} not supported."	)
  
  
  
  					@property
  					def      __a    (		self    :    Optional[Any]	):
  							return self._out_features
  
  
  
  					@out_features.setter
  					def      __a    (		self    :    Union[str, Any]					,			lowerCamelCase    :    Tuple	):
  							lowerCamelCase_	,			lowerCamelCase_  :    Any   	=			get_aligned_output_features_output_indices(
  							    out_features=a_					,			out_indices=a_					,			stage_names=self.stage_names	)
  
  
  
  					@property
  					def      __a    (		self    :    Optional[int]	):
  							return self._out_indices
  
  
  
  					@out_indices.setter
  					def      __a    (		self    :    List[Any]					,			lowerCamelCase    :    Any	):
  							lowerCamelCase_	,			lowerCamelCase_  :    Optional[Any]   	=			get_aligned_output_features_output_indices(
  							    out_features=a_					,			out_indices=a_					,			stage_names=self.stage_names	)
  
  
  
  					@property
  					def      __a    (		self    :    Any	):
  							# 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      __a    (		self    :    List[str]	):
  							return [self.out_feature_channels[name] for name in self.out_features]
  
  
  
  					def      __a    (		self    :    Union[str, Any]					,			*lowerCamelCase    :    Dict					,			**lowerCamelCase    :    Any	):
  							lowerCamelCase_  :    int   	=			dict(inspect.signature(self.forward	).parameters	)
  							lowerCamelCase_  :    Optional[Any]   	=			{k: v for k, v in kwargs.items() if k in signature}
  							return self(*a_					,			**a_	)
  
  
  
  					def      __a    (		self    :    int					,			lowerCamelCase    :    Tuple					,			lowerCamelCase    :    List[str] = None					,			lowerCamelCase    :    Any = None					,			lowerCamelCase    :    Tuple = None					,			):
  							raise NotImplementedError('This method should be implemented by the derived class.'	)
  
  
  
  
  
  					def      __a    (		self    :    List[Any]	):
  							lowerCamelCase_  :    Any   	=			super().to_dict()
  							lowerCamelCase_  :    Optional[Any]   	=			output.pop('_out_features'	)
  							lowerCamelCase_  :    int   	=			output.pop('_out_indices'	)
  							return output
  
  
  
  
  
  
  class 	UpperCamelCase_     :
  
  
  
  					@property
  					def      __a    (		self    :    str	):
  							return self._out_features
  
  
  
  					@out_features.setter
  					def      __a    (		self    :    str					,			lowerCamelCase    :    List[Any]	):
  							lowerCamelCase_	,			lowerCamelCase_  :    Union[str, Any]   	=			get_aligned_output_features_output_indices(
  							    out_features=a_					,			out_indices=a_					,			stage_names=self.stage_names	)
  
  
  
  					@property
  					def      __a    (		self    :    Tuple	):
  							return self._out_indices
  
  
  
  					@out_indices.setter
  					def      __a    (		self    :    List[Any]					,			lowerCamelCase    :    Union[str, Any]	):
  							lowerCamelCase_	,			lowerCamelCase_  :    Optional[int]   	=			get_aligned_output_features_output_indices(
  							    out_features=a_					,			out_indices=a_					,			stage_names=self.stage_names	)
  
  
  
  
  
  					def      __a    (		self    :    Optional[int]	):
  							lowerCamelCase_  :    Any   	=			super().to_dict()
  							lowerCamelCase_  :    str   	=			output.pop('_out_features'	)
  							lowerCamelCase_  :    Optional[Any]   	=			output.pop('_out_indices'	)
  							return output
  
style_context: 
  
  
  
  
  
  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		:     int 					={
      """configuration_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Any 					=["""MarianTokenizer"""]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Any 					=[
  		    """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
  		    """MarianForCausalLM""",
  		    """MarianModel""",
  		    """MarianMTModel""",
  		    """MarianPreTrainedModel""",
  		]
  
  try:
  		if not is_tf_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Dict 					=["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Optional[Any] 					=["""FlaxMarianModel""", """FlaxMarianMTModel""", """FlaxMarianPreTrainedModel"""]
  
  if TYPE_CHECKING:
  		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig
  
  		try:
  				if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_marian import MarianTokenizer
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_marian import (
  				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    MarianForCausalLM,
  				    MarianModel,
  				    MarianMTModel,
  				    MarianPreTrainedModel,
  				)
  
  		try:
  				if not is_tf_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel
  
  		try:
  				if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel
  
  else:
  		import sys
  
  		_lowercase		:     List[str] 					=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  