






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


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  def is_in_circle(lowercase						,     lowercase				) -> bool:
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    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
  SCREAMING_SNAKE_CASE       :      Tuple							=    mean(
      int(is_in_circle(uniform(-1.0						,     1.0				)						,     uniform(-1.0						,     1.0				)				)				)
      for _ in range(_SCREAMING_SNAKE_CASE				)				)
  # The ratio of the area for circle to square is pi/4.
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    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__			(     lowercase						,     lowercase						,     lowercase = 0.0						,     lowercase = 1.0						,     ):

  """simple docstring"""






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


def   lowerCamelCase__			(     lowercase						,     lowercase = 0.0						,     lowercase = 1.0				):

  """simple docstring"""






  def identity_function(lowercase				) -> float:
    return x

  SCREAMING_SNAKE_CASE       :      List[str]							=    area_under_curve_estimator(
      _SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE				)
  SCREAMING_SNAKE_CASE       :      str							=    (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__			(     lowercase				):

  """simple docstring"""






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

  SCREAMING_SNAKE_CASE       :      str							=    area_under_curve_estimator(
      _SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE						,     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()














from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Dict			=	'''camembert'''


     def __init__(				self       :	str					, UpperCAmelCase_       :	Optional[Any]=3_0522					, UpperCAmelCase_       :	Optional[int]=768					, UpperCAmelCase_       :	List[Any]=12					, UpperCAmelCase_       :	Union[str, Any]=12					, UpperCAmelCase_       :	Optional[Any]=3072					, UpperCAmelCase_       :	Optional[int]="gelu"					, UpperCAmelCase_       :	List[Any]=0.1					, UpperCAmelCase_       :	Dict=0.1					, UpperCAmelCase_       :	List[str]=512					, UpperCAmelCase_       :	List[Any]=2					, UpperCAmelCase_       :	Optional[int]=0.02					, UpperCAmelCase_       :	List[str]=1E-12					, UpperCAmelCase_       :	int=1					, UpperCAmelCase_       :	Union[str, Any]=0					, UpperCAmelCase_       :	List[str]=2					, UpperCAmelCase_       :	int="absolute"					, UpperCAmelCase_       :	Tuple=True					, UpperCAmelCase_       :	Optional[Any]=None					, **UpperCAmelCase_       :	Optional[int]					, ):
       super().__init__(pad_token_id=UpperCAmelCase_					, bos_token_id=UpperCAmelCase_					, eos_token_id=UpperCAmelCase_					, **UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    vocab_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    hidden_size
       SCREAMING_SNAKE_CASE       :      Any							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Dict							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      Dict							=    hidden_act
       SCREAMING_SNAKE_CASE       :      Tuple							=    intermediate_size
       SCREAMING_SNAKE_CASE       :      Any							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      int							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      Dict							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      List[Any]							=    type_vocab_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    initializer_range
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    layer_norm_eps
       SCREAMING_SNAKE_CASE       :      Dict							=    position_embedding_type
       SCREAMING_SNAKE_CASE       :      List[Any]							=    use_cache
       SCREAMING_SNAKE_CASE       :      str							=    classifier_dropout





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     @property
     def 				_A       (				self       :	Optional[int]      ):
       if self.task == "multiple-choice":
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {0: "batch", 1: "choice", 2: "sequence"}
       else:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]      )






