




def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int     ,							lowercase     :	bool = False					):


      '''simple docstring'''



      if n == 2:
            return True
      if not n % 2 or n < 2:
            return False
      if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
            return False
      if n > 3_31_70_44_06_46_79_88_73_85_96_19_81 and not allow_probable:
            raise ValueError(
                'Warning: upper bound of deterministic test is exceeded. '
                'Pass allow_probable=True to allow probabilistic test. '
                'A return value of True indicates a probable prime.'					)
      # array bounds provided by analysis
      lowerCamelCase_							=   [
          20_47,
          1_37_36_53,
          25_32_60_01,
          32_15_03_17_51,
          2_15_23_02_89_87_47,
          3_47_47_49_66_03_83,
          3_41_55_00_71_72_83_21,
          1,
          3_82_51_23_05_65_46_41_30_51,
          1,
          1,
          31_86_65_85_78_34_03_11_51_16_74_61,
          3_31_70_44_06_46_79_88_73_85_96_19_81,
      ]

      lowerCamelCase_							=   [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
      for idx, _p in enumerate(lowercase     ,							1					):
            if n < _p:
                  # then we have our last prime to check
                  lowerCamelCase_							=   primes[:idx]
                  break
      lowerCamelCase_					,    lowerCamelCase_							=   n - 1, 0
      # break up n -1 into a power of 2 (s) and
      # remaining odd component
      # essentially, solve for d * 2 ** s == n - 1
      while d % 2 == 0:
            d //= 2
            s += 1
      for prime in plist:
            lowerCamelCase_							=   False
            for r in range(lowercase					):
                  lowerCamelCase_							=   pow(lowercase     ,							d * 2**r     ,							lowercase					)
                  # see article for analysis explanation for m
                  if (r == 0 and m == 1) or ((m + 1) % n == 0):
                        lowerCamelCase_							=   True
                        # this loop will not determine compositeness
                        break
            if pr:
                  continue
            # if pr is False, then the above loop never evaluated to true,
            # and the n MUST be composite
            return False
      return True

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



      assert not miller_rabin(5_61					)
      assert miller_rabin(5_63					)
      # 2047

      assert not miller_rabin(83_82_01					)
      assert miller_rabin(83_82_07					)
      # 1_373_653

      assert not miller_rabin(17_31_60_01					)
      assert miller_rabin(17_31_60_17					)
      # 25_326_001

      assert not miller_rabin(30_78_38_66_41					)
      assert miller_rabin(30_78_38_66_53					)
      # 3_215_031_751

      assert not miller_rabin(1_71_30_45_57_48_01					)
      assert miller_rabin(1_71_30_45_57_48_19					)
      # 2_152_302_898_747

      assert not miller_rabin(2_77_97_99_72_83_07					)
      assert miller_rabin(2_77_97_99_72_83_27					)
      # 3_474_749_660_383

      assert not miller_rabin(1_13_85_00_23_90_94_41					)
      assert miller_rabin(1_13_85_00_23_90_95_27					)
      # 341_550_071_728_321

      assert not miller_rabin(1_27_50_41_01_88_48_80_43_51					)
      assert miller_rabin(1_27_50_41_01_88_48_80_43_91					)
      # 3_825_123_056_546_413_051

      assert not miller_rabin(7_96_66_46_44_58_50_77_87_79_18_67					)
      assert miller_rabin(7_96_66_46_44_58_50_77_87_79_19_51					)
      # 318_665_857_834_031_151_167_461

      assert not miller_rabin(55_28_40_67_74_46_64_78_97_66_03_33					)
      assert miller_rabin(55_28_40_67_74_46_64_78_97_66_03_59					)
      # 3_317_044_064_679_887_385_961_981
      # upper limit for probabilistic test


if __name__ == "__main__":
 test_miller_rabin()









import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						['''image_processor''', '''tokenizer''']
 UpperCamelCase						       =						'''OwlViTImageProcessor'''
 UpperCamelCase						       =						('''CLIPTokenizer''', '''CLIPTokenizerFast''')

 def __init__(    self :  Optional[Any]						,							A_ :  Tuple=None						,							A_ :  Tuple=None						,							**A_ :  List[str]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   None
       if "feature_extractor" in kwargs:
             warnings.warn(
                 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                 ' instead.'						,							A_						,							)
             lowerCamelCase_							=   kwargs.pop('feature_extractor'  )

       lowerCamelCase_							=   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
             raise ValueError('You need to specify an `image_processor`.'  )
       if tokenizer is None:
             raise ValueError('You need to specify a `tokenizer`.'  )

       super().__init__(A_						,							A_  )

 def __call__(    self :  List[str]						,							A_ :  List[str]=None						,							A_ :  List[Any]=None						,							A_ :  Dict=None						,							A_ :  Tuple="max_length"						,							A_ :  int="np"						,							**A_ :  Union[str, Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       if text is None and query_images is None and images is None:
             raise ValueError(
                 'You have to specify at least one text or query image or image. All three cannot be none.'  )

       if text is not None:
             if isinstance(A_						,							A_  ) or (isinstance(A_						,							A_  ) and not isinstance(text[0]						,							A_  )):
                   lowerCamelCase_							=   [self.tokenizer(A_						,							padding=A_						,							return_tensors=A_						,							**A_  )]

             elif isinstance(A_						,							A_  ) and isinstance(text[0]						,							A_  ):
                   lowerCamelCase_							=   []

