


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_STANDARD_MEAN,
    IMAGENET_STANDARD_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


__lowerCAmelCase      :					Tuple    							=logging.get_logger(__name__)
class 			_lowercase (     UpperCamelCase__			):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__							:     int         =						['''pixel_values''']


				def __init__(			self     :Union[str, Any]     ,     lowerCAmelCase__     :bool = True     ,     lowerCAmelCase__     :Dict[str, int] = None     ,     lowerCAmelCase__     :float = None     ,     lowerCAmelCase__     :PILImageResampling = PILImageResampling.BILINEAR     ,     lowerCAmelCase__     :bool = True     ,     lowerCAmelCase__     :Union[int, float] = 1 / 255     ,     lowerCAmelCase__     :bool = True     ,     lowerCAmelCase__     :Optional[Union[float, List[float]]] = None     ,     lowerCAmelCase__     :Optional[Union[float, List[float]]] = None     ,     **lowerCAmelCase__     :str     ,     )						->     List[Any]:
										super().__init__(**__snake_case      )
										__SCREAMING_SNAKE_CASE		:	int						     =						size if size is not None else {"""shortest_edge""": 384}
										__SCREAMING_SNAKE_CASE		:	str						     =						get_size_dict(__snake_case     ,     default_to_square=__snake_case      )

										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						do_resize
										__SCREAMING_SNAKE_CASE		:	str						     =						size
										# Default value set here for backwards compatibility where the value in config is None
										__SCREAMING_SNAKE_CASE		:	str						     =						crop_pct if crop_pct is not None else 224 / 256
										__SCREAMING_SNAKE_CASE		:	List[str]						     =						resample
										__SCREAMING_SNAKE_CASE		:	Any						     =						do_rescale
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						rescale_factor
										__SCREAMING_SNAKE_CASE		:	Tuple						     =						do_normalize
										__SCREAMING_SNAKE_CASE		:	int						     =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						image_std if image_std is not None else IMAGENET_STANDARD_STD


				def 						__magic_name__(			self     :Optional[Any]     ,     lowerCAmelCase__     :np.ndarray     ,     lowerCAmelCase__     :Dict[str, int]     ,     lowerCAmelCase__     :float     ,     lowerCAmelCase__     :PILImageResampling = PILImageResampling.BICUBIC     ,     lowerCAmelCase__     :Optional[Union[str, ChannelDimension]] = None     ,     **lowerCAmelCase__     :int     ,     )						->     Any:
										__SCREAMING_SNAKE_CASE		:	Tuple						     =						get_size_dict(__snake_case     ,     default_to_square=__snake_case      )
										if "shortest_edge" not in size:
																raise ValueError(f'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''      )
										__SCREAMING_SNAKE_CASE		:	Dict						     =						size["""shortest_edge"""]

										if shortest_edge < 384:
																# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						int(shortest_edge / crop_pct      )
																__SCREAMING_SNAKE_CASE		:	str						     =						get_resize_output_image_size(__snake_case     ,     size=__snake_case     ,     default_to_square=__snake_case      )
																__SCREAMING_SNAKE_CASE		:	int						     =						resize(image=__snake_case     ,     size=__snake_case     ,     resample=__snake_case     ,     data_format=__snake_case     ,     **__snake_case      )
																# then crop to (shortest_edge, shortest_edge)
																return center_crop(image=__snake_case     ,     size=(shortest_edge, shortest_edge)     ,     data_format=__snake_case     ,     **__snake_case      )
										else:
																# warping (no cropping) when evaluated at 384 or larger
																return resize(
																    __snake_case     ,     size=(shortest_edge, shortest_edge)     ,     resample=__snake_case     ,     data_format=__snake_case     ,     **__snake_case      )


				def 						__magic_name__(			self     :int     ,     lowerCAmelCase__     :np.ndarray     ,     lowerCAmelCase__     :Union[int, float]     ,     lowerCAmelCase__     :Optional[Union[str, ChannelDimension]] = None     ,     **lowerCAmelCase__     :Any     ,     )						->     Union[str, Any]:
										return rescale(__snake_case     ,     scale=__snake_case     ,     data_format=__snake_case     ,     **__snake_case      )


				def 						__magic_name__(			self     :List[str]     ,     lowerCAmelCase__     :np.ndarray     ,     lowerCAmelCase__     :Union[float, List[float]]     ,     lowerCAmelCase__     :Union[float, List[float]]     ,     lowerCAmelCase__     :Optional[Union[str, ChannelDimension]] = None     ,     **lowerCAmelCase__     :List[Any]     ,     )						->     str:
										return normalize(__snake_case     ,     mean=__snake_case     ,     std=__snake_case     ,     data_format=__snake_case     ,     **__snake_case      )



