

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       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_				,   UpperCAmelCase_       				=			image.size
	UpperCAmelCase_				,   UpperCAmelCase_       				=			(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
	UpperCAmelCase_       				=			image.resize((w, h)       ,     resample=PIL_INTERPOLATION["lanczos"]    )
	UpperCAmelCase_       				=			np.array(A    ).astype(np.floataa    ) / 255.0
	UpperCAmelCase_       				=			image[None].transpose(0       ,     3       ,     1       ,     2    )
	UpperCAmelCase_       				=			torch.from_numpy(A    )
	return 2.0 * image - 1.0




class 						__UpperCamelCase    (					lowercase						):




	def __init__(		self	:						Any		,	lowerCAmelCase	:						VQModel		,	lowerCAmelCase	:						UNetaDModel		,	lowerCAmelCase	:						Union[
	        DDIMScheduler,
	        PNDMScheduler,
	        LMSDiscreteScheduler,
	        EulerDiscreteScheduler,
	        EulerAncestralDiscreteScheduler,
	        DPMSolverMultistepScheduler,
	    ]		,	):


		'''simple docstring'''







		super().__init__()
		self.register_modules(vqvae=lowerCAmelCase		,	unet=lowerCAmelCase		,	scheduler=lowerCAmelCase							)




	@torch.no_grad()
	def __call__(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Union[torch.Tensor, PIL.Image.Image] = None		,	lowerCAmelCase	:						Optional[int] = 1		,	lowerCAmelCase	:						Optional[int] = 100		,	lowerCAmelCase	:						Optional[float] = 0.0		,	lowerCAmelCase	:						Optional[Union[torch.Generator, List[torch.Generator]]] = None		,	lowerCAmelCase	:						Optional[str] = "pil"		,	lowerCAmelCase	:						bool = True		,	):


		'''simple docstring'''







		if isinstance(lowerCAmelCase		,	PIL.Image.Image							):
			UpperCAmelCase_       				=			1
		elif isinstance(lowerCAmelCase		,	torch.Tensor							):
			UpperCAmelCase_       				=			image.shape[0]
		else:
			raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCAmelCase							)}"							)

		if isinstance(lowerCAmelCase		,	PIL.Image.Image							):
			UpperCAmelCase_       				=			preprocess(lowerCAmelCase							)

		UpperCAmelCase_				,   UpperCAmelCase_       				=			image.shape[-2:]

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

		UpperCAmelCase_       				=			randn_tensor(lowerCAmelCase		,	generator=lowerCAmelCase		,	device=self.device		,	dtype=lowerCAmelCase							)

		UpperCAmelCase_       				=			image.to(device=self.device		,	dtype=lowerCAmelCase							)

		# set timesteps and move to the correct device
		self.scheduler.set_timesteps(lowerCAmelCase		,	device=self.device							)
		UpperCAmelCase_       				=			self.scheduler.timesteps

		# scale the initial noise by the standard deviation required by the scheduler
		UpperCAmelCase_       				=			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]
		UpperCAmelCase_       				=			"eta" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
		UpperCAmelCase_       				=			{}
		if accepts_eta:
			UpperCAmelCase_       				=			eta

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

		# decode the image latents with the VQVAE
		UpperCAmelCase_       				=			self.vqvae.decode(lowerCAmelCase							).sample
		UpperCAmelCase_       				=			torch.clamp(lowerCAmelCase		,	-1.0		,	1.0							)
		UpperCAmelCase_       				=			image / 2 + 0.5
		UpperCAmelCase_       				=			image.cpu().permute(0		,	2		,	3		,	1							).numpy()

		if output_type == "pil":
			UpperCAmelCase_       				=			self.numpy_to_pil(lowerCAmelCase							)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=lowerCAmelCase							)


from .imports import is_tqdm_available


if is_tqdm_available():
				from tqdm.auto import tqdm as _tqdm

from ..state import PartialState



def       __lowerCAmelCase      (						A = True       ,     *A       ,     **A    ):
	if not is_tqdm_available():
		raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."    )
	UpperCAmelCase_       				=			False
	if main_process_only:
		UpperCAmelCase_       				=			PartialState().local_process_index == 0
	return _tqdm(*A       ,     **A       ,     disable=A    )