






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






class 	lowercase_      (			enum.Enum     ):






      '''simple docstring'''




      __snake_case  						=						'''timm'''
      __snake_case  						=						'''transformers'''
def 		_a					(   a				:Optional[Iterable[str]]			,							a				:Optional[Iterable[int]]			,							a				:Optional[Iterable[str]]				)							->    Optional[Any]:
 if stage_names is None:
  raise ValueError('''Stage_names must be set for transformers backbones'''				)

 if out_features is not None:
  if not isinstance(a			,							(list,)				):
   raise ValueError(F"""out_features must be a list {type(a				)}"""				)
  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(a			,							(list, tuple)				):
   raise ValueError(F"""out_indices must be a list or tuple, got {type(a				)}"""				)
  if any(idx >= len(a				) 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(a				) != len(a				):
   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					(   a				:Optional[List[str]]			,							a				:Optional[Union[List[int], Tuple[int]]]			,							a				:List[str]			,							)							->    Union[str, Any]:
 if out_indices is None and out_features is None:
  a							=   [len(a				) - 1]
  a							=   [stage_names[-1]]
 elif out_indices is None and out_features is not None:
  a							=   [stage_names.index(a				) for layer in out_features]
 elif out_features is None and out_indices is not None:
  a							=   [stage_names[idx] for idx in out_indices]
 return out_features, out_indices
def 		_a					(   a				:Optional[List[str]]			,							a				:Optional[Union[List[int], Tuple[int]]]			,							a				:List[str]			,							)							->    Tuple[List[str], List[int]]:
 verify_out_features_out_indices(out_features=a			,							out_indices=a			,							stage_names=a				)
 a						,  a							=   _align_output_features_output_indices(
     out_features=a			,							out_indices=a			,							stage_names=a				)
 # Verify that the aligned out_features and out_indices are valid
 verify_out_features_out_indices(out_features=a			,							out_indices=a			,							stage_names=a				)
 return output_features, output_indices






class 	lowercase_      :






      '''simple docstring'''




      __snake_case  						=						None







      def    __lowerCAmelCase      (   self   :		Any				,      __UpperCAmelCase   :		Tuple						)					->None:




       """simple docstring"""


       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']
       a							=   [stage['''module'''] for stage in self._backbone.feature_info.info]
       a							=   [stage['''num_chs'''] for stage in self._backbone.feature_info.info]
       a							=   self._backbone.feature_info.out_indices
       a							=   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						)
       a						,  a							=   out_features, out_indices







      def    __lowerCAmelCase      (   self   :		Any				,      __UpperCAmelCase   :		Tuple						)					->None:




       """simple docstring"""


       a							=   getattr(__UpperCAmelCase				,      '''stage_names'''						)
       a							=   getattr(__UpperCAmelCase				,      '''out_features'''				,      __UpperCAmelCase						)
       a							=   getattr(__UpperCAmelCase				,      '''out_indices'''				,      __UpperCAmelCase						)

       a							=   stage_names
       a						,  a							=   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
       a							=   None







      def    __lowerCAmelCase      (   self   :		str				,      __UpperCAmelCase   :		Union[str, Any]						)					->None:




       """simple docstring"""


       a							=   config

       a							=   getattr(__UpperCAmelCase				,      '''use_timm_backbone'''				,      __UpperCAmelCase						)
       a							=   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    __lowerCAmelCase      (   self   :		Dict						)					->Optional[Any]:




       """simple docstring"""


       return self._out_features







      @out_features.setter
      def    __lowerCAmelCase      (   self   :		Tuple				,      __UpperCAmelCase   :		List[str]						)					->List[Any]:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







      @property
      def    __lowerCAmelCase      (   self   :		Dict						)					->int:




       """simple docstring"""


       return self._out_indices







      @out_indices.setter
      def    __lowerCAmelCase      (   self   :		Tuple				,      __UpperCAmelCase   :		Union[Tuple[int], List[int]]						)					->List[str]:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







      @property
      def    __lowerCAmelCase      (   self   :		Tuple						)					->str:




       """simple docstring"""


       return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names						)}







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




       """simple docstring"""


       return [self.out_feature_channels[name] for name in self.out_features]







      def    __lowerCAmelCase      (   self   :		Optional[Any]				,      *__UpperCAmelCase   :		List[str]				,      **__UpperCAmelCase   :		Optional[Any]						)					->Optional[int]:




