


import random



def UpperCamelCase_				(				__a      )	->   Dict:
      a__	:  Any     =   num - 1
      a__	:  Union[str, Any]     =   0

      while s % 2 == 0:
            a__	:  str     =   s // 2
            t += 1

      for _ in range(5      ):
            a__	:  int     =   random.randrange(2			,				num - 1      )
            a__	:  Dict     =   pow(_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase      )
            if v != 1:
                  a__	:  str     =   0
                  while v != (num - 1):
                        if i == t - 1:
                              return False
                        else:
                              a__	:  int     =   i + 1
                              a__	:  Any     =   (v**2) % num
      return True



def UpperCamelCase_				(				__a      )	->   Union[str, Any]:







      if num < 2:
            return False

      a__	:  Dict     =   [
          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_				(				__a = 1_024      )	->   str:







      while True:
            a__	:  int     =   random.randrange(2 ** (keysize - 1)			,				2 ** (keysize)      )
            if is_prime_low_num(_UpperCamelCase      ):
                  return num


if __name__ == "__main__":
    UpperCamelCase					:       Optional[int]			    =     generate_large_prime()
    print(("""Prime number:""", num))
    print(("""is_prime_low_num:""", is_prime_low_num(num)))





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


a			:			List[str]												=					logging.get_logger(__name__)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					["pixel_values"]






					def __init__(					self			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = PILImageResampling.BILINEAR			,							snake_case_ = True			,							snake_case_ = 1 / 2_5_5			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = None			,							**snake_case_			,							) ->      None:







								'''simple docstring'''

								super().__init__(**snake_case_    )
								__lowercase			       = size if size is not None else {'''shortest_edge''': 3_8_4}
								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )

								__lowercase			       = do_resize
								__lowercase			       = size
								# Default value set here for backwards compatibility where the value in config is None
								__lowercase			       = crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
								__lowercase			       = resample
								__lowercase			       = do_rescale
								__lowercase			       = rescale_factor
								__lowercase			       = do_normalize
								__lowercase			       = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__lowercase			       = image_std if image_std is not None else IMAGENET_STANDARD_STD






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = PILImageResampling.BICUBIC			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

								__lowercase			       = 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()}'    )
								__lowercase			       = size['''shortest_edge''']

								if shortest_edge < 3_8_4:
											# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
											__lowercase			       = int(shortest_edge / crop_pct    )
											__lowercase			       = get_resize_output_image_size(snake_case_			,							size=snake_case_			,							default_to_square=snake_case_    )
											__lowercase			       = 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 A   (					self			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      Optional[int]:







								'''simple docstring'''

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






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

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






					def A   (					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = ChannelDimension.FIRST			,							**snake_case_			,							) ->      PIL.Image.Image:







								'''simple docstring'''

								__lowercase			       = do_resize if do_resize is not None else self.do_resize
								__lowercase			       = crop_pct if crop_pct is not None else self.crop_pct
								__lowercase			       = resample if resample is not None else self.resample
								__lowercase			       = do_rescale if do_rescale is not None else self.do_rescale
								__lowercase			       = rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowercase			       = do_normalize if do_normalize is not None else self.do_normalize
								__lowercase			       = image_mean if image_mean is not None else self.image_mean
								__lowercase			       = image_std if image_std is not None else self.image_std

								__lowercase			       = size if size is not None else self.size
								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )

								__lowercase			       = 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"] < 3_8_4 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.
								__lowercase			       = [to_numpy_array(snake_case_    ) for image in images]

								if do_resize:
											__lowercase			       = [self.resize(image=snake_case_			,							size=snake_case_			,							crop_pct=snake_case_			,							resample=snake_case_    ) for image in images]

								if do_rescale:
											__lowercase			       = [self.rescale(image=snake_case_			,							scale=snake_case_    ) for image in images]

								if do_normalize:
											__lowercase			       = [self.normalize(image=snake_case_			,							mean=snake_case_			,							std=snake_case_    ) for image in images]

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

								__lowercase			       = {'''pixel_values''': images}
								return BatchFeature(data=snake_case_			,							tensor_type=snake_case_    )


