






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      =				False





class 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							def       __A (  self:   str		,     __A:   Optional[Any]=32   )    ->				int:
												set_seed(0   )
												_A								= UNetaDModel(sample_size=__A		,     in_channels=3		,     out_channels=3   )
												_A								= torch.optim.SGD(model.parameters()		,     lr=0.0_001   )
												return model, optimizer



							@slow
							def       __A (  self:   Union[str, Any]   )    ->				Tuple:
												_A								= '''cpu'''  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
												_A								= DDPMScheduler(
												    num_train_timesteps=10_00		,     beta_start=0.0_001		,     beta_end=0.02		,     beta_schedule='''linear'''		,     clip_sample=__A		,     )
												_A								= DDIMScheduler(
												    num_train_timesteps=10_00		,     beta_start=0.0_001		,     beta_end=0.02		,     beta_schedule='''linear'''		,     clip_sample=__A		,     )

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

												# shared batches for DDPM and DDIM
												set_seed(0   )
												_A								= [torch.randn((4, 3, 32, 32)   ).clip(-1		,     1   ).to(__A   ) for _ in range(4   )]
												_A								= [torch.randn((4, 3, 32, 32)   ).to(__A   ) for _ in range(4   )]
												_A								= [torch.randint(0		,     10_00		,     (4,)   ).long().to(__A   ) for _ in range(4   )]

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

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

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











import flax.linen as nn
import jax
import jax.numpy as jnp





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					jnp.floataa

							def       __A (  self:   Tuple   )    ->				Tuple:
												_A								= nn.Conv(
												    self.out_channels		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )



							def __call__(  self:   Dict		,     __A:   Dict   )    ->				Tuple:
												_A							,_A							,_A							,_A								= hidden_states.shape
												_A								= jax.image.resize(
												    __A		,     shape=(batch, height * 2, width * 2, channels)		,     method='''nearest'''		,     )
												_A								= self.conv(__A   )
												return hidden_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					jnp.floataa

							def       __A (  self:   List[str]   )    ->				Tuple:
												_A								= nn.Conv(
												    self.out_channels		,     kernel_size=(3, 3)		,     strides=(2, 2)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )



							def __call__(  self:   Union[str, Any]		,     __A:   List[Any]   )    ->				Union[str, Any]:
												# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
												# hidden_states = jnp.pad(hidden_states, pad_width=pad)
												_A								= self.conv(__A   )
												return hidden_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					None
							A_						  =					0.0
							A_						  =					None
							A_						  =					jnp.floataa

							def       __A (  self:   Dict   )    ->				Dict:
												_A								= self.in_channels if self.out_channels is None else self.out_channels

												_A								= nn.GroupNorm(num_groups=32		,     epsilon=1e-5   )
												_A								= nn.Conv(
												    __A		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )

												_A								= nn.Dense(__A		,     dtype=self.dtype   )

												_A								= nn.GroupNorm(num_groups=32		,     epsilon=1e-5   )
												_A								= nn.Dropout(self.dropout_prob   )
												_A								= nn.Conv(
												    __A		,     kernel_size=(3, 3)		,     strides=(1, 1)		,     padding=((1, 1), (1, 1))		,     dtype=self.dtype		,     )

												_A								= self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

												_A								= None
												if use_nin_shortcut:
																	_A								= nn.Conv(
																	    __A		,     kernel_size=(1, 1)		,     strides=(1, 1)		,     padding='''VALID'''		,     dtype=self.dtype		,     )



							def __call__(  self:   Dict		,     __A:   List[Any]		,     __A:   List[Any]		,     __A:   Any=True   )    ->				List[Any]:
												_A								= hidden_states
												_A								= self.norma(__A   )
												_A								= nn.swish(__A   )
												_A								= self.conva(__A   )

												_A								= self.time_emb_proj(nn.swish(__A   )   )
												_A								= jnp.expand_dims(jnp.expand_dims(__A		,     1   )		,     1   )
												_A								= hidden_states + temb

												_A								= self.norma(__A   )
												_A								= nn.swish(__A   )
												_A								= self.dropout(__A		,     __A   )
												_A								= self.conva(__A   )

												if self.conv_shortcut is not None:
																	_A								= self.conv_shortcut(__A   )

												return hidden_states + residual



