

def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       len(UpperCamelCase__ )
							snake_case_     							=       [[0] * n for i in range(UpperCamelCase__ )]
							for i in range(UpperCamelCase__ ):
														snake_case_     							=       y_points[i]

							for i in range(2  ,				UpperCamelCase__ ):
														for j in range(UpperCamelCase__  ,				UpperCamelCase__ ):
																					snake_case_     							=       (
																					    (xa - x_points[j - i + 1]) * q[j][i - 1]
																					    - (xa - x_points[j]) * q[j - 1][i - 1]
																					) / (x_points[j] - x_points[j - i + 1])

							return [q[n - 1][n - 1], q]


if __name__ == "__main__":
					import doctest

					doctest.testmod()







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







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     '''AutoImageProcessor'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     '''AutoTokenizer'''




				def __init__(   self ,  snake_case ,  snake_case			):
											super().__init__(snake_case ,  snake_case			)
											snake_case_     							=       self.image_processor




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

											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:
																		snake_case_     							=       self.tokenizer(snake_case ,  return_tensors=snake_case ,  **snake_case			)

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

											if text is not None and images is not None:
																		snake_case_     							=       image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**snake_case			) ,  tensor_type=snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.batch_decode(*snake_case ,  **snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.decode(*snake_case ,  **snake_case			)




				@property
				def        a				(   self			):
											return ["input_ids", "attention_mask", "pixel_values"]




