






'''simple docstring'''







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

from ...utils import logging


__lowerCAmelCase       		=      logging.get_logger(__name__)





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				CLIPConfig

			snake_case__  =				["CLIPEncoderLayer"]






			def __init__( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			CLIPConfig					)			->			Dict:
							super().__init__(__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[Any]											=      CLIPVisionModelWithProjection(config.vision_config					)

							a_		:  List[Any]											=      nn.Linear(config.vision_config.projection_dim  ,						1					)
							a_		:  Any											=      nn.Linear(config.vision_config.projection_dim  ,						1					)







			@torch.no_grad()
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Any  ,						__SCREAMING_SNAKE_CASE      :			Any=0.5  ,						__SCREAMING_SNAKE_CASE      :			List[str]=0.5					)			->			List[str]:
							a_		:  Any											=      self.vision_model(__SCREAMING_SNAKE_CASE					)[0]

							a_		:  Optional[int]											=      self.p_head(__SCREAMING_SNAKE_CASE					)
							a_		:  str											=      nsfw_detected.flatten()
							a_		:  Dict											=      nsfw_detected > p_threshold
							a_		:  str											=      nsfw_detected.tolist()

							if any(__SCREAMING_SNAKE_CASE					):
											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(__SCREAMING_SNAKE_CASE					):
											if nsfw_detected_:
															a_		:  Dict											=      np.zeros(images[idx].shape					)

							a_		:  List[str]											=      self.w_head(__SCREAMING_SNAKE_CASE					)
							a_		:  Dict											=      watermark_detected.flatten()
							a_		:  Union[str, Any]											=      watermark_detected > w_threshold
							a_		:  List[str]											=      watermark_detected.tolist()

							if any(__SCREAMING_SNAKE_CASE					):
											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(__SCREAMING_SNAKE_CASE					):
											if watermark_detected_:
															a_		:  Optional[Any]											=      np.zeros(images[idx].shape					)

							return images, nsfw_detected, watermark_detected












'''simple docstring'''







from __future__ import annotations





def 		_UpperCAmelCase   (							__A							:   tuple[int, int]						,		__A							:   int      ):
				a_				,			a_		:  List[str]											=      position
				a_		:  Optional[int]											=      [
				    (y + 1, x + 2),
				    (y - 1, x + 2),
				    (y + 1, x - 2),
				    (y - 1, x - 2),
				    (y + 2, x + 1),
				    (y + 2, x - 1),
				    (y - 2, x + 1),
				    (y - 2, x - 1),
				]
				a_		:  Any											=      []

				for position in positions:
								a_				,			a_		:  Dict											=      position
								if 0 <= y_test < n and 0 <= x_test < n:
												permissible_positions.append(__A      )

				return permissible_positions





def 		_UpperCAmelCase   (							__A							:   list[list[int]]      ):
				return not any(elem == 0 for row in board for elem in row      )





def 		_UpperCAmelCase   (							__A							:   list[list[int]]						,		__A							:   tuple[int, int]						,		__A							:   int      ):

				if is_complete(__A      ):
								return True

				for position in get_valid_pos(__A						,		len(__A      )      ):
								a_				,			a_		:  Dict											=      position

								if board[y][x] == 0:
												a_		:  Optional[Any]											=      curr + 1
												if open_knight_tour_helper(__A						,		__A						,		curr + 1      ):
																return True
												a_		:  Tuple											=      0

				return False





def 		_UpperCAmelCase   (							__A							:   int      ):
				a_		:  List[str]											=      [[0 for i in range(__A      )] for j in range(__A      )]

				for i in range(__A      ):
								for j in range(__A      ):
												a_		:  Optional[Any]											=      1
												if open_knight_tour_helper(__A						,		(i, j)						,		1      ):
																return board
												a_		:  Union[str, Any]											=      0

				a_		:  Dict											=      f'Open Kight Tour cannot be performed on a board of size {n}'
				raise ValueError(__A      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




