






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





def  _a(  UpperCamelCase__				:						Optional[int]							):




						'''simple docstring'''






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




						'''simple docstring'''






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





def  _a(  UpperCamelCase__				:						Any,	UpperCamelCase__				:						int = 0.0,	UpperCamelCase__				:						Any = 1.0							):




						'''simple docstring'''






						def identity_function(UpperCamelCase__				:						Dict							) -> float:
												return x

						SCREAMING_SNAKE_CASE__     :				Tuple									=area_under_curve_estimator(
						    __snake_case,	__snake_case,	__snake_case,	__snake_case							)
						SCREAMING_SNAKE_CASE__     :				int									=(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  _a(  UpperCamelCase__				:						int							):




						'''simple docstring'''






						def function_to_integrate(UpperCamelCase__				:						Union[str, Any]							) -> float:
												return sqrt(4.0 - x * x							)

						SCREAMING_SNAKE_CASE__     :				Tuple									=area_under_curve_estimator(
						    __snake_case,	__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()







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_					= {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= ['DebertaTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DebertaForMaskedLM',
      'DebertaForQuestionAnswering',
      'DebertaForSequenceClassification',
      'DebertaForTokenClassification',
      'DebertaModel',
      'DebertaPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFDebertaForMaskedLM',
      'TFDebertaForQuestionAnswering',
      'TFDebertaForSequenceClassification',
      'TFDebertaForTokenClassification',
      'TFDebertaModel',
      'TFDebertaPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
  from .tokenization_deberta import DebertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_fast import DebertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta import (
        DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
        DebertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta import (
        TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
        TFDebertaPreTrainedModel,
    )


else:
  import sys

  a_					= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)