
from __future__ import annotations

import math
from collections.abc import Callable




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Callable[[int | float], int | float]		,   SCREAMING_SNAKE_CASE_	:	int | float		,   SCREAMING_SNAKE_CASE_	:	int | float		,   SCREAMING_SNAKE_CASE_	:	int = 1_00		,   )  ->     float:
						_lowercase					       =					x_start
						_lowercase					       =					fnc(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					0.0

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

												# Increment step
												_lowercase					       =					xa
												_lowercase					       =					fxa

						return length


if __name__ == "__main__":




		def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     List[Any]:
								return math.sin(10 * x       )

		print('''f(x) = sin(10 * x)''')
		print('''The length of the curve from x = -10 to x = 10 is:''')
		A		: Any								=     10
		while i <= 100_000:
				print(F'With {i} steps: {line_length(f, -10, 10, i)}')
				i *= 10

import warnings

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



class    a_ (			_a    ):
			a	:			Optional[Any]					   =							['''image_processor''', '''tokenizer''']
			a	:			Optional[Any]					   =							'''ChineseCLIPImageProcessor'''
			a	:			List[str]					   =							('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):
									_lowercase					       =					None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""				,			__UpperCamelCase				,			)
															_lowercase					       =					kwargs.pop("""feature_extractor"""  )

									_lowercase					       =					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__(__UpperCamelCase				,			__UpperCamelCase  )
									_lowercase					       =					self.image_processor






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

									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:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if images is not None:
															_lowercase					       =					self.image_processor(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

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






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			@property
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.tokenizer.model_input_names
									_lowercase					       =					self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )






			@property
			def    UpperCamelCase_				(      self  ):
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,			__UpperCamelCase				,			)
									return self.image_processor_class