       """simple docstring"""


       a							=   dict(inspect.signature(self.forward						).parameters						)
       a							=   {k: v for k, v in kwargs.items() if k in signature}
       return self(*__UpperCAmelCase				,      **__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		List[str]				,      __UpperCAmelCase   :		Dict				,      __UpperCAmelCase   :		Optional[bool] = None				,      __UpperCAmelCase   :		Optional[bool] = None				,      __UpperCAmelCase   :		Optional[bool] = None				,      )					->int:




       """simple docstring"""


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







      def    __lowerCAmelCase      (   self   :		Union[str, Any]						)					->Optional[Any]:




       """simple docstring"""


       a							=   super().to_dict()
       a							=   output.pop('''_out_features'''						)
       a							=   output.pop('''_out_indices'''						)
       return output






class 	lowercase_      :






      '''simple docstring'''




      @property
      def    __lowerCAmelCase      (   self   :		List[str]						)					->Optional[int]:




       """simple docstring"""


       return self._out_features







      @out_features.setter
      def    __lowerCAmelCase      (   self   :		int				,      __UpperCAmelCase   :		List[str]						)					->int:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







      @property
      def    __lowerCAmelCase      (   self   :		str						)					->str:




       """simple docstring"""


       return self._out_indices







      @out_indices.setter
      def    __lowerCAmelCase      (   self   :		str				,      __UpperCAmelCase   :		Union[Tuple[int], List[int]]						)					->int:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







      def    __lowerCAmelCase      (   self   :		Dict						)					->List[Any]:




       """simple docstring"""


       a							=   super().to_dict()
       a							=   output.pop('''_out_features'''						)
       a							=   output.pop('''_out_indices'''						)
       return output










import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 	lowercase_      (			lowercase     ):






      '''simple docstring'''




      __snake_case  						=						['''image_processor''', '''tokenizer''']
      __snake_case  						=						'''AutoImageProcessor'''
      __snake_case  						=						'''AutoTokenizer'''







      def __init__(   self   :		Any				,      __UpperCAmelCase   :		Union[str, Any]=None				,      __UpperCAmelCase   :		int=None				,      **__UpperCAmelCase   :		Union[str, Any]						)					->Dict:




       """simple docstring"""


       a							=   None
       if "feature_extractor" in kwargs:
        warnings.warn(
            '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
            ''' instead.'''				,      __UpperCAmelCase				,      )
        a							=   kwargs.pop('''feature_extractor'''						)

       a							=   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
        raise ValueError('''You have to specify an image_processor.'''						)
       if tokenizer is None:
        raise ValueError('''You have to specify a tokenizer.'''						)

       super().__init__(__UpperCAmelCase				,      __UpperCAmelCase						)
       a							=   self.image_processor







      def __call__(   self   :		List[Any]				,      __UpperCAmelCase   :		Optional[Any]=None				,      __UpperCAmelCase   :		Optional[int]=None				,      __UpperCAmelCase   :		str=None				,      **__UpperCAmelCase   :		List[Any]						)					->Dict:




       """simple docstring"""


       if text is None and images is None:
        raise ValueError('''You have to specify either text or images. Both cannot be none.'''						)

       if text is not None:
        a							=   self.tokenizer(__UpperCAmelCase				,      return_tensors=__UpperCAmelCase				,      **__UpperCAmelCase						)

       if images is not None:
        a							=   self.image_processor(__UpperCAmelCase				,      return_tensors=__UpperCAmelCase				,      **__UpperCAmelCase						)

       if text is not None and images is not None:
        a							=   image_features.pixel_values
        return encoding
       elif text is not None:
        return encoding
       else:
        return BatchEncoding(data=dict(**__UpperCAmelCase						)				,      tensor_type=__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		List[Any]				,      *__UpperCAmelCase   :		Optional[Any]				,      **__UpperCAmelCase   :		List[str]						)					->Optional[Any]:




       """simple docstring"""


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







      def    __lowerCAmelCase      (   self   :		str				,      *__UpperCAmelCase   :		List[Any]				,      **__UpperCAmelCase   :		Optional[Any]						)					->Union[str, Any]:




       """simple docstring"""


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







      @property
      def    __lowerCAmelCase      (   self   :		str						)					->str:




       """simple docstring"""


       a							=   self.tokenizer.model_input_names
       a							=   self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)







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




       """simple docstring"""


       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,      __UpperCAmelCase				,      )
       return self.image_processor_class







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




       """simple docstring"""


       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,      __UpperCAmelCase				,      )
       return self.image_processor


