

'''simple docstring'''

import warnings

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




class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      str    						=    ["""image_processor""", """tokenizer"""]
     UpperCAmelCase_		:      Optional[Any]    						=    """AutoImageProcessor"""
     UpperCAmelCase_		:      List[str]    						=    """AutoTokenizer"""


     def __init__(						self					:	List[str]	,     lowercase_					:	Dict=None	,     lowercase_					:	Union[str, Any]=None	,     **lowercase_					:	Optional[Any]			)		->      Optional[Any]:
      UpperCAmelCase			:      List[Any]		   =					None
      if "feature_extractor" in kwargs:
       warnings.warn(
           'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
           ' instead.'	,     lowercase_	,     )
       UpperCAmelCase			:      Optional[Any]		   =					kwargs.pop('feature_extractor'			)

      UpperCAmelCase			:      Optional[Any]		   =					image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
       raise ValueError('You have to specify an image_processor.'			)
      if tokenizer is None:
       raise ValueError('You have to specify a tokenizer.'			)

      super().__init__(lowercase_	,     lowercase_			)
      UpperCAmelCase			:      str		   =					self.image_processor


     def __call__(						self					:	Optional[int]	,     lowercase_					:	Dict=None	,     lowercase_					:	str=None	,     lowercase_					:	List[str]=None	,     **lowercase_					:	List[Any]			)		->      int:

      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			:      str		   =					self.tokenizer(lowercase_	,     return_tensors=lowercase_	,     **lowercase_			)

      if images is not None:
       UpperCAmelCase			:      Any		   =					self.image_processor(lowercase_	,     return_tensors=lowercase_	,     **lowercase_			)

      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(**lowercase_			)	,     tensor_type=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Optional[Any]	,     *lowercase_					:	Dict	,     **lowercase_					:	int			)		->      Tuple:
      return self.tokenizer.batch_decode(*lowercase_	,     **lowercase_			)


     def   UpperCAmelCase_			(						self					:	Optional[int]	,     *lowercase_					:	Dict	,     **lowercase_					:	int			)		->      Dict:
      return self.tokenizer.decode(*lowercase_	,     **lowercase_			)


     @property
     def   UpperCAmelCase_			(						self					:	int			)		->      int:
      UpperCAmelCase			:      int		   =					self.tokenizer.model_input_names
      UpperCAmelCase			:      Optional[Any]		   =					self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


     @property
     def   UpperCAmelCase_			(						self					:	Dict			)		->      Union[str, Any]:
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'	,     lowercase_	,     )
      return self.image_processor_class




     @property
     def   UpperCAmelCase_			(						self					:	Optional[Any]			)		->      List[str]:
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'	,     lowercase_	,     )
      return self.image_processor







'''simple docstring'''

from __future__ import annotations

from collections.abc import Callable




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_ = 1_00    ,			):
 UpperCAmelCase			:      Any		   =					x_start
 UpperCAmelCase			:      int		   =					fnc(UpperCAmelCase_      )
 UpperCAmelCase			:      str		   =					0.0

 for _ in range(UpperCAmelCase_      ):
  # Approximates small segments of curve as linear and solve
  # for trapezoidal area
  UpperCAmelCase			:      List[Any]		   =					(x_end - x_start) / steps + xa
  UpperCAmelCase			:      int		   =					fnc(UpperCAmelCase_      )
  area += abs(fxa + fxa      ) * (xa - xa) / 2

  # Increment step
  UpperCAmelCase			:      List[str]		   =					xa
  UpperCAmelCase			:      List[Any]		   =					fxa
 return area


if __name__ == "__main__":




 def  UpperCamelCase(	UpperCAmelCase_      ):
  return x**3

 print("f(x) = x^3")
 print("The area between the curve, x = -10, x = 10 and the x axis is:")
 lowercase__            =     10
 while i <= 100000:
  lowercase__            =     trapezoidal_area(f, -5, 5, i)
  print(f'''with {i} steps: {area}''')
  i *= 10




