
'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


_snake_case				:   Optional[int]     =				logging.get_logger(__name__)


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					List[str]       =					["""pixel_values"""]




		def __init__(				self					,						UpperCamelCase = True					,						UpperCamelCase = None					,						UpperCamelCase = PILImageResampling.BICUBIC					,						UpperCamelCase = True					,						UpperCamelCase = 1 / 255					,						UpperCamelCase = True					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = True					,						**UpperCamelCase					,						):
								super().__init__(**_UpperCAmelCase      )
								_SCREAMING_SNAKE_CASE				= size if size is not None else {"height": 384, "width": 384}
								_SCREAMING_SNAKE_CASE				= get_size_dict(_UpperCAmelCase					,						default_to_square=_UpperCAmelCase      )

								_SCREAMING_SNAKE_CASE				= do_resize
								_SCREAMING_SNAKE_CASE				= size
								_SCREAMING_SNAKE_CASE				= resample
								_SCREAMING_SNAKE_CASE				= do_rescale
								_SCREAMING_SNAKE_CASE				= rescale_factor
								_SCREAMING_SNAKE_CASE				= do_normalize
								_SCREAMING_SNAKE_CASE				= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								_SCREAMING_SNAKE_CASE				= image_std if image_std is not None else OPENAI_CLIP_STD
								_SCREAMING_SNAKE_CASE				= do_convert_rgb




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = PILImageResampling.BICUBIC					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= get_size_dict(_UpperCAmelCase					,						default_to_square=_UpperCAmelCase      )
								if "height" not in size or "width" not in size:
														raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'      )
								_SCREAMING_SNAKE_CASE				= (size["height"], size["width"])
								return resize(_UpperCAmelCase					,						size=_UpperCAmelCase					,						resample=_UpperCAmelCase					,						data_format=_UpperCAmelCase					,						**_UpperCAmelCase      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								return rescale(_UpperCAmelCase					,						scale=_UpperCAmelCase					,						data_format=_UpperCAmelCase					,						**_UpperCAmelCase      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								return normalize(_UpperCAmelCase					,						mean=_UpperCAmelCase					,						std=_UpperCAmelCase					,						data_format=_UpperCAmelCase					,						**_UpperCAmelCase      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = None					,						UpperCamelCase = ChannelDimension.FIRST					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= do_resize if do_resize is not None else self.do_resize
								_SCREAMING_SNAKE_CASE				= resample if resample is not None else self.resample
								_SCREAMING_SNAKE_CASE				= do_rescale if do_rescale is not None else self.do_rescale
								_SCREAMING_SNAKE_CASE				= rescale_factor if rescale_factor is not None else self.rescale_factor
								_SCREAMING_SNAKE_CASE				= do_normalize if do_normalize is not None else self.do_normalize
								_SCREAMING_SNAKE_CASE				= image_mean if image_mean is not None else self.image_mean
								_SCREAMING_SNAKE_CASE				= image_std if image_std is not None else self.image_std
								_SCREAMING_SNAKE_CASE				= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								_SCREAMING_SNAKE_CASE				= size if size is not None else self.size
								_SCREAMING_SNAKE_CASE				= get_size_dict(_UpperCAmelCase					,						default_to_square=_UpperCAmelCase      )

								_SCREAMING_SNAKE_CASE				= make_list_of_images(_UpperCAmelCase      )

								if not valid_images(_UpperCAmelCase      ):
														raise ValueError(
														    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
														    "torch.Tensor, tf.Tensor or jax.ndarray."      )

								if do_resize and size is None or resample is None:
														raise ValueError("Size and resample must be specified if do_resize is True."      )

								if do_rescale and rescale_factor is None:
														raise ValueError("Rescale factor must be specified if do_rescale is True."      )

								if do_normalize and (image_mean is None or image_std is None):
														raise ValueError("Image mean and std must be specified if do_normalize is True."      )

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
														_SCREAMING_SNAKE_CASE				= [convert_to_rgb(_UpperCAmelCase      ) for image in images]

								# All transformations expect numpy arrays.
								_SCREAMING_SNAKE_CASE				= [to_numpy_array(_UpperCAmelCase      ) for image in images]

								if do_resize:
														_SCREAMING_SNAKE_CASE				= [self.resize(image=_UpperCAmelCase					,						size=_UpperCAmelCase					,						resample=_UpperCAmelCase      ) for image in images]

								if do_rescale:
														_SCREAMING_SNAKE_CASE				= [self.rescale(image=_UpperCAmelCase					,						scale=_UpperCAmelCase      ) for image in images]

								if do_normalize:
														_SCREAMING_SNAKE_CASE				= [self.normalize(image=_UpperCAmelCase					,						mean=_UpperCAmelCase					,						std=_UpperCAmelCase      ) for image in images]

								_SCREAMING_SNAKE_CASE				= [to_channel_dimension_format(_UpperCAmelCase					,						_UpperCAmelCase      ) for image in images]

								_SCREAMING_SNAKE_CASE				= BatchFeature(data={"pixel_values": images}					,						tensor_type=_UpperCAmelCase      )

								return encoded_outputs

'''simple docstring'''







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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):

						# A local function to see if a dot lands in the circle.
						def is_in_circle(_SCREAMING_SNAKE_CASE		:   float	,			_SCREAMING_SNAKE_CASE		:   float  ) -> bool:
												_SCREAMING_SNAKE_CASE				= 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				= 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				= 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		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   Callable[[float], float]	,			_SCREAMING_SNAKE_CASE		:   float = 0.0	,			_SCREAMING_SNAKE_CASE		:   float = 1.0	,			):
						return mean(
						    function_to_integrate(uniform(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )  ) for _ in range(_SCREAMING_SNAKE_CASE  )  ) * (max_value - min_value)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   float = 0.0	,			_SCREAMING_SNAKE_CASE		:   float = 1.0  ):

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

						_SCREAMING_SNAKE_CASE				= area_under_curve_estimator(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= (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		(	_SCREAMING_SNAKE_CASE		:   int  ):

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

						_SCREAMING_SNAKE_CASE				= 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()