




"""simple docstring"""





import random

def 					_UpperCamelCase    (			UpperCamelCase							)			->			bool:


			"""simple docstring"""
			__UpperCAmelCase	: Tuple		=					num - 1
			__UpperCAmelCase	: Union[str, Any]		=					0

			while s % 2 == 0:
						__UpperCAmelCase	: Any		=					s // 2
						t += 1

			for _ in range(5							):
						__UpperCAmelCase	: Dict		=					random.randrange(2				,       num - 1							)
						__UpperCAmelCase	: Dict		=					pow(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
						if v != 1:
									__UpperCAmelCase	: Any		=					0
									while v != (num - 1):
												if i == t - 1:
															return False
												else:
															__UpperCAmelCase	: List[Any]		=					i + 1
															__UpperCAmelCase	: Any		=					(v**2) % num
			return True

def 					_UpperCamelCase    (			UpperCamelCase							)			->			bool:


			"""simple docstring"""
			if num < 2:
						return False

			__UpperCAmelCase	: str		=					[
			    2,
			    3,
			    5,
			    7,
			    11,
			    13,
			    17,
			    19,
			    23,
			    29,
			    31,
			    37,
			    41,
			    43,
			    47,
			    53,
			    59,
			    61,
			    67,
			    71,
			    73,
			    79,
			    83,
			    89,
			    97,
			    101,
			    103,
			    107,
			    109,
			    113,
			    127,
			    131,
			    137,
			    139,
			    149,
			    151,
			    157,
			    163,
			    167,
			    173,
			    179,
			    181,
			    191,
			    193,
			    197,
			    199,
			    211,
			    223,
			    227,
			    229,
			    233,
			    239,
			    241,
			    251,
			    257,
			    263,
			    269,
			    271,
			    277,
			    281,
			    283,
			    293,
			    307,
			    311,
			    313,
			    317,
			    331,
			    337,
			    347,
			    349,
			    353,
			    359,
			    367,
			    373,
			    379,
			    383,
			    389,
			    397,
			    401,
			    409,
			    419,
			    421,
			    431,
			    433,
			    439,
			    443,
			    449,
			    457,
			    461,
			    463,
			    467,
			    479,
			    487,
			    491,
			    499,
			    503,
			    509,
			    521,
			    523,
			    541,
			    547,
			    557,
			    563,
			    569,
			    571,
			    577,
			    587,
			    593,
			    599,
			    601,
			    607,
			    613,
			    617,
			    619,
			    631,
			    641,
			    643,
			    647,
			    653,
			    659,
			    661,
			    673,
			    677,
			    683,
			    691,
			    701,
			    709,
			    719,
			    727,
			    733,
			    739,
			    743,
			    751,
			    757,
			    761,
			    769,
			    773,
			    787,
			    797,
			    809,
			    811,
			    821,
			    823,
			    827,
			    829,
			    839,
			    853,
			    857,
			    859,
			    863,
			    877,
			    881,
			    883,
			    887,
			    907,
			    911,
			    919,
			    929,
			    937,
			    941,
			    947,
			    953,
			    967,
			    971,
			    977,
			    983,
			    991,
			    997,
			]

			if num in low_primes:
						return True

			for prime in low_primes:
						if (num % prime) == 0:
									return False

			return rabin_miller(UpperCamelCase							)

def 					_UpperCamelCase    (			UpperCamelCase = 1024							)			->			int:


			"""simple docstring"""
			while True:
						__UpperCAmelCase	: Optional[Any]		=					random.randrange(2 ** (keysize - 1)				,       2 ** (keysize)							)
						if is_prime_low_num(UpperCamelCase							):
									return num


if __name__ == "__main__":
	A   =       generate_large_prime()
	print(("""Prime number:""", num))
	print(("""is_prime_low_num:""", is_prime_low_num(num)))








"""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,
    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 TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


A   =       logging.get_logger(__name__)

def 					_UpperCamelCase    (			UpperCamelCase							)			->			List[List[ImageInput]]:


			"""simple docstring"""
			if isinstance(UpperCamelCase				,       (list, tuple)							) and isinstance(videos[0]				,       (list, tuple)							) and is_valid_image(videos[0][0]							):
						return videos

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

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

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





class      a__				(	__magic_name__   ):
			lowercase_    =	["pixel_values"]


			def __init__(      self      :    Tuple      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    PILImageResampling = PILImageResampling.BILINEAR      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    Union[int, float] = 1 / 255      ,  UpperCamelCase_      :    bool = True      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  **UpperCamelCase_      :    List[str]      ,  ):


						"""simple docstring"""




						super().__init__(**UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					size if size is not None else {"shortest_edge": 224}
						__UpperCAmelCase	: Union[str, Any]		=					get_size_dict(UpperCamelCase_      ,  default_to_square=UpperCamelCase_)
						__UpperCAmelCase	: Optional[int]		=					crop_size if crop_size is not None else {"height": 224, "width": 224}
						__UpperCAmelCase	: List[str]		=					get_size_dict(UpperCamelCase_      ,  param_name="crop_size")

