"""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


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						["""pixel_values"""]

				def __init__(				self		,    lowerCAmelCase = True		,    lowerCAmelCase = None		,    lowerCAmelCase = PILImageResampling.BICUBIC		,    lowerCAmelCase = True		,    lowerCAmelCase = 1 / 2_55		,    lowerCAmelCase = True		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = True		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase    )
										snake_case								=			size if size is not None else {'height': 3_84, 'width': 3_84}
										snake_case								=			get_size_dict(lowerCAmelCase		,    default_to_square=lowerCAmelCase    )

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

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = PILImageResampling.BICUBIC		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			get_size_dict(lowerCAmelCase		,    default_to_square=lowerCAmelCase    )
										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()}"""    )
										snake_case								=			(size['height'], size['width'])
										return resize(lowerCAmelCase		,    size=lowerCAmelCase		,    resample=lowerCAmelCase		,    data_format=lowerCAmelCase		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										return rescale(lowerCAmelCase		,    scale=lowerCAmelCase		,    data_format=lowerCAmelCase		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										return normalize(lowerCAmelCase		,    mean=lowerCAmelCase		,    std=lowerCAmelCase		,    data_format=lowerCAmelCase		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = ChannelDimension.FIRST		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			do_resize if do_resize is not None else self.do_resize
										snake_case								=			resample if resample is not None else self.resample
										snake_case								=			do_rescale if do_rescale is not None else self.do_rescale
										snake_case								=			rescale_factor if rescale_factor is not None else self.rescale_factor
										snake_case								=			do_normalize if do_normalize is not None else self.do_normalize
										snake_case								=			image_mean if image_mean is not None else self.image_mean
										snake_case								=			image_std if image_std is not None else self.image_std
										snake_case								=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										snake_case								=			size if size is not None else self.size
										snake_case								=			get_size_dict(lowerCAmelCase		,    default_to_square=lowerCAmelCase    )

										snake_case								=			make_list_of_images(lowerCAmelCase    )

										if not valid_images(lowerCAmelCase    ):
																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:
																snake_case								=			[convert_to_rgb(lowerCAmelCase    ) for image in images]

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

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

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

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

										snake_case								=			[to_channel_dimension_format(lowerCAmelCase		,    lowerCAmelCase    ) for image in images]

										snake_case								=			BatchFeature(data={'pixel_values': images}		,    tensor_type=lowerCAmelCase    )

										return encoded_outputs



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





def    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				Dict:





						"""simple docstring"""






						def is_in_circle(_UpperCamelCase       :				float ,			_UpperCamelCase       :				float					) -> bool:
												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
						snake_case								=			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.
						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    lowerCAmelCase__    (			_UpperCamelCase       :				int ,			_UpperCamelCase       :				Callable[[float], float] ,			_UpperCamelCase       :				float = 0.0 ,			_UpperCamelCase       :				float = 1.0 ,			)					->				float:





						"""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					)					->				None:





						"""simple docstring"""






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

						snake_case								=			area_under_curve_estimator(
						    _UpperCamelCase ,			_UpperCamelCase ,			_UpperCamelCase ,			_UpperCamelCase					)
						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    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				None:





						"""simple docstring"""






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

						snake_case								=			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()


