






import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union
class 	a_(		enum.Enum	):



     """simple docstring"""



     __snake_case		:				str  					='''timm'''
     __snake_case		:				Tuple  					='''transformers'''





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->Optional[Any]:







 """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(lowercase_	,  (list,)			):
   raise ValueError(f'''out_features must be a list {type(lowercase_			)}'''			)
  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(lowercase_	,  (list, tuple)			):
   raise ValueError(f'''out_indices must be a list or tuple, got {type(lowercase_			)}'''			)
  if any(idx >= len(lowercase_			) 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(lowercase_			) != len(lowercase_			):
   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 						A_					(						lowercase_	,  lowercase_	,  lowercase_	,  )				->Union[str, Any]:







 """simple docstring"""

 if out_indices is None and out_features is None:
  SCREAMING_SNAKE_CASE				    =		[len(lowercase_			) - 1]
  SCREAMING_SNAKE_CASE				    =		[stage_names[-1]]
 elif out_indices is None and out_features is not None:
  SCREAMING_SNAKE_CASE				    =		[stage_names.index(lowercase_			) for layer in out_features]
 elif out_features is None and out_indices is not None:
  SCREAMING_SNAKE_CASE				    =		[stage_names[idx] for idx in out_indices]
 return out_features, out_indices





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_	,  )				->Tuple[List[str], List[int]]:







 """simple docstring"""

 verify_out_features_out_indices(out_features=lowercase_	,  out_indices=lowercase_	,  stage_names=lowercase_			)
 SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		_align_output_features_output_indices(
     out_features=lowercase_	,  out_indices=lowercase_	,  stage_names=lowercase_			)
 # Verify that the aligned out_features and out_indices are valid
 verify_out_features_out_indices(out_features=lowercase_	,  out_indices=lowercase_	,  stage_names=lowercase_			)
 return output_features, output_indices
class 	a_:



     """simple docstring"""



     __snake_case		:				Optional[BackboneType]  					=None





     def     __UpperCamelCase						(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	List[str])							->	None:

      """simple docstring"""
      if getattr(self  ,  '_backbone'  ,  lowerCAmelCase__) 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				    =		[stage['module'] for stage in self._backbone.feature_info.info]
      SCREAMING_SNAKE_CASE				    =		[stage['num_chs'] for stage in self._backbone.feature_info.info]
      SCREAMING_SNAKE_CASE				    =		self._backbone.feature_info.out_indices
      SCREAMING_SNAKE_CASE				    =		self._backbone.feature_info.module_name()

      # We verify the out indices and out features are valid
      verify_out_features_out_indices(
          out_features=lowerCAmelCase__  ,  out_indices=lowerCAmelCase__  ,  stage_names=self.stage_names)
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		out_features, out_indices





     def     __UpperCamelCase						(		self					:	str  ,  lowerCAmelCase__					:	Dict)							->	None:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		getattr(lowerCAmelCase__  ,  'stage_names')
      SCREAMING_SNAKE_CASE				    =		getattr(lowerCAmelCase__  ,  'out_features'  ,  lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		getattr(lowerCAmelCase__  ,  'out_indices'  ,  lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		stage_names
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		get_aligned_output_features_output_indices(
          out_features=lowerCAmelCase__  ,  out_indices=lowerCAmelCase__  ,  stage_names=lowerCAmelCase__)
      # Number of channels for each stage. This is set in the transformer backbone model init
      SCREAMING_SNAKE_CASE				    =		None





     def     __UpperCamelCase						(		self					:	Dict  ,  lowerCAmelCase__					:	int)							->	None:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		config

      SCREAMING_SNAKE_CASE				    =		getattr(lowerCAmelCase__  ,  'use_timm_backbone'  ,  lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

      if self.backbone_type == BackboneType.TIMM:
       self._init_timm_backbone(lowerCAmelCase__)
      elif self.backbone_type == BackboneType.TRANSFORMERS:
       self._init_transformers_backbone(lowerCAmelCase__)
      else:
       raise ValueError(f'''backbone_type {self.backbone_type} not supported.''')





     @property
     def     __UpperCamelCase						(		self					:	int)							->	Optional[int]:

      """simple docstring"""
      return self._out_features





     @out_features.setter
     def     __UpperCamelCase						(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	List[str])							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		get_aligned_output_features_output_indices(
          out_features=lowerCAmelCase__  ,  out_indices=lowerCAmelCase__  ,  stage_names=self.stage_names)





     @property
     def     __UpperCamelCase						(		self					:	str)							->	List[str]:

      """simple docstring"""
      return self._out_indices





     @out_indices.setter
     def     __UpperCamelCase						(		self					:	Tuple  ,  lowerCAmelCase__					:	Union[Tuple[int], List[int]])							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		get_aligned_output_features_output_indices(
          out_features=lowerCAmelCase__  ,  out_indices=lowerCAmelCase__  ,  stage_names=self.stage_names)





     @property
     def     __UpperCamelCase						(		self					:	List[str])							->	Optional[Any]:

      """simple docstring"""
      # 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     __UpperCamelCase						(		self					:	Optional[int])							->	List[str]:

