


import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						image.size
		SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[Any]				     =						(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		SCREAMING_SNAKE_CASE_				:Any				     =						image.resize((w, h)     , resample=PIL_INTERPOLATION["lanczos"] )
		SCREAMING_SNAKE_CASE_				:Dict				     =						np.array(a ).astype(np.floataa ) / 255.0
		SCREAMING_SNAKE_CASE_				:Tuple				     =						image[None].transpose(0     , 3     , 1     , 2 )
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						torch.from_numpy(a )
		return 2.0 * image - 1.0



class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     Optional[int]   ,  UpperCAmelCase				:     VQModel   ,  UpperCAmelCase				:     UNetaDModel   ,  UpperCAmelCase				:     Union[
		        DDIMScheduler,
		        PNDMScheduler,
		        LMSDiscreteScheduler,
		        EulerDiscreteScheduler,
		        EulerAncestralDiscreteScheduler,
		        DPMSolverMultistepScheduler,
		    ]   ,  ):
				super().__init__()
				self.register_modules(vqvae=UpperCAmelCase   ,  unet=UpperCAmelCase   ,  scheduler=UpperCAmelCase)







		@torch.no_grad()
		def __call__(						self				:     Optional[Any]   ,  UpperCAmelCase				:     Union[torch.Tensor, PIL.Image.Image] = None   ,  UpperCAmelCase				:     Optional[int] = 1   ,  UpperCAmelCase				:     Optional[int] = 1_00   ,  UpperCAmelCase				:     Optional[float] = 0.0   ,  UpperCAmelCase				:     Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,  UpperCAmelCase				:     Optional[str] = "pil"   ,  UpperCAmelCase				:     bool = True   ,  ):
				if isinstance(UpperCAmelCase   ,  PIL.Image.Image):
						SCREAMING_SNAKE_CASE_				:int				     =						1
				elif isinstance(UpperCAmelCase   ,  torch.Tensor):
						SCREAMING_SNAKE_CASE_				:Dict				     =						image.shape[0]
				else:
						raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCAmelCase)}")

				if isinstance(UpperCAmelCase   ,  PIL.Image.Image):
						SCREAMING_SNAKE_CASE_				:str				     =						preprocess(UpperCAmelCase)

				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[Any]				     =						image.shape[-2:]

				# in_channels should be 6: 3 for latents, 3 for low resolution image
				SCREAMING_SNAKE_CASE_				:int				     =						(batch_size, self.unet.config.in_channels // 2, height, width)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						next(self.unet.parameters()).dtype

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						randn_tensor(UpperCAmelCase   ,  generator=UpperCAmelCase   ,  device=self.device   ,  dtype=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Any				     =						image.to(device=self.device   ,  dtype=UpperCAmelCase)

				# set timesteps and move to the correct device
				self.scheduler.set_timesteps(UpperCAmelCase   ,  device=self.device)
				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.scheduler.timesteps

				# scale the initial noise by the standard deviation required by the scheduler
				SCREAMING_SNAKE_CASE_				:str				     =						latents * self.scheduler.init_noise_sigma

				# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
				# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
				# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
				# and should be between [0, 1]
				SCREAMING_SNAKE_CASE_				:Tuple				     =						"eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						{}
				if accepts_eta:
						SCREAMING_SNAKE_CASE_				:Dict				     =						eta

				for t in self.progress_bar(UpperCAmelCase):
						# concat latents and low resolution image in the channel dimension.
						SCREAMING_SNAKE_CASE_				:str				     =						torch.cat([latents, image]   ,  dim=1)
						SCREAMING_SNAKE_CASE_				:str				     =						self.scheduler.scale_model_input(UpperCAmelCase   ,  UpperCAmelCase)
						# predict the noise residual
						SCREAMING_SNAKE_CASE_				:Any				     =						self.unet(UpperCAmelCase   ,  UpperCAmelCase).sample
						# compute the previous noisy sample x_t -> x_t-1
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.scheduler.step(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  **UpperCAmelCase).prev_sample

				# decode the image latents with the VQVAE
				SCREAMING_SNAKE_CASE_				:Any				     =						self.vqvae.decode(UpperCAmelCase).sample
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						torch.clamp(UpperCAmelCase   ,  -1.0   ,  1.0)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						image / 2 + 0.5
				SCREAMING_SNAKE_CASE_				:Any				     =						image.cpu().permute(0   ,  2   ,  3   ,  1).numpy()

				if output_type == "pil":
						SCREAMING_SNAKE_CASE_				:List[str]				     =						self.numpy_to_pil(UpperCAmelCase)

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=UpperCAmelCase)










import unittest

import torch

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


SCREAMING_SNAKE_CASE__     					=			False



class 						_UpperCAmelCase      (     unittest.TestCase							):






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







		@slow
		def _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				SCREAMING_SNAKE_CASE_				:Any				     =						DDPMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  beta_end=0.02   ,  beta_schedule="linear"   ,  clip_sample=UpperCAmelCase   ,  )
				SCREAMING_SNAKE_CASE_				:str				     =						DDIMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  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)
				SCREAMING_SNAKE_CASE_				:Any				     =						[torch.randn((4, 3, 32, 32)).clip(-1   ,  1).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:str				     =						[torch.randn((4, 3, 32, 32)).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						[torch.randint(0   ,  10_00   ,  (4,)).long().to(UpperCAmelCase) for _ in range(4)]

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

				# recreate the model and optimizer, and retry with DDIM
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.get_model_optimizer(resolution=32)
				model.train().to(UpperCAmelCase)
				for i in range(4):
						optimizer.zero_grad()
						SCREAMING_SNAKE_CASE_				:List[str]				     =						ddim_scheduler.add_noise(clean_images[i]   ,  noise[i]   ,  timesteps[i])
						SCREAMING_SNAKE_CASE_				:Tuple				     =						model(UpperCAmelCase   ,  timesteps[i]).sample
						SCREAMING_SNAKE_CASE_				:Optional[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))