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


			def       a_     (      self      :    int      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Dict[str, int]      ,  UpperCamelCase_      :    PILImageResampling = PILImageResampling.BILINEAR      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    List[Any]      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					get_size_dict(UpperCamelCase_      ,  default_to_square=UpperCamelCase_)
						if "shortest_edge" in size:
									__UpperCAmelCase	: Dict		=					get_resize_output_image_size(UpperCamelCase_      ,  size["shortest_edge"]      ,  default_to_square=UpperCamelCase_)
						elif "height" in size and "width" in size:
									__UpperCAmelCase	: Dict		=					(size["height"], size["width"])
						else:
									raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
						return resize(UpperCamelCase_      ,  size=UpperCamelCase_      ,  resample=UpperCamelCase_      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_)


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Dict[str, int]      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    Tuple      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					get_size_dict(UpperCamelCase_)
						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(UpperCamelCase_      ,  size=(size["height"], size["width"])      ,  data_format=UpperCamelCase_      ,  **UpperCamelCase_)


			def       a_     (      self      :    Optional[int]      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Union[int, float]      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    Optional[int]      ,  ):


						"""simple docstring"""




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


			def       a_     (      self      :    List[Any]      ,  UpperCamelCase_      :    np.ndarray      ,  UpperCamelCase_      :    Union[float, List[float]]      ,  UpperCamelCase_      :    Union[float, List[float]]      ,  UpperCamelCase_      :    Optional[Union[str, ChannelDimension]] = None      ,  **UpperCamelCase_      :    Union[str, Any]      ,  ):


						"""simple docstring"""




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


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    ImageInput      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    PILImageResampling = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    float = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  UpperCamelCase_      :    Optional[ChannelDimension] = ChannelDimension.FIRST      ,  ):


						"""simple docstring"""




						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.")

						# All transformations expect numpy arrays.
						__UpperCAmelCase	: List[str]		=					to_numpy_array(UpperCamelCase_)

						if do_resize:
									__UpperCAmelCase	: Tuple		=					self.resize(image=UpperCamelCase_      ,  size=UpperCamelCase_      ,  resample=UpperCamelCase_)

						if do_center_crop:
									__UpperCAmelCase	: str		=					self.center_crop(UpperCamelCase_      ,  size=UpperCamelCase_)

						if do_rescale:
									__UpperCAmelCase	: Union[str, Any]		=					self.rescale(image=UpperCamelCase_      ,  scale=UpperCamelCase_)

						if do_normalize:
									__UpperCAmelCase	: Optional[int]		=					self.normalize(image=UpperCamelCase_      ,  mean=UpperCamelCase_      ,  std=UpperCamelCase_)

						__UpperCAmelCase	: Any		=					to_channel_dimension_format(UpperCamelCase_      ,  UpperCamelCase_)
						return image


			def       a_     (      self      :    List[Any]      ,  UpperCamelCase_      :    ImageInput      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    PILImageResampling = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Dict[str, int] = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    float = None      ,  UpperCamelCase_      :    bool = None      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  UpperCamelCase_      :    Optional[Union[float, List[float]]] = None      ,  UpperCamelCase_      :    Optional[Union[str, TensorType]] = None      ,  UpperCamelCase_      :    ChannelDimension = ChannelDimension.FIRST      ,  **UpperCamelCase_      :    Optional[int]      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					do_resize if do_resize is not None else self.do_resize
						__UpperCAmelCase	: Optional[Any]		=					resample if resample is not None else self.resample
						__UpperCAmelCase	: Any		=					do_center_crop if do_center_crop is not None else self.do_center_crop
						__UpperCAmelCase	: str		=					do_rescale if do_rescale is not None else self.do_rescale
						__UpperCAmelCase	: str		=					rescale_factor if rescale_factor is not None else self.rescale_factor
						__UpperCAmelCase	: Tuple		=					do_normalize if do_normalize is not None else self.do_normalize
						__UpperCAmelCase	: str		=					image_mean if image_mean is not None else self.image_mean
						__UpperCAmelCase	: Optional[Any]		=					image_std if image_std is not None else self.image_std

						__UpperCAmelCase	: Tuple		=					size if size is not None else self.size
						__UpperCAmelCase	: str		=					get_size_dict(UpperCamelCase_      ,  default_to_square=UpperCamelCase_)
						__UpperCAmelCase	: Any		=					crop_size if crop_size is not None else self.crop_size
						__UpperCAmelCase	: int		=					get_size_dict(UpperCamelCase_      ,  param_name="crop_size")

						if not valid_images(UpperCamelCase_):
									raise ValueError(
									    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
									    "torch.Tensor, tf.Tensor or jax.ndarray.")

						__UpperCAmelCase	: Dict		=					make_batched(UpperCamelCase_)

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

						__UpperCAmelCase	: int		=					{"pixel_values": videos}
						return BatchFeature(data=UpperCamelCase_      ,  tensor_type=UpperCamelCase_)