				def 						__magic_name__(			self     :List[str]     ,     lowerCAmelCase__     :ImageInput     ,     lowerCAmelCase__     :bool = None     ,     lowerCAmelCase__     :Dict[str, int] = None     ,     lowerCAmelCase__     :float = None     ,     lowerCAmelCase__     :PILImageResampling = None     ,     lowerCAmelCase__     :bool = None     ,     lowerCAmelCase__     :float = None     ,     lowerCAmelCase__     :bool = None     ,     lowerCAmelCase__     :Optional[Union[float, List[float]]] = None     ,     lowerCAmelCase__     :Optional[Union[float, List[float]]] = None     ,     lowerCAmelCase__     :Optional[Union[str, TensorType]] = None     ,     lowerCAmelCase__     :ChannelDimension = ChannelDimension.FIRST     ,     **lowerCAmelCase__     :Union[str, Any]     ,     )						->     Optional[Any]:
										__SCREAMING_SNAKE_CASE		:	str						     =						do_resize if do_resize is not None else self.do_resize
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						crop_pct if crop_pct is not None else self.crop_pct
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						resample if resample is not None else self.resample
										__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						do_rescale if do_rescale is not None else self.do_rescale
										__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						rescale_factor if rescale_factor is not None else self.rescale_factor
										__SCREAMING_SNAKE_CASE		:	Tuple						     =						do_normalize if do_normalize is not None else self.do_normalize
										__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						image_mean if image_mean is not None else self.image_mean
										__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						image_std if image_std is not None else self.image_std

										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						size if size is not None else self.size
										__SCREAMING_SNAKE_CASE		:	int						     =						get_size_dict(__snake_case     ,     default_to_square=__snake_case      )

										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						make_list_of_images(__snake_case      )

										if not valid_images(__snake_case      ):
																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_resize and size["shortest_edge"] < 384 and crop_pct is None:
																raise ValueError('''crop_pct must be specified if size < 384.'''      )

										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		:	Union[str, Any]						     =						[to_numpy_array(__snake_case      ) for image in images]

										if do_resize:
																__SCREAMING_SNAKE_CASE		:	List[str]						     =						[self.resize(image=__snake_case     ,     size=__snake_case     ,     crop_pct=__snake_case     ,     resample=__snake_case      ) for image in images]

										if do_rescale:
																__SCREAMING_SNAKE_CASE		:	List[str]						     =						[self.rescale(image=__snake_case     ,     scale=__snake_case      ) for image in images]

										if do_normalize:
																__SCREAMING_SNAKE_CASE		:	Dict						     =						[self.normalize(image=__snake_case     ,     mean=__snake_case     ,     std=__snake_case      ) for image in images]

										__SCREAMING_SNAKE_CASE		:	int						     =						[to_channel_dimension_format(__snake_case     ,     __snake_case      ) for image in images]

										__SCREAMING_SNAKE_CASE		:	List[str]						     =						{"""pixel_values""": images}
										return BatchFeature(data=__snake_case     ,     tensor_type=__snake_case      )






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




def 		lowerCAmelCase_ (						lowerCamelCase ):

				# A local function to see if a dot lands in the circle.
				def is_in_circle(lowerCamelCase						,  lowerCamelCase ) -> bool:
								__magic_name__						:						Dict										=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
				__magic_name__						:						Union[str, Any]										=mean(
				    int(is_in_circle(uniform(-1.0						,  1.0 )						,  uniform(-1.0						,  1.0 ) ) )
				    for _ in range(lowerCamelCase ) )
				# The ratio of the area for circle to square is pi/4.
				__magic_name__						:						List[Any]										=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_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase = 0.0						,  lowerCamelCase = 1.0						,  ):
				return mean(
				    function_to_integrate(uniform(lowerCamelCase						,  lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value)




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase = 0.0						,  lowerCamelCase = 1.0 ):

				def identity_function(lowerCamelCase ) -> float:
								return x

				__magic_name__						:						Optional[int]										=area_under_curve_estimator(
				    lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
				__magic_name__						:						str										=(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_ (						lowerCamelCase ):

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

				__magic_name__						:						Dict										=area_under_curve_estimator(
				    lowerCamelCase						,  lowerCamelCase						,  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()




