from math import ceil


def  _lowerCamelCase   (				lowerCamelCase_ :							List[str]     ,					lowerCamelCase_ :							Tuple       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[int]       =				list(range(0     ,					lowerCamelCase_       )       )

     UpperCAmelCase_							:       Optional[Any]       =				[item for sublist in list(device_map.values()       ) for item in sublist]

     # Duplicate check
     UpperCAmelCase_							:       Union[str, Any]       =				[]
     for i in device_map_blocks:
          if device_map_blocks.count(lowerCamelCase_       ) > 1 and i not in duplicate_blocks:
               duplicate_blocks.append(lowerCamelCase_       )
    # Missing blocks
     UpperCAmelCase_							:       List[Any]       =				[i for i in blocks if i not in device_map_blocks]
     UpperCAmelCase_							:       Optional[int]       =				[i for i in device_map_blocks if i not in blocks]

     if len(lowerCamelCase_       ) != 0:
          raise ValueError(
              'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
              ' These attention blocks were specified more than once: ' + str(lowerCamelCase_       )       )
     if len(lowerCamelCase_       ) != 0:
          raise ValueError(
              'There are attention blocks for this model that are not specified in the device_map. Add these attention '
              'blocks to a device on the device_map: ' + str(lowerCamelCase_       )       )
     if len(lowerCamelCase_       ) != 0:
          raise ValueError(
              'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
              + str(lowerCamelCase_       )       )





def  _lowerCamelCase   (				lowerCamelCase_ :							int     ,					lowerCamelCase_ :							Any       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[Any]       =				list(range(lowerCamelCase_       )       )
     UpperCAmelCase_							:       List[str]       =				int(ceil(n_layers / len(lowerCamelCase_       )       )       )
     UpperCAmelCase_							:       Union[str, Any]       =				[layers[i : i + n_blocks] for i in range(0     ,					lowerCamelCase_     ,					lowerCamelCase_       )]

     return dict(zip(lowerCamelCase_     ,					lowerCamelCase_       )       )







'''simple docstring'''


import warnings

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






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						   =		['''image_processor''', '''tokenizer''']
  lowerCamelCase_		:Optional[Any]						   =		'''ViTImageProcessor'''
  lowerCamelCase_		:int						   =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')


  def __init__(			self				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

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

       UpperCAmelCase_							:       str       =				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__(snake_case_				,		snake_case_						)


  def __call__(			self				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       if text is None and visual_prompt is None and images is None:
            raise ValueError('You have to specify either text, visual prompt or images.'						)

       if text is not None and visual_prompt is not None:
            raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'						)

       if text is not None:
            UpperCAmelCase_							:       Optional[int]       =				self.tokenizer(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None:
            UpperCAmelCase_							:       Optional[Any]       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if images is not None:
            UpperCAmelCase_							:       int       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None and images is not None:
            UpperCAmelCase_							:       Tuple       =				{
                'pixel_values': image_features.pixel_values,
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
       elif text is not None and images is not None:
            UpperCAmelCase_							:       Optional[int]       =				image_features.pixel_values
            return encoding
       elif text is not None:
            return encoding
       elif visual_prompt is not None:
            UpperCAmelCase_							:       Dict       =				{
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
       else:
            return BatchEncoding(data=dict(**snake_case_						)				,		tensor_type=snake_case_						)


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

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


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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






  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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






