
from __future__ import annotations

import math





def      lowerCamelCase_					(							lowerCamelCase__       ):

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5      ,					int(math.sqrt(lowerCamelCase__       ) + 1       )      ,					6       ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True


__A							=[num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]





def      lowerCamelCase_					(							lowerCamelCase__       ):
				if not isinstance(lowerCamelCase__      ,					lowerCamelCase__       ):
								raise ValueError("n must be an integer"       )
				if n <= 0:
								raise ValueError("n must be >= 0"       )

				lowerCamelCase_         =     []
				for num in range(len(lowerCamelCase__       )       ):
								lowerCamelCase_         =     0
								while 2 * i * i <= odd_composites[num]:
												lowerCamelCase_         =     odd_composites[num] - 2 * i * i
												if is_prime(lowerCamelCase__       ):
																break
												i += 1
								else:
												list_nums.append(odd_composites[num]       )
												if len(lowerCamelCase__       ) == n:
																return list_nums

				return []





def      lowerCamelCase_					(							):
				return compute_nums(1       )[0]


if __name__ == "__main__":
			print(F"""{solution() = }""")




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

from ...utils import logging


__A							=logging.get_logger(__name__)




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					CLIPConfig

	lowerCAmelCase__   =					['CLIPEncoderLayer']



	def __init__(       self		,    lowercase					)							->			int:
					super().__init__(lowercase					)

					lowerCamelCase_         =     CLIPVisionModelWithProjection(config.vision_config					)

					lowerCamelCase_         =     nn.Linear(config.vision_config.projection_dim		,    1					)
					lowerCamelCase_         =     nn.Linear(config.vision_config.projection_dim		,    1					)



	@torch.no_grad()
	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase=0.5		,    lowercase=0.5					)							->			int:
					lowerCamelCase_         =     self.vision_model(lowercase					)[0]

					lowerCamelCase_         =     self.p_head(lowercase					)
					lowerCamelCase_         =     nsfw_detected.flatten()
					lowerCamelCase_         =     nsfw_detected > p_threshold
					lowerCamelCase_         =     nsfw_detected.tolist()

					if any(lowercase					):
									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(lowercase					):
									if nsfw_detected_:
													lowerCamelCase_         =     np.zeros(images[idx].shape					)

					lowerCamelCase_         =     self.w_head(lowercase					)
					lowerCamelCase_         =     watermark_detected.flatten()
					lowerCamelCase_         =     watermark_detected > w_threshold
					lowerCamelCase_         =     watermark_detected.tolist()

					if any(lowercase					):
									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(lowercase					):
									if watermark_detected_:
													lowerCamelCase_         =     np.zeros(images[idx].shape					)

					return images, nsfw_detected, watermark_detected


