



def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			bool = False    ):


						'''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
						snake_case_   : List[Any]						=			[
						    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,
						]

						snake_case_   : Dict						=			[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(__UpperCamelCase					, 1    ):
												if n < _p:
																		# then we have our last prime to check
																		snake_case_   : Optional[int]						=			primes[:idx]
																		break
						snake_case_   : Tuple						=			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:
												snake_case_   : List[str]						=			False
												for r in range(__UpperCamelCase    ):
																		snake_case_   : int						=			pow(__UpperCamelCase					, d * 2**r					, __UpperCamelCase    )
																		# see article for analysis explanation for m
																		if (r == 0 and m == 1) or ((m + 1) % n == 0):
																								snake_case_   : Optional[Any]						=			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 	__lowerCAmelCase						(		):


						'''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()






"""simple docstring"""





import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


__lowerCAmelCase       :					Optional[int]      					=					logging.get_logger(__name__)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Tuple					, __UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						snake_case_   : Union[str, Any]						=			nn.functional.normalize(__UpperCamelCase    )
						snake_case_   : Tuple						=			nn.functional.normalize(__UpperCamelCase    )
						return torch.mm(__UpperCamelCase					, normalized_text_embeds.t()    )






class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       CLIPConfig

		_lowerCamelCase			  =       ['''CLIPEncoderLayer''']





		def __init__(					self							,    _lowercase   ) ->						Union[str, Any]:







								'''simple docstring'''

								super().__init__(_lowercase   )

								snake_case_   : Tuple						=			CLIPVisionModel(config.vision_config   )
								snake_case_   : int						=			nn.Linear(config.vision_config.hidden_size							,    config.projection_dim							,    bias=_lowercase   )

								snake_case_   : Optional[Any]						=			nn.Parameter(torch.ones(1_7							,    config.projection_dim   )							,    requires_grad=_lowercase   )
								snake_case_   : Dict						=			nn.Parameter(torch.ones(3							,    config.projection_dim   )							,    requires_grad=_lowercase   )

								snake_case_   : Any						=			nn.Parameter(torch.ones(1_7   )							,    requires_grad=_lowercase   )
								snake_case_   : List[str]						=			nn.Parameter(torch.ones(3   )							,    requires_grad=_lowercase   )





		@torch.no_grad()
		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase   ) ->						Any:







								'''simple docstring'''

								snake_case_   : int						=			self.vision_model(_lowercase   )[1]  # pooled_output
								snake_case_   : str						=			self.visual_projection(_lowercase   )

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								snake_case_   : Dict						=			cosine_distance(_lowercase							,    self.special_care_embeds   ).cpu().float().numpy()
								snake_case_   : List[str]						=			cosine_distance(_lowercase							,    self.concept_embeds   ).cpu().float().numpy()

								snake_case_   : Any						=			[]
								snake_case_   : Any						=			image_embeds.shape[0]
								for i in range(_lowercase   ):
														snake_case_   : List[Any]						=			{"""special_scores""": {}, """special_care""": [], """concept_scores""": {}, """bad_concepts""": []}

														# increase this value to create a stronger `nfsw` filter
														# at the cost of increasing the possibility of filtering benign images
														snake_case_   : int						=			0.0

														for concept_idx in range(len(special_cos_dist[0]   )   ):
																				snake_case_   : List[str]						=			special_cos_dist[i][concept_idx]
																				snake_case_   : Union[str, Any]						=			self.special_care_embeds_weights[concept_idx].item()
																				snake_case_   : Tuple						=			round(concept_cos - concept_threshold + adjustment							,    3   )
																				if result_img["special_scores"][concept_idx] > 0:
																										result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]}   )
																										snake_case_   : Dict						=			0.01

														for concept_idx in range(len(cos_dist[0]   )   ):
																				snake_case_   : int						=			cos_dist[i][concept_idx]
																				snake_case_   : List[Any]						=			self.concept_embeds_weights[concept_idx].item()
																				snake_case_   : List[str]						=			round(concept_cos - concept_threshold + adjustment							,    3   )
																				if result_img["concept_scores"][concept_idx] > 0:
																										result_img["bad_concepts"].append(_lowercase   )

														result.append(_lowercase   )

								snake_case_   : Union[str, Any]						=			[len(res["""bad_concepts"""]   ) > 0 for res in result]

								return images, has_nsfw_concepts





		@torch.no_grad()
		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Optional[Any]						=			self.vision_model(_lowercase   )[1]  # pooled_output
								snake_case_   : List[str]						=			self.visual_projection(_lowercase   )

								snake_case_   : str						=			cosine_distance(_lowercase							,    self.special_care_embeds   )
								snake_case_   : Optional[int]						=			cosine_distance(_lowercase							,    self.concept_embeds   )

								# increase this value to create a stronger `nsfw` filter
								# at the cost of increasing the possibility of filtering benign images
								snake_case_   : Tuple						=			0.0

								snake_case_   : List[Any]						=			special_cos_dist - self.special_care_embeds_weights + adjustment
								# special_scores = special_scores.round(decimals=3)
								snake_case_   : str						=			torch.any(special_scores > 0							,    dim=1   )
								snake_case_   : List[str]						=			special_care * 0.01
								snake_case_   : Optional[int]						=			special_adjustment.unsqueeze(1   ).expand(-1							,    cos_dist.shape[1]   )

								snake_case_   : Optional[Any]						=			(cos_dist - self.concept_embeds_weights) + special_adjustment
								# concept_scores = concept_scores.round(decimals=3)
								snake_case_   : str						=			torch.any(concept_scores > 0							,    dim=1   )

								return images, has_nsfw_concepts

