

"""simple docstring"""




import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowerCAmelCase							:  Optional[Any]            =						False



class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					List[Any]=32):
									set_seed(0)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	UNetaDModel(sample_size=lowerCAmelCase__       ,						in_channels=3       ,						out_channels=3)
									SCREAMING_SNAKE_CASE_:			str  		=	torch.optim.SGD(model.parameters()       ,						lr=0.0001)
									return model, optimizer




			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	DDPMScheduler(
									    num_train_timesteps=1000       ,						beta_start=0.0001       ,						beta_end=0.02       ,						beta_schedule="linear"       ,						clip_sample=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			Any  		=	DDIMScheduler(
									    num_train_timesteps=1000       ,						beta_start=0.0001       ,						beta_end=0.02       ,						beta_schedule="linear"       ,						clip_sample=lowerCAmelCase__       ,						)

									assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

									# shared batches for DDPM and DDIM
									set_seed(0)
									SCREAMING_SNAKE_CASE_:			Any  		=	[torch.randn((4, 3, 32, 32)).clip(-1       ,						1).to(lowerCAmelCase__) for _ in range(4)]
									SCREAMING_SNAKE_CASE_:			Dict  		=	[torch.randn((4, 3, 32, 32)).to(lowerCAmelCase__) for _ in range(4)]
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[torch.randint(0       ,						1000       ,						(4,)).long().to(lowerCAmelCase__) for _ in range(4)]

									# train with a DDPM scheduler
									SCREAMING_SNAKE_CASE_:			str  		=	self.get_model_optimizer(resolution=32)
									model.train().to(lowerCAmelCase__)
									for i in range(4):
															optimizer.zero_grad()
															SCREAMING_SNAKE_CASE_:			str  		=	ddpm_scheduler.add_noise(clean_images[i]       ,						noise[i]       ,						timesteps[i])
															SCREAMING_SNAKE_CASE_:			List[str]  		=	model(lowerCAmelCase__       ,						timesteps[i]).sample
															SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.nn.functional.mse_loss(lowerCAmelCase__       ,						noise[i])
															loss.backward()
															optimizer.step()
									del model, optimizer

									# recreate the model and optimizer, and retry with DDIM
									SCREAMING_SNAKE_CASE_:			str  		=	self.get_model_optimizer(resolution=32)
									model.train().to(lowerCAmelCase__)
									for i in range(4):
															optimizer.zero_grad()
															SCREAMING_SNAKE_CASE_:			int  		=	ddim_scheduler.add_noise(clean_images[i]       ,						noise[i]       ,						timesteps[i])
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	model(lowerCAmelCase__       ,						timesteps[i]).sample
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.nn.functional.mse_loss(lowerCAmelCase__       ,						noise[i])
															loss.backward()
															optimizer.step()
									del model, optimizer

									self.assertTrue(torch.allclose(lowerCAmelCase__       ,						lowerCAmelCase__       ,						atol=1E-5))
									self.assertTrue(torch.allclose(lowerCAmelCase__       ,						lowerCAmelCase__       ,						atol=1E-5))









import doctest
from collections import deque

import numpy as np



class   __lowercase    :




			"""simple docstring"""







			def __init__(			self  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			int  		=	[2, 1, 2, -1]
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[1, 2, 3, 4]




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Any  		=	len(self.first_signal)
									SCREAMING_SNAKE_CASE_:			Dict  		=	len(self.second_signal)

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	max(lowerCAmelCase__       ,						lowerCAmelCase__)

									# create a zero matrix of max_length x max_length
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	[[0] * max_length for i in range(lowerCAmelCase__)]

									# fills the smaller signal with zeros to make both signals of same length
									if length_first_signal < length_second_signal:
															self.first_signal += [0] * (max_length - length_first_signal)
									elif length_first_signal > length_second_signal:
															self.second_signal += [0] * (max_length - length_second_signal)
									for i in range(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	deque(self.second_signal)
															rotated_signal.rotate(lowerCAmelCase__)
															for j, item in enumerate(lowerCAmelCase__):
																					matrix[i][j] += item

        # multiply the matrix with the first signal
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	np.matmul(np.transpose(lowerCAmelCase__)       ,						np.transpose(self.first_signal))

									# rounding-off to two decimal places
									return [round(lowerCAmelCase__       ,						2) for i in final_signal]


if __name__ == "__main__":
						doctest.testmod()






