






'''simple docstring'''







# 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 _lowercase			(       _lowercase       ,   _lowercase	):
							a          = 1




							@register_to_config
							def __init__(							self:       List[Any]    ,							UpperCamelCase__:       str=2_000    ,							UpperCamelCase__:       Dict=0.1    ,							UpperCamelCase__:       Dict=20    ,							UpperCamelCase__:       str=1e-3  ):
														lowerCamelCase__      :			Dict 						= None
														lowerCamelCase__      :			Optional[Any] 						= None
														lowerCamelCase__      :			Optional[int] 						= None




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Union[str, torch.device] = None  ):
														lowerCamelCase__      :			Any 						= torch.linspace(1    ,							self.config.sampling_eps    ,							UpperCamelCase__    ,							device=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       int=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__      :			List[Any] 						= (
														    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
														)
														lowerCamelCase__      :			Union[str, Any] 						= torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff  )  )
														lowerCamelCase__      :			List[str] 						= std.flatten()
														while len(std.shape  ) < len(score.shape  ):
																					lowerCamelCase__      :			Optional[int] 						= std.unsqueeze(-1  )
														lowerCamelCase__      :			List[str] 						= -score / std

														# compute
														lowerCamelCase__      :			Any 						= -1.0 / len(self.timesteps  )

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

														lowerCamelCase__      :			Dict 						= torch.sqrt(UpperCamelCase__  )
														lowerCamelCase__      :			Union[str, Any] 						= drift - diffusion**2 * score
														lowerCamelCase__      :			Tuple 						= x + drift * dt

														# add noise
														lowerCamelCase__      :			Union[str, 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:       Optional[int]  ):
														return self.config.num_train_timesteps









'''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


_A			:    Union[str, Any]					  =False

class _lowercase			(       unittest.TestCase	):




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       str=32  ):
														set_seed(0  )
														lowerCamelCase__      :			Optional[int] 						= UNetaDModel(sample_size=UpperCamelCase__    ,							in_channels=3    ,							out_channels=3  )
														lowerCamelCase__      :			List[Any] 						= torch.optim.SGD(model.parameters()    ,							lr=0.0_001  )
														return model, optimizer




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Optional[Any] 						= """cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
														lowerCamelCase__      :			List[Any] 						= DDPMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Any 						= DDIMScheduler(
														    num_train_timesteps=1_000    ,							beta_start=0.0_001    ,							beta_end=0.02    ,							beta_schedule="""linear"""    ,							clip_sample=UpperCamelCase__    ,							)

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

														# shared batches for DDPM and DDIM
														set_seed(0  )
														lowerCamelCase__      :			str 						= [torch.randn((4, 3, 32, 32)  ).clip(-1    ,							1  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randn((4, 3, 32, 32)  ).to(UpperCamelCase__  ) for _ in range(4  )]
														lowerCamelCase__      :			Tuple 						= [torch.randint(0    ,							1_000    ,							(4,)  ).long().to(UpperCamelCase__  ) for _ in range(4  )]

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

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

														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )
														self.assertTrue(torch.allclose(UpperCamelCase__    ,							UpperCamelCase__    ,							atol=1e-5  )  )

