


def     UpperCamelCase    (      _a       ,		_a = False		)					->							bool:
     '''simple docstring'''





     if n == 2:
          return True
     if not n % 2 or n < 2:
          return False
     if n > 5 and n % 1_0 not in (1, 3, 7, 9):  # can quickly check last digit
          return False
     if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 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
     lowercase_       :List[str]     =       [
         2_0_4_7,
         1_3_7_3_6_5_3,
         2_5_3_2_6_0_0_1,
         3_2_1_5_0_3_1_7_5_1,
         2_1_5_2_3_0_2_8_9_8_7_4_7,
         3_4_7_4_7_4_9_6_6_0_3_8_3,
         3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
         1,
         3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
         1,
         1,
         3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
         3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
     ]

     lowercase_       :int     =       [2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
     for idx, _p in enumerate(_a       ,		1		):
          if n < _p:
               # then we have our last prime to check
               lowercase_       :int     =       primes[:idx]
               break
     lowercase_       :Union[str, Any]     =       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:
          lowercase_       :int     =       False
          for r in range(_a		):
               lowercase_       :Dict     =       pow(_a       ,		d * 2**r       ,		_a		)
               # see article for analysis explanation for m
               if (r == 0 and m == 1) or ((m + 1) % n == 0):
                    lowercase_       :List[str]     =       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     UpperCamelCase    (      )					->							None:
     '''simple docstring'''





     assert not miller_rabin(5_6_1		)
     assert miller_rabin(5_6_3		)
     # 2047

     assert not miller_rabin(8_3_8_2_0_1		)
     assert miller_rabin(8_3_8_2_0_7		)
     # 1_373_653

     assert not miller_rabin(1_7_3_1_6_0_0_1		)
     assert miller_rabin(1_7_3_1_6_0_1_7		)
     # 25_326_001

     assert not miller_rabin(3_0_7_8_3_8_6_6_4_1		)
     assert miller_rabin(3_0_7_8_3_8_6_6_5_3		)
     # 3_215_031_751

     assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1		)
     assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9		)
     # 2_152_302_898_747

     assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7		)
     assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7		)
     # 3_474_749_660_383

     assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1		)
     assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7		)
     # 341_550_071_728_321

     assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1		)
     assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1		)
     # 3_825_123_056_546_413_051

     assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7		)
     assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1		)
     # 318_665_857_834_031_151_167_461

     assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3		)
     assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9		)
     # 3_317_044_064_679_887_385_961_981
     # upper limit for probabilistic test


if __name__ == "__main__":
   test_miller_rabin()






import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


SCREAMING_SNAKE_CASE       :		int         =				logging.get_logger(__name__)






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   lowercase					:	List[str]									=CLIPConfig

   lowercase					:	Tuple									=["""CLIPEncoderLayer"""]
   def __init__(     self   ,							UpperCamelCase_		):
        super().__init__(UpperCamelCase_		)

        lowercase_       :Union[str, Any]     =       CLIPVisionModelWithProjection(config.vision_config		)

        lowercase_       :str     =       nn.Linear(config.vision_config.projection_dim   ,							1		)
        lowercase_       :Optional[int]     =       nn.Linear(config.vision_config.projection_dim   ,							1		)




   @torch.no_grad()
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_=0.5   ,							UpperCamelCase_=0.5		):
        lowercase_       :Optional[int]     =       self.vision_model(UpperCamelCase_		)[0]

        lowercase_       :Optional[int]     =       self.p_head(UpperCamelCase_		)
        lowercase_       :List[Any]     =       nsfw_detected.flatten()
        lowercase_       :List[str]     =       nsfw_detected > p_threshold
        lowercase_       :Optional[int]     =       nsfw_detected.tolist()

        if any(UpperCamelCase_		):
             logger.warning(
                 '''Potential NSFW content was detected in one or more images. A black image will be returned instead.'''
                 ''' Try again with a different prompt and/or seed.'''		)

        for idx, nsfw_detected_ in enumerate(UpperCamelCase_		):
             if nsfw_detected_:
                  lowercase_       :str     =       np.zeros(images[idx].shape		)

        lowercase_       :int     =       self.w_head(UpperCamelCase_		)
        lowercase_       :Union[str, Any]     =       watermark_detected.flatten()
        lowercase_       :int     =       watermark_detected > w_threshold
        lowercase_       :int     =       watermark_detected.tolist()

        if any(UpperCamelCase_		):
             logger.warning(
                 '''Potential watermarked content was detected in one or more images. A black image will be returned instead.'''
                 ''' Try again with a different prompt and/or seed.'''		)

        for idx, watermark_detected_ in enumerate(UpperCamelCase_		):
             if watermark_detected_:
                  lowercase_       :Dict     =       np.zeros(images[idx].shape		)

        return images, nsfw_detected, watermark_detected

