from __future__ import annotations

import math
from collections.abc import Callable







def       A__   (						snake_case_			:		Union[str, Any]							,					snake_case_			:		str							,					snake_case_			:		int							,					snake_case_			:		Tuple = 100							,					):
 SCREAMING_SNAKE_CASE__: Any=   x_start
 SCREAMING_SNAKE_CASE__: Optional[int]=   fnc(lowercase_   )
 SCREAMING_SNAKE_CASE__: int=   0.0

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

  # Increment step
  SCREAMING_SNAKE_CASE__: List[Any]=   xa
  SCREAMING_SNAKE_CASE__: int=   fxa

 return length


if __name__ == "__main__":







      def       A__   (						snake_case_			:		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:')
      lowercase_  : Dict								=					1_0
      while i <= 1_0_0_0_0_0:
            print(f'''With {i} steps: {line_length(f, -1_0, 1_0, i)}''')
            i *= 1_0








"""simple docstring"""


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



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       ["image_processor", "tokenizer"]
  SCREAMING_SNAKE_CASE__				:Dict						  =       "AutoImageProcessor"
  SCREAMING_SNAKE_CASE__				:Optional[int]						  =       "AutoTokenizer"

  def __init__(		self :							Dict       , __a :							Tuple       , __a :							str						)		->					Optional[Any]:
        super().__init__(__a       , __a						)
        _UpperCamelCase   :						Tuple    =							self.image_processor

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

        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:
              _UpperCamelCase   :						List[str]    =							self.tokenizer(__a       , return_tensors=__a       , **__a						)

        if images is not None:
              _UpperCamelCase   :						str    =							self.image_processor(__a       , return_tensors=__a       , **__a						)

        if text is not None and images is not None:
              _UpperCamelCase   :						Optional[int]    =							image_features.pixel_values
              return encoding
        elif text is not None:
              return encoding
        else:
              return BatchEncoding(data=dict(**__a						)       , tensor_type=__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , *__a :							Optional[int]       , **__a :							Optional[int]						)		->					List[Any]:
        return self.tokenizer.batch_decode(*__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							int       , *__a :							Any       , **__a :							List[Any]						)		->					Tuple:
        return self.tokenizer.decode(*__a       , **__a						)


  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Any:
        return ["input_ids", "attention_mask", "pixel_values"]
