
'''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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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 (				__lowercase			):
		a							:					Union[str, Any]       =					['pixel_values']




		def __init__(				self					,						UpperCamelCase = True					,						UpperCamelCase = None					,						UpperCamelCase = 0.9					,						UpperCamelCase = PILImageResampling.BICUBIC					,						UpperCamelCase = True					,						UpperCamelCase = None					,						UpperCamelCase = 1 / 255					,						UpperCamelCase = True					,						UpperCamelCase = True					,						UpperCamelCase = None					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								super().__init__(**__A      )
								_SCREAMING_SNAKE_CASE				= size if size is not None else {"shortest_edge": 224}
								_SCREAMING_SNAKE_CASE				= get_size_dict(__A					,						default_to_square=__A      )
								_SCREAMING_SNAKE_CASE				= crop_size if crop_size is not None else {"height": 224, "width": 224}
								_SCREAMING_SNAKE_CASE				= get_size_dict(__A					,						param_name="crop_size"      )

								_SCREAMING_SNAKE_CASE				= do_resize
								_SCREAMING_SNAKE_CASE				= size
								_SCREAMING_SNAKE_CASE				= crop_pct
								_SCREAMING_SNAKE_CASE				= resample
								_SCREAMING_SNAKE_CASE				= do_center_crop
								_SCREAMING_SNAKE_CASE				= crop_size
								_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 IMAGENET_DEFAULT_MEAN
								_SCREAMING_SNAKE_CASE				= image_std if image_std is not None else IMAGENET_DEFAULT_STD




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = None					,						UpperCamelCase = PILImageResampling.BICUBIC					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= get_size_dict(__A					,						default_to_square=__A      )
								if "shortest_edge" not in size and ("height" not in size or "width" not in size):
														raise ValueError(F'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'      )
								if crop_pct is not None:
														if "shortest_edge" in size:
																				_SCREAMING_SNAKE_CASE				= int(size["shortest_edge"] / crop_pct      )
														elif "height" in size and "width" in size:
																				if size["height"] == size["width"]:
																										_SCREAMING_SNAKE_CASE				= int(size["height"] / crop_pct      )
																				else:
																										_SCREAMING_SNAKE_CASE				= (int(size["height"] / crop_pct      ), int(size["width"] / crop_pct      ))
														else:
																				raise ValueError("Invalid size for resize: {}".format(__A      )      )

														_SCREAMING_SNAKE_CASE				= get_resize_output_image_size(__A					,						size=__A					,						default_to_square=__A      )
								else:
														if "shortest_edge" in size:
																				_SCREAMING_SNAKE_CASE				= get_resize_output_image_size(__A					,						size=size["shortest_edge"]					,						default_to_square=__A      )
														elif "height" in size and "width" in size:
																				_SCREAMING_SNAKE_CASE				= (size["height"], size["width"])
														else:
																				raise ValueError("Invalid size for resize: {}".format(__A      )      )

								return resize(__A					,						size=__A					,						resample=__A					,						data_format=__A					,						**__A      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= get_size_dict(__A      )
								if "height" not in size or "width" not in size:
														raise ValueError(F'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'      )
								return center_crop(__A					,						size=(size["height"], size["width"])					,						data_format=__A					,						**__A      )




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




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




		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 = 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				= crop_pct if crop_pct is not None else self.crop_pct
								_SCREAMING_SNAKE_CASE				= resample if resample is not None else self.resample
								_SCREAMING_SNAKE_CASE				= do_center_crop if do_center_crop is not None else self.do_center_crop
								_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				= size if size is not None else self.size
								_SCREAMING_SNAKE_CASE				= get_size_dict(__A					,						default_to_square=__A      )
								_SCREAMING_SNAKE_CASE				= crop_size if crop_size is not None else self.crop_size
								_SCREAMING_SNAKE_CASE				= get_size_dict(__A					,						param_name="crop_size"      )

								_SCREAMING_SNAKE_CASE				= make_list_of_images(__A      )

								if not valid_images(__A      ):
														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_center_crop and crop_pct is None:
														raise ValueError("Crop_pct must be specified if do_center_crop 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."      )

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

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

								if do_center_crop:
														_SCREAMING_SNAKE_CASE				= [self.center_crop(image=__A					,						size=__A      ) for image in images]

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

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

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

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

'''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()