

'''simple docstring'''

import random




def 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     List[str]						 =			num - 1
 UpperCAmelCase	:     str						 =			0

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

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




def 				UpperCamelCase(					UpperCAmelCase_     ):
 if num < 2:
  return False

 UpperCAmelCase	:     List[Any]						 =			[
     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,
     1_01,
     1_03,
     1_07,
     1_09,
     1_13,
     1_27,
     1_31,
     1_37,
     1_39,
     1_49,
     1_51,
     1_57,
     1_63,
     1_67,
     1_73,
     1_79,
     1_81,
     1_91,
     1_93,
     1_97,
     1_99,
     2_11,
     2_23,
     2_27,
     2_29,
     2_33,
     2_39,
     2_41,
     2_51,
     2_57,
     2_63,
     2_69,
     2_71,
     2_77,
     2_81,
     2_83,
     2_93,
     3_07,
     3_11,
     3_13,
     3_17,
     3_31,
     3_37,
     3_47,
     3_49,
     3_53,
     3_59,
     3_67,
     3_73,
     3_79,
     3_83,
     3_89,
     3_97,
     4_01,
     4_09,
     4_19,
     4_21,
     4_31,
     4_33,
     4_39,
     4_43,
     4_49,
     4_57,
     4_61,
     4_63,
     4_67,
     4_79,
     4_87,
     4_91,
     4_99,
     5_03,
     5_09,
     5_21,
     5_23,
     5_41,
     5_47,
     5_57,
     5_63,
     5_69,
     5_71,
     5_77,
     5_87,
     5_93,
     5_99,
     6_01,
     6_07,
     6_13,
     6_17,
     6_19,
     6_31,
     6_41,
     6_43,
     6_47,
     6_53,
     6_59,
     6_61,
     6_73,
     6_77,
     6_83,
     6_91,
     7_01,
     7_09,
     7_19,
     7_27,
     7_33,
     7_39,
     7_43,
     7_51,
     7_57,
     7_61,
     7_69,
     7_73,
     7_87,
     7_97,
     8_09,
     8_11,
     8_21,
     8_23,
     8_27,
     8_29,
     8_39,
     8_53,
     8_57,
     8_59,
     8_63,
     8_77,
     8_81,
     8_83,
     8_87,
     9_07,
     9_11,
     9_19,
     9_29,
     9_37,
     9_41,
     9_47,
     9_53,
     9_67,
     9_71,
     9_77,
     9_83,
     9_91,
     9_97,
 ]

 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_ = 10_24     ):
 while True:
  UpperCAmelCase	:     List[str]						 =			random.randrange(2 ** (keysize - 1)  ,	2 ** (keysize)     )
  if is_prime_low_num(UpperCAmelCase_     ):
   return num


if __name__ == "__main__":
 lowercase__         =      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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


lowercase__         =      logging.get_logger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			b.T
 UpperCAmelCase	:     Optional[int]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=1     )
 UpperCAmelCase	:     List[Any]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=0     )
 UpperCAmelCase	:     List[str]						 =			np.matmul(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			aa[:, None] - 2 * ab + ba[None, :]
 return d




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			x.reshape(-1  ,	3     )
 UpperCAmelCase	:     Optional[int]						 =			squared_euclidean_distance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return np.argmin(UpperCAmelCase_  ,	axis=1     )




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[Any]  		=  ["""pixel_values"""]


     def __init__(		self			:					List[Any]					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					bool = True					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     None:
      super().__init__(**lowercase_	)
      UpperCAmelCase	:     Any						 =			size if size is not None else {'height': 256, 'width': 256}
      UpperCAmelCase	:     List[Any]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     str						 =			np.array(lowercase_	) if clusters is not None else None
      UpperCAmelCase	:     Any						 =			do_resize
      UpperCAmelCase	:     List[Any]						 =			size
      UpperCAmelCase	:     Any						 =			resample
      UpperCAmelCase	:     Dict						 =			do_normalize
      UpperCAmelCase	:     List[Any]						 =			do_color_quantize


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Any					,      )						->     np.ndarray:
      UpperCAmelCase	:     Dict						 =			get_size_dict(lowercase_	)
      if "height" not in size or "width" not in size:
       raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""	)
      return resize(
          lowercase_					,      size=(size['height'], size['width'])					,      resample=lowercase_					,      data_format=lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      )						->     np.ndarray:
      UpperCAmelCase	:     int						 =			rescale(image=lowercase_					,      scale=1 / 127.5					,      data_format=lowercase_	)
      UpperCAmelCase	:     Dict						 =			image - 1
      return image




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					ImageInput					,      lowercase_			:					bool = None					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = None					,      lowercase_			:					bool = None					,      lowercase_			:					Optional[bool] = None					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					Optional[Union[str, TensorType]] = None					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					,      **lowercase_			:					List[str]					,      )						->     PIL.Image.Image:
      UpperCAmelCase	:     Optional[int]						 =			do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase	:     Optional[Any]						 =			size if size is not None else self.size
      UpperCAmelCase	:     Optional[int]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     Any						 =			resample if resample is not None else self.resample
      UpperCAmelCase	:     Optional[Any]						 =			do_normalize if do_normalize is not None else self.do_normalize
      UpperCAmelCase	:     str						 =			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      UpperCAmelCase	:     Optional[int]						 =			clusters if clusters is not None else self.clusters
      UpperCAmelCase	:     List[str]						 =			np.array(lowercase_	)

      UpperCAmelCase	:     int						 =			make_list_of_images(lowercase_	)

      if not valid_images(lowercase_	):
       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_color_quantize and clusters is None:
       raise ValueError('Clusters must be specified if do_color_quantize is True.'	)

      # All transformations expect numpy arrays.
      UpperCAmelCase	:     Dict						 =			[to_numpy_array(lowercase_	) for image in images]

      if do_resize:
       UpperCAmelCase	:     List[Any]						 =			[self.resize(image=lowercase_					,      size=lowercase_					,      resample=lowercase_	) for image in images]

      if do_normalize:
       UpperCAmelCase	:     Tuple						 =			[self.normalize(image=lowercase_	) for image in images]

      if do_color_quantize:
       UpperCAmelCase	:     List[str]						 =			[to_channel_dimension_format(lowercase_					,      ChannelDimension.LAST	) for image in images]
       # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
       UpperCAmelCase	:     int						 =			np.array(lowercase_	)
       UpperCAmelCase	:     str						 =			color_quantize(lowercase_					,      lowercase_	).reshape(images.shape[:-1]	)

       # flatten to (batch_size, height*width)
       UpperCAmelCase	:     Optional[int]						 =			images.shape[0]
       UpperCAmelCase	:     Union[str, Any]						 =			images.reshape(lowercase_					,      -1	)

       # We need to convert back to a list of images to keep consistent behaviour across processors.
       UpperCAmelCase	:     int						 =			list(lowercase_	)
      else:
       UpperCAmelCase	:     Dict						 =			[to_channel_dimension_format(lowercase_					,      lowercase_	) for image in images]

      UpperCAmelCase	:     Any						 =			{'input_ids': images}
      return BatchFeature(data=lowercase_					,      tensor_type=lowercase_	)




