
'''simple docstring'''


import random



def  __snake_case     (    _UpperCAmelCase					:       int):
   UpperCamelCase       =  num - 1
   UpperCamelCase       =  0

   while s % 2 == 0:
      UpperCamelCase       =  s // 2
      t += 1

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



def  __snake_case     (    _UpperCAmelCase					:       int):
   if num < 2:
      return False

   UpperCamelCase       =  [
       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  __snake_case     (    _UpperCAmelCase					:       int = 1024):
   while True:
      UpperCamelCase       =  random.randrange(2 ** (keysize - 1),      2 ** (keysize))
      if is_prime_low_num(_UpperCAmelCase):
         return num


if __name__ == "__main__":
   snake_case_ :  Dict     =					generate_large_prime()
   print(('Prime number:', num))
   print(('is_prime_low_num:', is_prime_low_num(num)))







'''simple docstring'''


from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
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_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
   import PIL

# soft dependency
if is_pytesseract_available():
   import pytesseract

snake_case_ :  Tuple     =					logging.get_logger(__name__)



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Optional[int]):
   return [
       int(1000 * (box[0] / width)),
       int(1000 * (box[1] / height)),
       int(1000 * (box[2] / width)),
       int(1000 * (box[3] / height)),
   ]



def  __snake_case     (    _UpperCAmelCase					:       np.ndarray,      _UpperCAmelCase					:       Optional[str],      _UpperCAmelCase					:       Optional[str]):
   UpperCamelCase       =  to_pil_image(_UpperCAmelCase)
   UpperCamelCase     , UpperCamelCase       =  pil_image.size
   UpperCamelCase       =  pytesseract.image_to_data(_UpperCAmelCase,      lang=_UpperCAmelCase,      output_type='''dict''',      config=_UpperCAmelCase)
   UpperCamelCase     , UpperCamelCase     , UpperCamelCase     , UpperCamelCase     , UpperCamelCase       =  data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

   # filter empty words and corresponding coordinates
   UpperCamelCase       =  [idx for idx, word in enumerate(_UpperCAmelCase) if not word.strip()]
   UpperCamelCase       =  [word for idx, word in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
   UpperCamelCase       =  [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
   UpperCamelCase       =  [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
   UpperCamelCase       =  [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]
   UpperCamelCase       =  [coord for idx, coord in enumerate(_UpperCAmelCase) if idx not in irrelevant_indices]

   # turn coordinates into (left, top, left+width, top+height) format
   UpperCamelCase       =  []
   for x, y, w, h in zip(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase):
      UpperCamelCase       =  [x, y, x + w, y + h]
      actual_boxes.append(_UpperCAmelCase)

   # finally, normalize the bounding boxes
   UpperCamelCase       =  []
   for box in actual_boxes:
      normalized_boxes.append(normalize_box(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase))

   assert len(_UpperCAmelCase) == len(_UpperCAmelCase), "Not as many words as there are bounding boxes"

   return words, normalized_boxes


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							['''pixel_values''']







   def __init__(			self ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   lowerCamelCase__ = PILImageResampling.BILINEAR ,   lowerCamelCase__ = True ,   lowerCamelCase__ = 1 / 2_5_5 ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   lowerCamelCase__ = "" ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      super().__init__(**lowerCamelCase__   )
      UpperCamelCase       =  size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
      UpperCamelCase       =  get_size_dict(lowerCamelCase__   )

      UpperCamelCase       =  do_resize
      UpperCamelCase       =  size
      UpperCamelCase       =  resample
      UpperCamelCase       =  do_rescale
      UpperCamelCase       =  rescale_value
      UpperCamelCase       =  do_normalize
      UpperCamelCase       =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      UpperCamelCase       =  image_std if image_std is not None else IMAGENET_STANDARD_STD
      UpperCamelCase       =  apply_ocr
      UpperCamelCase       =  ocr_lang
      UpperCamelCase       =  tesseract_config







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ = PILImageResampling.BILINEAR ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  get_size_dict(lowerCamelCase__   )
      if "height" not in size or "width" not in size:
         raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'   )
      UpperCamelCase       =  (size['''height'''], size['''width'''])
      return resize(lowerCamelCase__ ,   size=lowerCamelCase__ ,   resample=lowerCamelCase__ ,   data_format=lowerCamelCase__ ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__=None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = ChannelDimension.FIRST ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  do_resize if do_resize is not None else self.do_resize
      UpperCamelCase       =  size if size is not None else self.size
      UpperCamelCase       =  get_size_dict(lowerCamelCase__   )
      UpperCamelCase       =  resample if resample is not None else self.resample
      UpperCamelCase       =  do_rescale if do_rescale is not None else self.do_rescale
      UpperCamelCase       =  rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCamelCase       =  do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase       =  image_mean if image_mean is not None else self.image_mean
      UpperCamelCase       =  image_std if image_std is not None else self.image_std
      UpperCamelCase       =  apply_ocr if apply_ocr is not None else self.apply_ocr
      UpperCamelCase       =  ocr_lang if ocr_lang is not None else self.ocr_lang
      UpperCamelCase       =  tesseract_config if tesseract_config is not None else self.tesseract_config

      UpperCamelCase       =  make_list_of_images(lowerCamelCase__   )

      if not valid_images(lowerCamelCase__   ):
         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:
         raise ValueError('''Size must be specified if do_resize 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('''If do_normalize is True, image_mean and image_std must be specified.'''   )

      # All transformations expect numpy arrays.
      UpperCamelCase       =  [to_numpy_array(lowerCamelCase__   ) for image in images]

      # Tesseract OCR to get words + normalized bounding boxes
      if apply_ocr:
         requires_backends(self ,   '''pytesseract'''   )
         UpperCamelCase       =  []
         UpperCamelCase       =  []
         for image in images:
            UpperCamelCase     , UpperCamelCase       =  apply_tesseract(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )
            words_batch.append(lowerCamelCase__   )
            boxes_batch.append(lowerCamelCase__   )

      if do_resize:
         UpperCamelCase       =  [self.resize(image=lowerCamelCase__ ,   size=lowerCamelCase__ ,   resample=lowerCamelCase__   ) for image in images]

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

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

      UpperCamelCase       =  [to_channel_dimension_format(lowerCamelCase__ ,   lowerCamelCase__   ) for image in images]

      UpperCamelCase       =  BatchFeature(data={'''pixel_values''': images} ,   tensor_type=lowerCamelCase__   )

      if apply_ocr:
         UpperCamelCase       =  words_batch
         UpperCamelCase       =  boxes_batch
      return data