      """simple docstring"""
      return [self.out_feature_channels[name] for name in self.out_features]





     def     __UpperCamelCase						(		self					:	List[str]  ,  *lowerCAmelCase__					:	Any  ,  **lowerCAmelCase__					:	Tuple)							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		dict(inspect.signature(self.forward).parameters)
      SCREAMING_SNAKE_CASE				    =		{k: v for k, v in kwargs.items() if k in signature}
      return self(*lowerCAmelCase__  ,  **lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Optional[int]  ,  lowerCAmelCase__					:	Any  ,  lowerCAmelCase__					:	Optional[bool] = None  ,  lowerCAmelCase__					:	Optional[bool] = None  ,  lowerCAmelCase__					:	Optional[bool] = None  ,  )							->	Union[str, Any]:

      """simple docstring"""
      raise NotImplementedError('This method should be implemented by the derived class.')





     def     __UpperCamelCase						(		self					:	Tuple)							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		super().to_dict()
      SCREAMING_SNAKE_CASE				    =		output.pop('_out_features')
      SCREAMING_SNAKE_CASE				    =		output.pop('_out_indices')
      return output

class 	a_:



     """simple docstring"""



     @property
     def     __UpperCamelCase						(		self					:	Any)							->	Union[str, Any]:

      """simple docstring"""
      return self._out_features





     @out_features.setter
     def     __UpperCamelCase						(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	List[str])							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		get_aligned_output_features_output_indices(
          out_features=lowerCAmelCase__  ,  out_indices=lowerCAmelCase__  ,  stage_names=self.stage_names)





     @property
     def     __UpperCamelCase						(		self					:	Tuple)							->	List[str]:

      """simple docstring"""
      return self._out_indices





     @out_indices.setter
     def     __UpperCamelCase						(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	Union[Tuple[int], List[int]])							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		get_aligned_output_features_output_indices(
          out_features=lowerCAmelCase__  ,  out_indices=lowerCAmelCase__  ,  stage_names=self.stage_names)





     def     __UpperCamelCase						(		self					:	str)							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		super().to_dict()
      SCREAMING_SNAKE_CASE				    =		output.pop('_out_features')
      SCREAMING_SNAKE_CASE				    =		output.pop('_out_indices')
      return output










'''simple docstring'''


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 						lowerCAmelCase_	(       UpperCAmelCase_							):







  '''simple docstring'''

  UpperCamelCase_			:      Optional[int]										=				"""AutoFeatureExtractor"""
  UpperCamelCase_			:      List[Any]										=				"""Speech2Text2Tokenizer"""



  def __init__( self				:							str					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int	)			->		Any:







       '''simple docstring'''



       super().__init__(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       A:		Union[str, Any]          =		self.feature_extractor
       A:		Dict          =		False



  def __call__( self				:							Any					,  *SCREAMING_SNAKE_CASE_				:							Any					,  **SCREAMING_SNAKE_CASE_				:							Optional[int]	)			->		Optional[Any]:







       '''simple docstring'''



       if self._in_target_context_manager:
            return self.current_processor(*SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)

       if "raw_speech" in kwargs:
            warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''	)
            A:		List[str]          =		kwargs.pop('''raw_speech'''	)
       else:
            A:		Any          =		kwargs.pop('''audio'''					,  SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		kwargs.pop('''sampling_rate'''					,  SCREAMING_SNAKE_CASE_	)
       A:		Optional[int]          =		kwargs.pop('''text'''					,  SCREAMING_SNAKE_CASE_	)
       if len(SCREAMING_SNAKE_CASE_	) > 0:
            A:		str          =		args[0]
            A:		Tuple          =		args[1:]

       if audio is None and text is None:
            raise ValueError('''You need to specify either an `audio` or `text` input to process.'''	)

       if audio is not None:
            A:		Tuple          =		self.feature_extractor(SCREAMING_SNAKE_CASE_					,  *SCREAMING_SNAKE_CASE_					,  sampling_rate=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)
       if text is not None:
            A:		List[Any]          =		self.tokenizer(SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)

       if text is None:
            return inputs
       elif audio is None:
            return encodings
       else:
            A:		str          =		encodings['''input_ids''']
            return inputs



  def 					_snake_case     ( self				:							Tuple					,  *SCREAMING_SNAKE_CASE_				:							int					,  **SCREAMING_SNAKE_CASE_				:							Union[str, Any]	)			->		str:







       '''simple docstring'''



       return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							Optional[int]					,  *SCREAMING_SNAKE_CASE_				:							str					,  **SCREAMING_SNAKE_CASE_				:							int	)			->		Union[str, Any]:







       '''simple docstring'''



       return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)




  @contextmanager
  def 					_snake_case     ( self				:							str	)			->		int:







       '''simple docstring'''



       warnings.warn(
           '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
           '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
           '''your audio inputs, or in a separate call.'''	)
       A:		List[Any]          =		True
       A:		List[str]          =		self.tokenizer
       yield
       A:		List[Any]          =		self.feature_extractor
       A:		int          =		False






