from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__a  :     int       =  logging.get_logger(__name__)




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : List[Any]			 =						['''pixel_values''']





			def __init__(   self      ,   lowerCAmelCase__ = True      ,   lowerCAmelCase__ = 32      ,   lowerCAmelCase__=PILImageResampling.BILINEAR      ,   lowerCAmelCase__ = True      ,   **lowerCAmelCase__      ,   )     ->					None:

							'''simple docstring'''


							__lowercase 			=							do_resize
							__lowercase 			=							do_rescale
							__lowercase 			=							size_divisor
							__lowercase 			=							resample
							super().__init__(**lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None      ,   **lowerCAmelCase__						)     ->					np.ndarray:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							get_image_size(lowerCAmelCase__						)
							# Rounds the height and width down to the closest multiple of size_divisor
							__lowercase 			=							height // size_divisor * size_divisor
							__lowercase 			=							width // size_divisor * size_divisor
							__lowercase 			=							resize(lowerCAmelCase__      ,   (new_h, new_w)      ,   resample=lowerCAmelCase__      ,   data_format=lowerCAmelCase__      ,   **lowerCAmelCase__						)
							return image





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None      ,   **lowerCAmelCase__						)     ->					np.ndarray:

							'''simple docstring'''


							return rescale(image=lowerCAmelCase__      ,   scale=lowerCAmelCase__      ,   data_format=lowerCAmelCase__      ,   **lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__=None      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = ChannelDimension.FIRST      ,   **lowerCAmelCase__      ,   )     ->					BatchFeature:

							'''simple docstring'''


							__lowercase 			=							do_resize if do_resize is not None else self.do_resize
							__lowercase 			=							do_rescale if do_rescale is not None else self.do_rescale
							__lowercase 			=							size_divisor if size_divisor is not None else self.size_divisor
							__lowercase 			=							resample if resample is not None else self.resample

							if do_resize and size_divisor is None:
											raise ValueError('''size_divisor is required for resizing'''						)

							__lowercase 			=							make_list_of_images(lowerCAmelCase__						)

							if not valid_images(lowerCAmelCase__						):
											raise ValueError('''Invalid image(s)'''						)

							# All transformations expect numpy arrays.
							__lowercase 			=							[to_numpy_array(lowerCAmelCase__						) for img in images]

							if do_resize:
											__lowercase 			=							[self.resize(lowerCAmelCase__      ,   size_divisor=lowerCAmelCase__      ,   resample=lowerCAmelCase__						) for image in images]

							if do_rescale:
											__lowercase 			=							[self.rescale(lowerCAmelCase__      ,   scale=1 / 2_55						) for image in images]

							__lowercase 			=							[to_channel_dimension_format(lowerCAmelCase__      ,   lowerCAmelCase__						) for image in images]

							__lowercase 			=							{'''pixel_values''': images}
							return BatchFeature(data=lowerCAmelCase__      ,   tensor_type=lowerCAmelCase__						)
from decimal import Decimal, getcontext
from math import ceil, factorial





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				if not isinstance(lowercase							,			lowercase							):
								raise TypeError('''Undefined for non-integers'''							)
				elif precision < 1:
								raise ValueError('''Undefined for non-natural numbers'''							)

				__lowercase 			=							precision
				__lowercase 			=							ceil(precision / 14							)
				__lowercase 			=							426880 * Decimal(10005							).sqrt()
				__lowercase 			=							1
				__lowercase 			=							13591409
				__lowercase 			=							Decimal(lowercase							)
				for k in range(1							,			lowercase							):
								__lowercase 			=							factorial(6 * k							) // (factorial(3 * k							) * factorial(lowercase							) ** 3)
								linear_term += 545140134
								exponential_term *= -262537412640768000
								partial_sum += Decimal(multinomial_term * linear_term							) / exponential_term
				return str(constant_term / partial_sum							)[:-1]


if __name__ == "__main__":
		__a  :     Optional[Any]       =  5_0
		print(F'''The first {n} digits of pi is: {pi(n)}''')