

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


class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	['''image_processor''', '''tokenizer''']
 _lowerCAmelCase									=	'''EfficientNetImageProcessor'''
 _lowerCAmelCase									=	('''BertTokenizer''', '''BertTokenizerFast''')




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   super().__init__(_UpperCAmelCase  ,  _UpperCAmelCase							)




 def __call__(			self  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase="max_length"  ,  _UpperCAmelCase=64  ,  _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:
     __UpperCAmelCase	:  Union[str, Any]		=     self.tokenizer(
         _UpperCAmelCase  ,  padding=_UpperCAmelCase  ,  max_length=_UpperCAmelCase  ,  return_tensors=_UpperCAmelCase  ,  **_UpperCAmelCase							)

   if images is not None:
     __UpperCAmelCase	:  Union[str, Any]		=     self.image_processor(_UpperCAmelCase  ,  return_tensors=_UpperCAmelCase  ,  **_UpperCAmelCase							)

   if text is not None and images is not None:
     __UpperCAmelCase	:  List[Any]		=     image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**_UpperCAmelCase							)  ,  tensor_type=_UpperCAmelCase							)




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




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




 @property
 def     a       (			self							):
   __UpperCAmelCase	:  Tuple		=     self.tokenizer.model_input_names
   __UpperCAmelCase	:  List[Any]		=     self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)








'''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




