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



def  __lowerCamelCase       (      _UpperCamelCase  :  int					):


      '''simple docstring'''


      def is_in_circle(_UpperCamelCase  :  float							,    _UpperCamelCase  :  float					) -> 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(_UpperCamelCase					)					)
      # 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       (      _UpperCamelCase  :  int							,    _UpperCamelCase  :  Callable[[float], float]							,    _UpperCamelCase  :  float = 0.0							,    _UpperCamelCase  :  float = 1.0							,    ):


      '''simple docstring'''


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



def  __lowerCamelCase       (      _UpperCamelCase  :  int							,    _UpperCamelCase  :  float = 0.0							,    _UpperCamelCase  :  float = 1.0					):


      '''simple docstring'''


      def identity_function(_UpperCamelCase  :  float					) -> float:
            return x

      UpperCAmelCase_            =  area_under_curve_estimator(
          _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					)
      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       (      _UpperCamelCase  :  int					):


      '''simple docstring'''


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

      UpperCAmelCase_            =  area_under_curve_estimator(
          _UpperCamelCase							,    _UpperCamelCase							,    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_torch_available,
    is_vision_available,
)


lowercase__  :      Union[str, Any]        =					{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Union[str, Any]        =					["MobileViTFeatureExtractor"]
      lowercase__  :      List[Any]        =					["MobileViTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Dict        =					[
          "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MobileViTForImageClassification",
          "MobileViTForSemanticSegmentation",
          "MobileViTModel",
          "MobileViTPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Optional[int]        =					[
          "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMobileViTForImageClassification",
          "TFMobileViTForSemanticSegmentation",
          "TFMobileViTModel",
          "TFMobileViTPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilevit import MobileViTFeatureExtractor
            from .image_processing_mobilevit import MobileViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevit import (
                MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTForImageClassification,
                MobileViTForSemanticSegmentation,
                MobileViTModel,
                MobileViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilevit import (
                TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileViTForImageClassification,
                TFMobileViTForSemanticSegmentation,
                TFMobileViTModel,
                TFMobileViTPreTrainedModel,
            )


else:
      import sys

      lowercase__  :      int        =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





