



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

from ...utils import logging


UpperCAmelCase__   =       logging.get_logger(__name__)







class 			__lowerCAmelCase    (			A						):
			UpperCamelCase   					=     CLIPConfig

			UpperCamelCase   					=     ['''CLIPEncoderLayer''']



			def __init__(						self   :  Optional[Any] ,		A   :  str)   ->    List[Any]:



						"""simple docstring"""






						super().__init__(A)

						_UpperCAmelCase		       =   CLIPVisionModelWithProjection(config.vision_config)

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



			@torch.no_grad()
			def     _lowerCamelCase       (						self   :  List[Any] ,		A   :  Tuple ,		A   :  Any ,		A   :  Any=0.5 ,		A   :  Optional[int]=0.5)   ->    Any:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.vision_model(A)[0]

						_UpperCAmelCase		       =   self.p_head(A)
						_UpperCAmelCase		       =   nsfw_detected.flatten()
						_UpperCAmelCase		       =   nsfw_detected > p_threshold
						_UpperCAmelCase		       =   nsfw_detected.tolist()

						if any(A):
									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(A):
									if nsfw_detected_:
												_UpperCAmelCase		       =   np.zeros(images[idx].shape)

						_UpperCAmelCase		       =   self.w_head(A)
						_UpperCAmelCase		       =   watermark_detected.flatten()
						_UpperCAmelCase		       =   watermark_detected > w_threshold
						_UpperCAmelCase		       =   watermark_detected.tolist()

						if any(A):
									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(A):
									if watermark_detected_:
												_UpperCAmelCase		       =   np.zeros(images[idx].shape)

						return images, nsfw_detected, watermark_detected






"""simple docstring"""






import socket







def      _snake_case			(				):
				_lowerCamelCase     :			List[Any]    =    socket.socket(socket.AF_INET							,    socket.SOCK_STREAM		)
				_lowerCamelCase     :			Union[str, Any]    =    socket.gethostname()
				_lowerCamelCase     :			List[Any]    =    12312

				sock.connect((host, port)		)
				sock.send(B'Hello server!'		)

				with open('Received_file'							,    'wb'		) as out_file:
								print('File opened'		)
								print('Receiving data...'		)
								while True:
												_lowerCamelCase     :			int    =    sock.recv(1024		)
												if not data:
																break
												out_file.write(lowercase__		)

				print('Successfully received the file'		)
				sock.close()
				print('Connection closed'		)


if __name__ == "__main__":
			main()