






"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class  _UpperCAmelCase	(			_A							):




					def __init__(    self					:						List[str]   ,						A					:						str   ,						A					:						str       )  ->  Optional[int]:
							super().__init__()
							self.register_modules(unet=A   ,						scheduler=A       )






					@torch.no_grad()
					def __call__(    self					:						Union[str, Any]   ,						A					:						int = 1   ,						A					:						Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,						A					:						int = 10_00   ,						A					:						Optional[str] = "pil"   ,						A					:						bool = True   ,						)  ->  Union[ImagePipelineOutput, Tuple]:
							# Sample gaussian noise to begin loop
							if isinstance(self.unet.config.sample_size   ,						A       ):
									lowercase_			:			Optional[int]								=       (
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
							else:
									lowercase_			:			Tuple								=       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if self.device.type == "mps":
									# randn does not work reproducibly on mps
									lowercase_			:			Any								=       randn_tensor(A   ,						generator=A       )
									lowercase_			:			int								=       image.to(self.device       )
							else:
									lowercase_			:			Any								=       randn_tensor(A   ,						generator=A   ,						device=self.device       )

							# set step values
							self.scheduler.set_timesteps(A       )

							for t in self.progress_bar(self.scheduler.timesteps       ):
									# 1. predict noise model_output
									lowercase_			:			Any								=       self.unet(A   ,						A       ).sample

									# 2. compute previous image: x_t -> x_t-1
									lowercase_			:			Tuple								=       self.scheduler.step(A   ,						A   ,						A   ,						generator=A       ).prev_sample

							lowercase_			:			int								=       (image / 2 + 0.5).clamp(0   ,						1       )
							lowercase_			:			List[Any]								=       image.cpu().permute(0   ,						2   ,						3   ,						1       ).numpy()
							if output_type == "pil":
									lowercase_			:			Optional[Any]								=       self.numpy_to_pil(A       )

							if not return_dict:
									return (image,)

							return ImagePipelineOutput(images=A       )











"""simple docstring"""



def 						lowercase   (    __snake_case    :			str = "mytext.txt"		, __snake_case    :			bool = False     ):
		import socket

		lowercase_			:			List[str]								=       1_2_3_1_2  # Reserve a port for your service.
		lowercase_			:			Dict								=       socket.socket()  # Create a socket object
		lowercase_			:			int								=       socket.gethostname()  # Get local machine name
		sock.bind((host, port)     )  # Bind to the port
		sock.listen(5     )  # Now wait for client connection.

		print('''Server listening....'''     )

		while True:
				lowercase_	, lowercase_			:			int								=       sock.accept()  # Establish connection with client.
				print(F'''Got connection from {addr}'''     )
				lowercase_			:			List[Any]								=       conn.recv(1_0_2_4     )
				print(F'''Server received: {data = }'''     )

				with open(__snake_case		, '''rb'''     ) as in_file:
						lowercase_			:			List[Any]								=       in_file.read(1_0_2_4     )
						while data:
								conn.send(__snake_case     )
								print(F'''Sent {data!r}'''     )
								lowercase_			:			List[Any]								=       in_file.read(1_0_2_4     )

				print('''Done sending'''     )
				conn.close()
				if testing:  # Allow the test to complete
						break

		sock.shutdown(1     )
		sock.close()


if __name__ == "__main__":
						send_file()



