


'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    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


__A	=logging.get_logger(__name__)


if is_vision_available():
		import PIL


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict   =					['''pixel_values''']




					def __init__(      self							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							_lowerCamelCase = PILImageResampling.BICUBIC							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							_lowerCamelCase = True							,							_lowerCamelCase = 1 / 255							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = True							,							**_lowerCamelCase							,							):
								super().__init__(**_lowerCamelCase)
								UpperCAmelCase__  :					Dict        =     size if size is not None else {"""shortest_edge""": 224}
								UpperCAmelCase__  :					Union[str, Any]        =     get_size_dict(_lowerCamelCase							,							default_to_square=_lowerCamelCase)
								UpperCAmelCase__  :					List[Any]        =     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								UpperCAmelCase__  :					Any        =     get_size_dict(_lowerCamelCase							,							default_to_square=_lowerCamelCase							,							param_name="""crop_size""")

								UpperCAmelCase__  :					int        =     do_resize
								UpperCAmelCase__  :					Any        =     size
								UpperCAmelCase__  :					Optional[Any]        =     resample
								UpperCAmelCase__  :					Optional[int]        =     do_center_crop
								UpperCAmelCase__  :					List[str]        =     crop_size
								UpperCAmelCase__  :					Tuple        =     do_rescale
								UpperCAmelCase__  :					Tuple        =     rescale_factor
								UpperCAmelCase__  :					Dict        =     do_normalize
								UpperCAmelCase__  :					Optional[int]        =     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								UpperCAmelCase__  :					Dict        =     image_std if image_std is not None else OPENAI_CLIP_STD
								UpperCAmelCase__  :					int        =     do_convert_rgb




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = PILImageResampling.BICUBIC							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					Optional[int]        =     get_size_dict(_lowerCamelCase							,							default_to_square=_lowerCamelCase)
								if "shortest_edge" not in size:
											raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''')
								UpperCAmelCase__  :					Any        =     get_resize_output_image_size(_lowerCamelCase							,							size=size["""shortest_edge"""]							,							default_to_square=_lowerCamelCase)
								return resize(_lowerCamelCase							,							size=_lowerCamelCase							,							resample=_lowerCamelCase							,							data_format=_lowerCamelCase							,							**_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					int        =     get_size_dict(_lowerCamelCase)
								if "height" not in size or "width" not in size:
											raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''')
								return center_crop(_lowerCamelCase							,							size=(size["""height"""], size["""width"""])							,							data_format=_lowerCamelCase							,							**_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								return rescale(_lowerCamelCase							,							scale=_lowerCamelCase							,							data_format=_lowerCamelCase							,							**_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								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 = None							,							_lowerCamelCase = None							,							_lowerCamelCase = ChannelDimension.FIRST							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					Dict        =     do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase__  :					int        =     size if size is not None else self.size
								UpperCAmelCase__  :					Any        =     get_size_dict(_lowerCamelCase							,							param_name="""size"""							,							default_to_square=_lowerCamelCase)
								UpperCAmelCase__  :					Optional[Any]        =     resample if resample is not None else self.resample
								UpperCAmelCase__  :					Optional[Any]        =     do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase__  :					Optional[Any]        =     crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase__  :					Tuple        =     get_size_dict(_lowerCamelCase							,							param_name="""crop_size"""							,							default_to_square=_lowerCamelCase)
								UpperCAmelCase__  :					int        =     do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase__  :					Any        =     rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase__  :					Optional[int]        =     do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase__  :					Any        =     image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase__  :					List[Any]        =     image_std if image_std is not None else self.image_std
								UpperCAmelCase__  :					Optional[int]        =     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								UpperCAmelCase__  :					Any        =     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:
											raise ValueError("""Size must be specified if do_resize is True.""")

								if do_center_crop and crop_size is None:
											raise ValueError("""Crop size 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.""")

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
											UpperCAmelCase__  :					Optional[int]        =     [convert_to_rgb(_lowerCamelCase) for image in images]

								# All transformations expect numpy arrays.
								UpperCAmelCase__  :					List[str]        =     [to_numpy_array(_lowerCamelCase) for image in images]

								if do_resize:
											UpperCAmelCase__  :					Optional[int]        =     [self.resize(image=_lowerCamelCase							,							size=_lowerCamelCase							,							resample=_lowerCamelCase) for image in images]

								if do_center_crop:
											UpperCAmelCase__  :					Any        =     [self.center_crop(image=_lowerCamelCase							,							size=_lowerCamelCase) for image in images]

								if do_rescale:
											UpperCAmelCase__  :					Dict        =     [self.rescale(image=_lowerCamelCase							,							scale=_lowerCamelCase) for image in images]

								if do_normalize:
											UpperCAmelCase__  :					Any        =     [self.normalize(image=_lowerCamelCase							,							mean=_lowerCamelCase							,							std=_lowerCamelCase) for image in images]

								UpperCAmelCase__  :					str        =     [to_channel_dimension_format(_lowerCamelCase							,							_lowerCamelCase) for image in images]

								UpperCAmelCase__  :					List[Any]        =     {"""pixel_values""": images}
								return BatchFeature(data=_lowerCamelCase							,							tensor_type=_lowerCamelCase)



'''simple docstring'''



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







def     _UpperCamelCase      (		UpperCamelCase__					):

			# A local function to see if a dot lands in the circle.
			def is_in_circle(UpperCamelCase__ ,      UpperCamelCase__					) -> bool:
						UpperCAmelCase__  :					List[str]        =     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__  :					Optional[Any]        =     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__  :					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     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ = 0.0 ,      UpperCamelCase__ = 1.0 ,      ):
			return mean(
			    function_to_integrate(uniform(UpperCamelCase__ ,      UpperCamelCase__					)					) for _ in range(UpperCamelCase__					)					) * (max_value - min_value)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ = 0.0 ,      UpperCamelCase__ = 1.0					):

			def identity_function(UpperCamelCase__					) -> float:
						return x

			UpperCAmelCase__  :					List[str]        =     area_under_curve_estimator(
			    UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
			UpperCAmelCase__  :					Optional[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     _UpperCamelCase      (		UpperCamelCase__					):

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

			UpperCAmelCase__  :					Dict        =     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()