

import warnings

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

class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Optional[int]             =       ["image_processor", "tokenizer"]
							UpperCAmelCase__			:		List[Any]             =       "CLIPImageProcessor"
							UpperCAmelCase__			:		List[str]             =       ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")


							def __init__(     self			,				_a=None			,				_a=None			,				**_a			)       ->       Tuple:
												_a      :       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.'''			,				_a			,				)
																	_a      :       int   =						kwargs.pop('''feature_extractor'''			)

												_a      :       Union[str, 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			)


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

												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      :       Optional[Any]   =						self.tokenizer(_a			,				return_tensors=_a			,				**_a			)

												if images is not None:
																	_a      :       Optional[int]   =						self.image_processor(_a			,				return_tensors=_a			,				**_a			)

												if text is not None and images is not None:
																	_a      :       List[Any]   =						image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a			)			,				tensor_type=_a			)


							def 							__lowercase							(     self			,				*_a			,				**_a			)       ->       Any:
												return self.tokenizer.batch_decode(*_a			,				**_a			)


							def 							__lowercase							(     self			,				*_a			,				**_a			)       ->       List[Any]:
												return self.tokenizer.decode(*_a			,				**_a			)




							@property
							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       int   =						self.tokenizer.model_input_names
												_a      :       str   =						self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)











def 	UpperCamelCase	(							snake_case__  : list	):




     '''simple docstring'''






     if not grid or not grid[0]:
          raise TypeError("""The grid does not contain the appropriate information"""	)

     for cell_n in range(1						,len(grid[0]	)	):
          grid[0][cell_n] += grid[0][cell_n - 1]
     __snake_case      :str 							=    grid[0]

     for row_n in range(1						,len(snake_case__	)	):
          __snake_case      :Optional[int] 							=    grid[row_n]
          __snake_case      :Optional[Any] 							=    fill_row(snake_case__						,snake_case__	)
          __snake_case      :Dict 							=    grid[row_n]

     return grid[-1][-1]






def 	UpperCamelCase	(							snake_case__  : list						,snake_case__  : list	):




     '''simple docstring'''






     current_row[0] += row_above[0]
     for cell_n in range(1						,len(snake_case__	)	):
          current_row[cell_n] += min(current_row[cell_n - 1]						,row_above[cell_n]	)

     return current_row


if __name__ == "__main__":
     import doctest

     doctest.testmod()


