


'''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    lowerCamelCase__   ( A     :    Tuple  ):




						'''simple docstring'''






						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__(     lowerCAmelCase ):




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

										"""simple docstring"""




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





				@torch.no_grad()
				def __call__(	self    :				Tuple   ,					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   ,					)->  Union[Tuple, ImagePipelineOutput]:

										"""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   )









'''simple docstring'''



from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def    lowerCamelCase__   ( A     :    List[Any]  ):




						'''simple docstring'''






						return DownloadCommand(args.model   ,     args.cache_dir   ,     args.force   ,     args.trust_remote_code  )






class  UpperCamelCase__(     lowerCAmelCase ):




				@staticmethod
				def   a__(	lowerCAmelCase    :				ArgumentParser   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			parser.add_parser('''download'''   )
										download_parser.add_argument(
										    '''--cache-dir'''   ,					type=lowerCAmelCase   ,					default=lowerCAmelCase   ,					help='''Path to location to store the models'''   )
										download_parser.add_argument(
										    '''--force'''   ,					action='''store_true'''   ,					help='''Force the model to be download even if already in cache-dir'''   )
										download_parser.add_argument(
										    '''--trust-remote-code'''   ,					action='''store_true'''   ,					help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''   ,					)
										download_parser.add_argument('''model'''   ,					type=lowerCAmelCase   ,					help='''Name of the model to download'''   )
										download_parser.set_defaults(func=lowerCAmelCase   )




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				bool   ,					lowerCAmelCase    :				bool   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			model
										UpperCAmelCase         =			cache
										UpperCAmelCase         =			force
										UpperCAmelCase         =			trust_remote_code





				def   a__(	self    :				int   )->  Optional[Any]:

										"""simple docstring"""




										from ..models.auto import AutoModel, AutoTokenizer

										AutoModel.from_pretrained(
										    self._model   ,					cache_dir=self._cache   ,					force_download=self._force   ,					trust_remote_code=self._trust_remote_code   )
										AutoTokenizer.from_pretrained(
										    self._model   ,					cache_dir=self._cache   ,					force_download=self._force   ,					trust_remote_code=self._trust_remote_code   )





