




import socket







def      _a				(						):
					__lowerCAmelCase           =	socket.socket(socket.AF_INET				,				socket.SOCK_STREAM					)
					__lowerCAmelCase           =	socket.gethostname()
					__lowerCAmelCase           =	1_23_12

					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           =	sock.recv(10_24					)
															if not data:
																				break
															out_file.write(_A					)

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


if __name__ == "__main__":
					main()










import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class       a__   ( unittest.TestCase				):






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											torch.manual_seed(0      )
											__lowerCAmelCase           =	UNetaDModel(
											    block_out_channels=(3_2, 6_4)		,      layers_per_block=2		,      sample_size=3_2		,      in_channels=3		,      out_channels=3		,      down_block_types=("DownBlock2D", "AttnDownBlock2D")		,      up_block_types=("AttnUpBlock2D", "UpBlock2D")		,      )
											return model






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.dummy_uncond_unet
											__lowerCAmelCase           =	ScoreSdeVeScheduler()

											__lowerCAmelCase           =	ScoreSdeVePipeline(unet=_A		,      scheduler=_A      )
											sde_ve.to(_A      )
											sde_ve.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=2		,      output_type="numpy"		,      generator=_A      ).images

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=2		,      output_type="numpy"		,      generator=_A		,      return_dict=_A      )[
											    0
											]

											__lowerCAmelCase           =	image[0, -3:, -3:, -1]
											__lowerCAmelCase           =	image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 3_2, 3_2, 3)
											__lowerCAmelCase           =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

											assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2






@slow
@require_torch
class       a__   ( unittest.TestCase				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"google/ncsnpp-church-256"
											__lowerCAmelCase           =	UNetaDModel.from_pretrained(_A      )

											__lowerCAmelCase           =	ScoreSdeVeScheduler.from_pretrained(_A      )

											__lowerCAmelCase           =	ScoreSdeVePipeline(unet=_A		,      scheduler=_A      )
											sde_ve.to(_A      )
											sde_ve.set_progress_bar_config(disable=_A      )

											__lowerCAmelCase           =	torch.manual_seed(0      )
											__lowerCAmelCase           =	sde_ve(num_inference_steps=1_0		,      output_type="numpy"		,      generator=_A      ).images

											__lowerCAmelCase           =	image[0, -3:, -3:, -1]

											assert image.shape == (1, 2_5_6, 2_5_6, 3)

											__lowerCAmelCase           =	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]      )

											assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2




