




import math


class 							__UpperCamelCase			:





				'''simple docstring'''



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_						):
											lowerCAmelCase   =		0.0
											lowerCAmelCase   =		0.0
											for i in range(len(UpperCAmelCase_						)						):
																		da += math.pow((sample[i] - weights[0][i])       , 2						)
																		da += math.pow((sample[i] - weights[1][i])       , 2						)
																		return 0 if da > da else 1
											return 0






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
											for i in range(len(UpperCAmelCase_						)						):
																		weights[j][i] += alpha * (sample[i] - weights[j][i])
											return weights




def       UpperCAmelCase       (     ):
							# Training Examples ( m, n )
							lowerCAmelCase   =		[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

							# weight initialization ( n, C )
							lowerCAmelCase   =		[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

							# training
							lowerCAmelCase   =		SelfOrganizingMap()
							lowerCAmelCase   =		3
							lowerCAmelCase   =		0.5

							for _ in range(_snake_case							):
														for j in range(len(_snake_case							)							):
																					# training sample
																					lowerCAmelCase   =		training_samples[j]

																					# Compute the winning vector
																					lowerCAmelCase   =		self_organizing_map.get_winner(_snake_case						, _snake_case							)

																					# Update the winning vector
																					lowerCAmelCase   =		self_organizing_map.update(_snake_case						, _snake_case						, _snake_case						, _snake_case							)

    # classify test sample
							lowerCAmelCase   =		[0, 0, 0, 1]
							lowerCAmelCase   =		self_organizing_map.get_winner(_snake_case						, _snake_case							)

							# results
							print(F"""Clusters that the test sample belongs to : {winner}"""							)
							print(F"""Weights that have been trained : {weights}"""							)


# running the main() function
if __name__ == "__main__":
		main()









# 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 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Any										=1



				@register_to_config
				def __init__( self       , UpperCAmelCase_=20_00       , UpperCAmelCase_=0.1       , UpperCAmelCase_=20       , UpperCAmelCase_=1E-3						):
											lowerCAmelCase   =		None
											lowerCAmelCase   =		None
											lowerCAmelCase   =		None



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):
											lowerCAmelCase   =		torch.linspace(1       , self.config.sampling_eps       , UpperCAmelCase_       , device=UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_=None						):
											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   =		(
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											lowerCAmelCase   =		torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff						)						)
											lowerCAmelCase   =		std.flatten()
											while len(std.shape						) < len(score.shape						):
																		lowerCAmelCase   =		std.unsqueeze(-1						)
											lowerCAmelCase   =		-score / std

											# compute
											lowerCAmelCase   =		-1.0 / len(self.timesteps						)

											lowerCAmelCase   =		self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											lowerCAmelCase   =		beta_t.flatten()
											while len(beta_t.shape						) < len(x.shape						):
																		lowerCAmelCase   =		beta_t.unsqueeze(-1						)
											lowerCAmelCase   =		-0.5 * beta_t * x

											lowerCAmelCase   =		torch.sqrt(UpperCAmelCase_						)
											lowerCAmelCase   =		drift - diffusion**2 * score
											lowerCAmelCase   =		x + drift * dt

											# add noise
											lowerCAmelCase   =		randn_tensor(x.shape       , layout=x.layout       , generator=UpperCAmelCase_       , device=x.device       , dtype=x.dtype						)
											lowerCAmelCase   =		x_mean + diffusion * math.sqrt(-dt						) * noise

											return x, x_mean






				def __len__( self						):
											return self.config.num_train_timesteps



