'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class       __lowercase		(    lowercase_    ):







     """simple docstring"""






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







     def __init__(		self  ,      _lowerCamelCase=None  ,      _lowerCamelCase=None  ,      **_lowerCamelCase     ):
         __UpperCamelCase						:						str		     =	None
         if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'  ,      a__  ,      )
             __UpperCamelCase						:						int		     =	kwargs.pop('feature_extractor'     )

         __UpperCamelCase						:						List[Any]		     =	image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'     )
         if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'     )

         super().__init__(a__  ,      a__     )
         __UpperCamelCase						:						Dict		     =	self.image_processor
         __UpperCamelCase						:						List[str]		     =	False







     def __call__(		self  ,      *_lowerCamelCase  ,      **_lowerCamelCase     ):
         # For backward compatibility
         if self._in_target_context_manager:
             return self.current_processor(*a__  ,      **a__     )

         __UpperCamelCase						:						Optional[Any]		     =	kwargs.pop('images'  ,      a__     )
         __UpperCamelCase						:						Tuple		     =	kwargs.pop('text'  ,      a__     )
         if len(a__     ) > 0:
             __UpperCamelCase						:						Tuple		     =	args[0]
             __UpperCamelCase						:						List[Any]		     =	args[1:]

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

         if images is not None:
             __UpperCamelCase						:						Union[str, Any]		     =	self.image_processor(a__  ,      *a__  ,      **a__     )
         if text is not None:
             __UpperCamelCase						:						List[str]		     =	self.tokenizer(a__  ,      **a__     )

         if text is None:
             return inputs
         elif images is None:
             return encodings
         else:
             __UpperCamelCase						:						Union[str, Any]		     =	encodings['input_ids']
             return inputs







     def lowerCAmelCase  (		self  ,      *_lowerCamelCase  ,      **_lowerCamelCase     ):
         return self.tokenizer.batch_decode(*a__  ,      **a__     )







     def lowerCAmelCase  (		self  ,      *_lowerCamelCase  ,      **_lowerCamelCase     ):
         return self.tokenizer.decode(*a__  ,      **a__     )







     @contextmanager
     def lowerCAmelCase  (		self     ):
         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 images inputs, or in a separate call.'     )
         __UpperCamelCase						:						Tuple		     =	True
         __UpperCamelCase						:						Optional[int]		     =	self.tokenizer
         yield
         __UpperCamelCase						:						List[Any]		     =	self.image_processor
         __UpperCamelCase						:						int		     =	False







     @property
     def lowerCAmelCase  (		self     ):
         warnings.warn(
             '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,      a__  ,      )
         return self.image_processor_class







     @property
     def lowerCAmelCase  (		self     ):
         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,      a__  ,      )
         return self.image_processor





import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


_UpperCAmelCase    			=					logging.get_logger(__name__)

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  def __init__(					self	,		*a__	,		**a__		):
    warnings.warn(
        '''The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
        ''' Please use ConditionalDetrImageProcessor instead.'''	,		a__	,		)
    super().__init__(*a__	,		**a__		)




