


def 						lowerCamelCase(   a__	,a__ = " "):
				_SCREAMING_SNAKE_CASE			  =[]

				_SCREAMING_SNAKE_CASE			  =0
				for index, char in enumerate(a__):
								if char == separator:
												split_words.append(string[last_index:index])
												_SCREAMING_SNAKE_CASE			  =index + 1
								elif index + 1 == len(a__):
												split_words.append(string[last_index : index + 1])
				return split_words


if __name__ == "__main__":
							from doctest import testmod

							testmod()



import warnings

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

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						["image_processor", "tokenizer"]
						UpperCAmelCase             =						"ViTImageProcessor"
						UpperCAmelCase             =						("CLIPTokenizer", "CLIPTokenizerFast")




						def __init__(       self					:      int				,    _a					:      int=None				,    _a					:      Optional[int]=None				,    **_a					:      str  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =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				,    )
														_SCREAMING_SNAKE_CASE			  =kwargs.pop('''feature_extractor'''  )

										_SCREAMING_SNAKE_CASE			  =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  )




						def __call__(       self					:      List[str]				,    _a					:      Tuple=None				,    _a					:      Optional[Any]=None				,    _a					:      List[Any]=None				,    _a					:      List[Any]=None				,    **_a					:      List[Any]  )							->     Optional[Any]:


										"""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:
														_SCREAMING_SNAKE_CASE			  =self.tokenizer(_a				,    return_tensors=_a				,    **_a  )

										if visual_prompt is not None:
														_SCREAMING_SNAKE_CASE			  =self.image_processor(_a				,    return_tensors=_a				,    **_a  )

										if images is not None:
														_SCREAMING_SNAKE_CASE			  =self.image_processor(_a				,    return_tensors=_a				,    **_a  )

										if visual_prompt is not None and images is not None:
														_SCREAMING_SNAKE_CASE			  ={
														    '''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:
														_SCREAMING_SNAKE_CASE			  =image_features.pixel_values
														return encoding
										elif text is not None:
														return encoding
										elif visual_prompt is not None:
														_SCREAMING_SNAKE_CASE			  ={
														    '''conditional_pixel_values''': prompt_features.pixel_values,
														}
														return encoding
										else:
														return BatchEncoding(data=dict(**_a  )				,    tensor_type=_a  )




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


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Dict				,    *_a					:      str				,    **_a					:      List[Any]  )							->     List[Any]:


										"""simple docstring"""





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




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


										"""simple docstring"""





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


										"""simple docstring"""





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