




"""simple docstring"""




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






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a		, ):





							"""simple docstring"""
							super().__init__()
							self.register_modules(vqvae=_a		, unet=_a		, scheduler=_a )





				@torch.no_grad()
				def __call__(       self		, _a = None		, _a = 1		, _a = 100		, _a = 0.0		, _a = None		, _a = "pil"		, _a = True		, ):





							"""simple docstring"""
							if isinstance(_a		, PIL.Image.Image ):
										lowerCamelCase			      =					1
							elif isinstance(_a		, torch.Tensor ):
										lowerCamelCase			      =					image.shape[0]
							else:
										raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_a )}' )

							if isinstance(_a		, PIL.Image.Image ):
										lowerCamelCase			      =					preprocess(_a )

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

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

							lowerCamelCase			      =					randn_tensor(_a		, generator=_a		, device=self.device		, dtype=_a )

							lowerCamelCase			      =					image.to(device=self.device		, dtype=_a )

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

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

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

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

							if output_type == "pil":
										lowerCamelCase			      =					self.numpy_to_pil(_a )

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=_a )









"""simple docstring"""




import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def     a__ (				)						->					Union[str, Any]:
			lowerCamelCase			      =					ArgumentParser(
			    description=(
			        """PyTorch TPU distributed training launch """
			        """helper utility that will spawn up """
			        """multiple distributed processes"""
			    )				)

			# Optional arguments for the launch helper
			parser.add_argument("""--num_cores"""       , type=snake_case__       , default=1       , help="""Number of TPU cores to use (1 or 8)."""				)

			# positional
			parser.add_argument(
			    """training_script"""       , type=snake_case__       , help=(
			        """The full path to the single TPU training """
			        """program/script to be launched in parallel, """
			        """followed by all the arguments for the """
			        """training script"""
			    )       , )

			# rest from the training program
			parser.add_argument("""training_script_args"""       , nargs=snake_case__				)

			return parser.parse_args()



def     a__ (				)						->					List[str]:
			lowerCamelCase			      =					parse_args()

			# Import training_script as a module.
			lowerCamelCase			      =					Path(args.training_script				)
			sys.path.append(str(script_fpath.parent.resolve()				)				)
			lowerCamelCase			      =					script_fpath.stem
			lowerCamelCase			      =					importlib.import_module(snake_case__				)

			# Patch sys.argv
			lowerCamelCase			      =					[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores				)]
			xmp.spawn(mod._mp_fn       , args=()       , nprocs=args.num_cores				)


if __name__ == "__main__":
					main()



