
import numpy as np




class        _SCREAMING_SNAKE_CASE	:



	def __init__(       self					)							->			Optional[Any]:
					lowerCamelCase_         =     (0, 0)
					lowerCamelCase_         =     None
					lowerCamelCase_         =     0
					lowerCamelCase_         =     0
					lowerCamelCase_         =     0



	def __eq__(       self		,    lowercase					)							->			Any:
					return self.position == cell.position



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					print(self.position					)




class        _SCREAMING_SNAKE_CASE	:



	def __init__(       self		,    lowercase=(5, 5)					)							->			Any:
					lowerCamelCase_         =     np.zeros(lowercase					)
					lowerCamelCase_         =     world_size[0]
					lowerCamelCase_         =     world_size[1]



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					print(self.w					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			Union[str, Any]:
					lowerCamelCase_         =     [
					    (-1, -1),
					    (-1, 0),
					    (-1, 1),
					    (0, -1),
					    (0, 1),
					    (1, -1),
					    (1, 0),
					    (1, 1),
					]
					lowerCamelCase_         =     cell.position[0]
					lowerCamelCase_         =     cell.position[1]
					lowerCamelCase_         =     []
					for n in neughbour_cord:
									lowerCamelCase_         =     current_x + n[0]
									lowerCamelCase_         =     current_y + n[1]
									if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
													lowerCamelCase_         =     Cell()
													lowerCamelCase_         =     (x, y)
													lowerCamelCase_         =     cell
													neighbours.append(lowercase					)
					return neighbours





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				_open.append(lowerCamelCase__       )

				while _open:
								lowerCamelCase_         =     np.argmin([n.f for n in _open]       )
								lowerCamelCase_         =     _open[min_f]
								_closed.append(_open.pop(lowerCamelCase__       )       )
								if current == goal:
												break
								for n in world.get_neigbours(lowerCamelCase__       ):
												for c in _closed:
																if c == n:
																				continue
												lowerCamelCase_         =     current.g + 1
												lowerCamelCase_      ,       lowerCamelCase_         =     n.position
												lowerCamelCase_      ,       lowerCamelCase_         =     goal.position
												lowerCamelCase_         =     (ya - ya) ** 2 + (xa - xa) ** 2
												lowerCamelCase_         =     n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(lowerCamelCase__       )
				lowerCamelCase_         =     []
				while current.parent is not None:
								path.append(current.position       )
								lowerCamelCase_         =     current.parent
				path.append(current.position       )
				return path[::-1]


if __name__ == "__main__":
			__A							=Gridworld()
			# Start position and goal
			__A							=Cell()
			__A							=(0, 0)
			__A							=Cell()
			__A							=(4, 4)
			print(F"""path from {start.position} to {goal.position}""")
			__A							=astar(world, start, goal)
			#   Just for visual reasons.
			for i in s:
						__A							=1
			print(world.w)




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