                   # Maximum number of queries across batch
                   lowerCamelCase_							=   max([len(A_  ) for t in text]  )

                   # Pad all batch samples to max number of text queries
                   for t in text:
                         if len(A_  ) != max_num_queries:
                               lowerCamelCase_							=   t + [' '] * (max_num_queries - len(A_  ))

                         lowerCamelCase_							=   self.tokenizer(A_						,							padding=A_						,							return_tensors=A_						,							**A_  )
                         encodings.append(A_  )
             else:
                   raise TypeError('Input text should be a string, a list of strings or a nested list of strings'  )

             if return_tensors == "np":
                   lowerCamelCase_							=   np.concatenate([encoding['input_ids'] for encoding in encodings]						,							axis=0  )
                   lowerCamelCase_							=   np.concatenate([encoding['attention_mask'] for encoding in encodings]						,							axis=0  )

             elif return_tensors == "jax" and is_flax_available():
                   import jax.numpy as jnp

                   lowerCamelCase_							=   jnp.concatenate([encoding['input_ids'] for encoding in encodings]						,							axis=0  )
                   lowerCamelCase_							=   jnp.concatenate([encoding['attention_mask'] for encoding in encodings]						,							axis=0  )

             elif return_tensors == "pt" and is_torch_available():
                   import torch

                   lowerCamelCase_							=   torch.cat([encoding['input_ids'] for encoding in encodings]						,							dim=0  )
                   lowerCamelCase_							=   torch.cat([encoding['attention_mask'] for encoding in encodings]						,							dim=0  )

             elif return_tensors == "tf" and is_tf_available():
                   import tensorflow as tf

                   lowerCamelCase_							=   tf.stack([encoding['input_ids'] for encoding in encodings]						,							axis=0  )
                   lowerCamelCase_							=   tf.stack([encoding['attention_mask'] for encoding in encodings]						,							axis=0  )

             else:
                   raise ValueError('Target return tensor type could not be returned'  )

             lowerCamelCase_							=   BatchEncoding()
             lowerCamelCase_							=   input_ids
             lowerCamelCase_							=   attention_mask

       if query_images is not None:
             lowerCamelCase_							=   BatchEncoding()
             lowerCamelCase_							=   self.image_processor(
                 A_						,							return_tensors=A_						,							**A_  ).pixel_values
             lowerCamelCase_							=   query_pixel_values

       if images is not None:
             lowerCamelCase_							=   self.image_processor(A_						,							return_tensors=A_						,							**A_  )

       if text is not None and images is not None:
             lowerCamelCase_							=   image_features.pixel_values
             return encoding
       elif query_images is not None and images is not None:
             lowerCamelCase_							=   image_features.pixel_values
             return encoding
       elif text is not None or query_images is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**A_  )						,							tensor_type=A_  )

 def     a__			(    self :  Tuple						,							*A_ :  Dict						,							**A_ :  Dict  )       ->     Any:

       """simple docstring"""






       return self.image_processor.post_process(*A_						,							**A_  )

 def     a__			(    self :  List[str]						,							*A_ :  Any						,							**A_ :  List[Any]  )       ->     Optional[int]:

       """simple docstring"""






       return self.image_processor.post_process_object_detection(*A_						,							**A_  )

 def     a__			(    self :  Any						,							*A_ :  str						,							**A_ :  List[Any]  )       ->     Any:

       """simple docstring"""






       return self.image_processor.post_process_image_guided_detection(*A_						,							**A_  )

 def     a__			(    self :  Union[str, Any]						,							*A_ :  Any						,							**A_ :  Union[str, Any]  )       ->     str:

       """simple docstring"""






       return self.tokenizer.batch_decode(*A_						,							**A_  )

 def     a__			(    self :  Optional[int]						,							*A_ :  List[Any]						,							**A_ :  int  )       ->     int:

       """simple docstring"""






       return self.tokenizer.decode(*A_						,							**A_  )

 @property
 def     a__			(    self :  List[Any]  )       ->     List[Any]:

       """simple docstring"""






       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,							A_						,							)
       return self.image_processor_class





 @property
 def     a__			(    self :  str  )       ->     List[str]:

       """simple docstring"""






       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,							A_						,							)
       return self.image_processor



