



'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
						import PIL

UpperCamelCase__:   Optional[Any]								=			logging.get_logger(__name__)


def  snake_case_     (					_lowerCAmelCase  :						List[Any]  )      ->   List[List[ImageInput]]:
							if isinstance(_lowerCAmelCase   ,      (list, tuple)  ) and isinstance(videos[0]   ,      (list, tuple)  ) and is_valid_image(videos[0][0]  ):
														return videos

							elif isinstance(_lowerCAmelCase   ,      (list, tuple)  ) and is_valid_image(videos[0]  ):
														return [videos]

							elif is_valid_image(_lowerCAmelCase  ):
														return [[videos]]

							raise ValueError(f"""Could not make batched video from {videos}"""  )




class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       ["""pixel_values"""]







	def __init__(						self			:     Optional[int]		,			__snake_case			:     bool = True		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     PILImageResampling = PILImageResampling.BILINEAR		,			__snake_case			:     bool = True		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     bool = True		,			__snake_case			:     Union[int, float] = 1 / 255		,			__snake_case			:     bool = True		,			__snake_case			:     bool = True		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			**__snake_case			:     Dict		,			)			->		None:
								super().__init__(**__snake_case		)
								UpperCAmelCase :       List[Any] 							=		size if size is not None else {'''shortest_edge''': 256}
								UpperCAmelCase :       Union[str, Any] 							=		get_size_dict(__snake_case		,			default_to_square=__snake_case		)
								UpperCAmelCase :       List[Any] 							=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								UpperCAmelCase :       Union[str, Any] 							=		get_size_dict(__snake_case		,			param_name='''crop_size'''		)

								UpperCAmelCase :       Any 							=		do_resize
								UpperCAmelCase :       Optional[int] 							=		size
								UpperCAmelCase :       List[str] 							=		do_center_crop
								UpperCAmelCase :       Union[str, Any] 							=		crop_size
								UpperCAmelCase :       Dict 							=		resample
								UpperCAmelCase :       str 							=		do_rescale
								UpperCAmelCase :       List[str] 							=		rescale_factor
								UpperCAmelCase :       List[str] 							=		offset
								UpperCAmelCase :       Tuple 							=		do_normalize
								UpperCAmelCase :       Tuple 							=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								UpperCAmelCase :       Optional[int] 							=		image_std if image_std is not None else IMAGENET_STANDARD_STD







	def      A	(						self			:     str		,			__snake_case			:     np.ndarray		,			__snake_case			:     Dict[str, int]		,			__snake_case			:     PILImageResampling = PILImageResampling.BILINEAR		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     Union[str, Any]		,			)			->		np.ndarray:
								UpperCAmelCase :       int 							=		get_size_dict(__snake_case		,			default_to_square=__snake_case		)
								if "shortest_edge" in size:
															UpperCAmelCase :       str 							=		get_resize_output_image_size(__snake_case		,			size['''shortest_edge''']		,			default_to_square=__snake_case		)
								elif "height" in size and "width" in size:
															UpperCAmelCase :       Optional[int] 							=		(size['''height'''], size['''width'''])
								else:
															raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""		)
								return resize(__snake_case		,			size=__snake_case		,			resample=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Dict		,			__snake_case			:     np.ndarray		,			__snake_case			:     Dict[str, int]		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     List[str]		,			)			->		np.ndarray:
								UpperCAmelCase :       Optional[Any] 							=		get_size_dict(__snake_case		)
								if "height" not in size or "width" not in size:
															raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""		)
								return center_crop(__snake_case		,			size=(size['''height'''], size['''width'''])		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Optional[int]		,			__snake_case			:     np.ndarray		,			__snake_case			:     Union[int, float]		,			__snake_case			:     bool = True		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     Optional[int]		,			)			->		Union[str, Any]:
								UpperCAmelCase :       List[str] 							=		image.astype(np.floataa		)
								if offset:
															UpperCAmelCase :       List[Any] 							=		image - (scale / 2)
								return rescale(__snake_case		,			scale=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







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







	def      A	(						self			:     int		,			__snake_case			:     ImageInput		,			__snake_case			:     bool = None		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     PILImageResampling = None		,			__snake_case			:     bool = None		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     bool = None		,			__snake_case			:     float = None		,			__snake_case			:     bool = None		,			__snake_case			:     bool = None		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			__snake_case			:     Optional[ChannelDimension] = ChannelDimension.FIRST		,			)			->		np.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_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.'''		)

								if offset and not do_rescale:
															raise ValueError('''For offset, do_rescale must also be set to True.'''		)

