

from __future__ import annotations

import math
from collections.abc import Callable





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ = 100  ,				):







							'''simple docstring'''






							snake_case_     							=       x_start
							snake_case_     							=       fnc(UpperCamelCase__ )
							snake_case_     							=       0.0

							for _ in range(UpperCamelCase__ ):
														# Approximates curve as a sequence of linear lines and sums their length
														snake_case_     							=       (x_end - x_start) / steps + xa
														snake_case_     							=       fnc(UpperCamelCase__ )
														length += math.hypot(xa - xa  ,				fxa - fxa )

														# Increment step
														snake_case_     							=       xa
														snake_case_     							=       fxa

							return length


if __name__ == "__main__":





					def    __lowerCamelCase      (					UpperCamelCase__ ):







												'''simple docstring'''






												return math.sin(10 * x )

					print("""f(x) = sin(10 * x)""")
					print("""The length of the curve from x = -10 to x = 10 is:""")
					_UpperCAmelCase						:  Optional[int]									=			10
					while i <= 10_0000:
										print(F'''With {i} steps: {line_length(f, -10, 10, i)}''')
										i *= 10







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"]




