code: 
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  
  
  
  
  
  
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			Union[str, Any]												=     ['image_processor', 'tokenizer']
     lowerCamelCase__				:			str												=     'AutoImageProcessor'
     lowerCamelCase__				:			Optional[int]												=     'AutoTokenizer'
  
  
  
     def __init__(       self,					lowercase_=None,					lowercase_=None,					**lowercase_ )							->					Tuple:
  
  
  
  
  
  
       """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.''',					lowercase_,					)
         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 need to specify an `image_processor`.''' )
       if tokenizer is None:
         raise ValueError('''You need to specify a `tokenizer`.''' )
  
       super().__init__(lowercase_,					lowercase_ )
       a__						=self.image_processor
       a__						=False
  
  
  
     def __call__(       self,					*lowercase_,					**lowercase_ )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if self._in_target_context_manager:
         return self.current_processor(*lowercase_,					**lowercase_ )
  
       a__						=kwargs.pop('''images''',					lowercase_ )
       a__						=kwargs.pop('''text''',					lowercase_ )
       if len(lowercase_ ) > 0:
         a__						=args[0]
         a__						=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:
         a__						=self.image_processor(lowercase_,					*lowercase_,					**lowercase_ )
       if text is not None:
         a__						=self.tokenizer(lowercase_,					**lowercase_ )
  
       if text is None:
         return inputs
       elif images is None:
         return encodings
       else:
         a__						=encodings['''input_ids''']
         return inputs
  
  
  
     def 				_UpperCAmelCase				(       self,					*lowercase_,					**lowercase_ )							->					List[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return self.tokenizer.batch_decode(*lowercase_,					**lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self,					*lowercase_,					**lowercase_ )							->					List[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return self.tokenizer.decode(*lowercase_,					**lowercase_ )
  
  
  
     @contextmanager
     def 				_UpperCAmelCase				(       self )							->					Union[str, Any]:
  
  
  
  
  
  
       """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 images inputs, or in a separate call.''' )
       a__						=True
       a__						=self.tokenizer
       yield
       a__						=self.image_processor
       a__						=False
  
  
  
     @property
     def 				_UpperCAmelCase				(       self )							->					List[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''',					lowercase_,					)
       return self.image_processor_class
  
  
  
     @property
     def 				_UpperCAmelCase				(       self )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''',					lowercase_,					)
       return self.image_processor
  
style_context: 
  
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  
  
  @dataclass
  # Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			Union[List[PIL.Image.Image], np.ndarray]
     lowerCamelCase__				:			Optional[List[bool]]
  
  
  if is_transformers_available() and is_torch_available():
         from .modeling_roberta_series import RobertaSeriesModelWithTransformation
         from .pipeline_alt_diffusion import AltDiffusionPipeline
         from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
  