								# All transformations expect numpy arrays.
								UpperCAmelCase :       Optional[int] 							=		to_numpy_array(__snake_case		)

								if do_resize:
															UpperCAmelCase :       Optional[int] 							=		self.resize(image=__snake_case		,			size=__snake_case		,			resample=__snake_case		)

								if do_center_crop:
															UpperCAmelCase :       str 							=		self.center_crop(__snake_case		,			size=__snake_case		)

								if do_rescale:
															UpperCAmelCase :       Union[str, Any] 							=		self.rescale(image=__snake_case		,			scale=__snake_case		,			offset=__snake_case		)

								if do_normalize:
															UpperCAmelCase :       Tuple 							=		self.normalize(image=__snake_case		,			mean=__snake_case		,			std=__snake_case		)

								UpperCAmelCase :       int 							=		to_channel_dimension_format(__snake_case		,			__snake_case		)
								return image







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     ImageInput		,			__snake_case			:     bool = None		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     PILImageResampling = None		,			__snake_case			:     bool = None		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     bool = None		,			__snake_case			:     float = None		,			__snake_case			:     bool = None		,			__snake_case			:     bool = None		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			__snake_case			:     Optional[Union[str, TensorType]] = None		,			__snake_case			:     ChannelDimension = ChannelDimension.FIRST		,			**__snake_case			:     int		,			)			->		PIL.Image.Image:
								UpperCAmelCase :       Optional[int] 							=		do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase :       Tuple 							=		resample if resample is not None else self.resample
								UpperCAmelCase :       List[str] 							=		do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase :       Optional[Any] 							=		do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase :       List[str] 							=		rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase :       Dict 							=		offset if offset is not None else self.offset
								UpperCAmelCase :       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 :       str 							=		image_std if image_std is not None else self.image_std

								UpperCAmelCase :       Optional[int] 							=		size if size is not None else self.size
								UpperCAmelCase :       Optional[Any] 							=		get_size_dict(__snake_case		,			default_to_square=__snake_case		)
								UpperCAmelCase :       str 							=		crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase :       str 							=		get_size_dict(__snake_case		,			param_name='''crop_size'''		)

								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.'''		)

								UpperCAmelCase :       Union[str, Any] 							=		make_batched(__snake_case		)

								UpperCAmelCase :       List[Any] 							=		[
								    [
								        self._preprocess_image(
								            image=__snake_case		,			do_resize=__snake_case		,			size=__snake_case		,			resample=__snake_case		,			do_center_crop=__snake_case		,			crop_size=__snake_case		,			do_rescale=__snake_case		,			rescale_factor=__snake_case		,			offset=__snake_case		,			do_normalize=__snake_case		,			image_mean=__snake_case		,			image_std=__snake_case		,			data_format=__snake_case		,			)
								        for img in video
								    ]
								    for video in videos
								]

								UpperCAmelCase :       Dict 							=		{'''pixel_values''': videos}
								return BatchFeature(data=__snake_case		,			tensor_type=__snake_case		)










'''simple docstring'''






import random

from .binary_exp_mod import bin_exp_mod


def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Optional[Any]=1000  )      ->   int:
							if n < 2:
														return False

							if n % 2 == 0:
														return n == 2

							# this means n is odd
							UpperCAmelCase :       str 							=		n - 1
							UpperCAmelCase :       List[Any] 							=		0
							while d % 2 == 0:
														d /= 2
														exp += 1

							# n - 1=d*(2**exp)
							UpperCAmelCase :       List[str] 							=		0
							while count < prec:
														UpperCAmelCase :       int 							=		random.randint(2   ,      n - 1  )
														UpperCAmelCase :       List[str] 							=		bin_exp_mod(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														if b != 1:
																					UpperCAmelCase :       int 							=		True
																					for _ in range(_lowerCAmelCase  ):
																												if b == n - 1:
																																			UpperCAmelCase :       Dict 							=		False
																																			break
																												UpperCAmelCase :       str 							=		b * b
																												b %= n
																					if flag:
																												return False
																					count += 1
							return True


if __name__ == "__main__":
						UpperCamelCase__:   Optional[int]								=			abs(int(input("Enter bound : ").strip()))
						print("Here's the list of primes:")
						print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))





