





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin

class      lowerCAmelCase							(      __UpperCamelCase,						__UpperCamelCase    ):
	UpperCAmelCase__									=	1
	@register_to_config
	def __init__(   self						:   Tuple    ,  UpperCAmelCase						:   str=2000    ,  UpperCAmelCase						:   Optional[int]=0.1    ,  UpperCAmelCase						:   Tuple=20    ,  UpperCAmelCase						:   int=1e-3			)    ->			int:
					lowerCamelCase__					:   Tuple      				=	None
					lowerCamelCase__					:   int      				=	None
					lowerCamelCase__					:   Any      				=	None
	def   A_			(   self						:   Any    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Union[str, torch.device] = None			)    ->			Tuple:
					lowerCamelCase__					:   Dict      				=	torch.linspace(1    ,  self.config.sampling_eps    ,  UpperCAmelCase    ,  device=UpperCAmelCase			)
	def   A_			(   self						:   Any    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   List[Any]=None			)    ->			Union[str, Any]:
					if self.timesteps is None:
									raise ValueError(
									    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'			)

					# TODO(Patrick) better comments + non-PyTorch
					# postprocess model score
					lowerCamelCase__					:   Optional[Any]      				=	(
					    -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
					)
					lowerCamelCase__					:   List[Any]      				=	torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
					lowerCamelCase__					:   Union[str, Any]      				=	std.flatten()
					while len(std.shape			) < len(score.shape			):
									lowerCamelCase__					:   Any      				=	std.unsqueeze(-1			)
					lowerCamelCase__					:   Union[str, Any]      				=	-score / std

					# compute
					lowerCamelCase__					:   Optional[int]      				=	-1.0 / len(self.timesteps			)

					lowerCamelCase__					:   str      				=	self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
					lowerCamelCase__					:   str      				=	beta_t.flatten()
					while len(beta_t.shape			) < len(x.shape			):
									lowerCamelCase__					:   str      				=	beta_t.unsqueeze(-1			)
					lowerCamelCase__					:   Union[str, Any]      				=	-0.5 * beta_t * x

					lowerCamelCase__					:   List[str]      				=	torch.sqrt(UpperCAmelCase			)
					lowerCamelCase__					:   Optional[Any]      				=	drift - diffusion**2 * score
					lowerCamelCase__					:   Tuple      				=	x + drift * dt

					# add noise
					lowerCamelCase__					:   Any      				=	randn_tensor(x.shape    ,  layout=x.layout    ,  generator=UpperCAmelCase    ,  device=x.device    ,  dtype=x.dtype			)
					lowerCamelCase__					:   Union[str, Any]      				=	x_mean + diffusion * math.sqrt(-dt			) * noise

					return x, x_mean







	def __len__(   self						:   Union[str, Any]			)    ->			Optional[int]:
					return self.config.num_train_timesteps












import socket


def  SCREAMING_SNAKE_CASE   ( )    ->  Any:
    lowerCamelCase__					:   int      				=	socket.socket(socket.AF_INET		,			socket.SOCK_STREAM				)
    lowerCamelCase__					:   Optional[int]      				=	socket.gethostname()
    lowerCamelCase__					:   Optional[int]      				=	1_2312

    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__					:   str      				=	sock.recv(1024				)
            if not data:
                break
            out_file.write(_UpperCAmelCase				)

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


if __name__ == "__main__":
    main()





