from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean




def   lowerCAmelCase_					(	__A				)	->							str:







      '''simple docstring'''


      def is_in_circle(__A,    __A				) -> bool:
            UpperCAmelCase__								=    sqrt((x**2) + (y**2)				)
            # Our circle has a radius of 1, so a distance
            # greater than 1 would land outside the circle.
            return distance_from_centre <= 1

      # The proportion of guesses that landed in the circle
      UpperCAmelCase__								=    mean(
          int(is_in_circle(uniform(-1.0,    1.0				),    uniform(-1.0,    1.0				)				)				)
          for _ in range(a__				)				)
      # The ratio of the area for circle to square is pi/4.
      UpperCAmelCase__								=    proportion * 4
      print(f"""The estimated value of pi is {pi_estimate}"""				)
      print(f"""The numpy value of pi is {pi}"""				)
      print(f"""The total error is {abs(pi - pi_estimate				)}"""				)




def   lowerCAmelCase_					(	__A,    __A,    __A = 0.0,    __A = 1.0,    )	->							str:







      '''simple docstring'''


      return mean(
          function_to_integrate(uniform(a__,    a__				)				) for _ in range(a__				)				) * (max_value - min_value)




def   lowerCAmelCase_					(	__A,    __A = 0.0,    __A = 1.0				)	->							Optional[Any]:







      '''simple docstring'''


      def identity_function(__A				) -> float:
            return x

      UpperCAmelCase__								=    area_under_curve_estimator(
          a__,    a__,    a__,    a__				)
      UpperCAmelCase__								=    (max_value * max_value - min_value * min_value) / 2

      print("******************"				)
      print(f"""Estimating area under y=x where x varies from {min_value} to {max_value}"""				)
      print(f"""Estimated value is {estimated_value}"""				)
      print(f"""Expected value is {expected_value}"""				)
      print(f"""Total error is {abs(estimated_value - expected_value				)}"""				)
      print("******************"				)




def   lowerCAmelCase_					(	__A				)	->							str:







      '''simple docstring'''


      def function_to_integrate(__A				) -> float:
            return sqrt(4.0 - x * x				)

      UpperCAmelCase__								=    area_under_curve_estimator(
          a__,    a__,    0.0,    2.0				)

      print("******************"				)
      print("Estimating pi using area_under_curve_estimator"				)
      print(f"""Estimated value is {estimated_value}"""				)
      print(f"""Expected value is {pi}"""				)
      print(f"""Total error is {abs(estimated_value - pi				)}"""				)
      print("******************"				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






'''simple docstring'''



import warnings

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







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "CLIPImageProcessor"
 lowerCAmelCase__       = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
 def __init__(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			str=None   ,				**__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					Dict:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE      = 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__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					int:


      """simple docstring"""




      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:
           __SCREAMING_SNAKE_CASE      = self.tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

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

      if text is not None and images is not None:
           __SCREAMING_SNAKE_CASE      = image_features.pixel_values
           return encoding
      elif text is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE     )   ,				tensor_type=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[str]   ,				*__SCREAMING_SNAKE_CASE   :			Tuple   ,				**__SCREAMING_SNAKE_CASE   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]   ,				*__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					Optional[Any]:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )


 @property
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.tokenizer.model_input_names
      __SCREAMING_SNAKE_CASE      = self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )






