




'''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 					UpperCamelCase_(     snake_case							:	Dict					):

       '''simple docstring'''






       snake_case_ ,				snake_case_      =				image.size
       snake_case_ ,				snake_case_      =				(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
       snake_case_      =				image.resize((w, h)							,    resample=PIL_INTERPOLATION["lanczos"]					)
       snake_case_      =				np.array(lowerCAmelCase__					).astype(np.floataa					) / 2_5_5.0
       snake_case_      =				image[None].transpose(0							,    3							,    1							,    2					)
       snake_case_      =				torch.from_numpy(lowerCAmelCase__					)
       return 2.0 * image - 1.0




class 			_snake_case		(     __SCREAMING_SNAKE_CASE							):




   def __init__( self    ,							a__    ,							a__    ,							a__    ,							)    ->    Any:

          '''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    ,							)    ->    List[str]:

          '''simple docstring'''




          if isinstance(_a    ,							PIL.Image.Image							):
                 snake_case_      =				1
          elif isinstance(_a    ,							torch.Tensor							):
                 snake_case_      =				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							):
                 snake_case_      =				preprocess(_a							)

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

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

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

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

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

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

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

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

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

          if not return_dict:
                 return (image,)

          return ImagePipelineOutput(images=_a							)










'''simple docstring'''





import logging
import os

from .state import PartialState




class 			_snake_case		(     logging.LoggerAdapter							):




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

										'''simple docstring'''




										snake_case_      =				PartialState()
										return not main_process_only or (main_process_only and state.is_main_process)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							*a__    ,							**a__							)    ->    List[Any]:

										'''simple docstring'''




										if PartialState._shared_state == {}:
																	raise RuntimeError(
																	    "You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."							)
										snake_case_      =				kwargs.pop("main_process_only"    ,							a__							)
										snake_case_      =				kwargs.pop("in_order"    ,							a__							)

										if self.isEnabledFor(a__							):
																	if self._should_log(a__							):
																								snake_case_ ,				snake_case_      =				self.process(a__    ,							a__							)
																								self.logger.log(a__    ,							a__    ,							*a__    ,							**a__							)

																	elif in_order:
																								snake_case_      =				PartialState()
																								for i in range(state.num_processes							):
																															if i == state.process_index:
																																						snake_case_ ,				snake_case_      =				self.process(a__    ,							a__							)
																																						self.logger.log(a__    ,							a__    ,							*a__    ,							**a__							)
																															state.wait_for_everyone()


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	str = None					):

							'''simple docstring'''






							if log_level is None:
														snake_case_      =				os.environ.get("ACCELERATE_LOG_LEVEL"							,    snake_case					)
							snake_case_      =				logging.getLogger(snake_case					)
							if log_level is not None:
														logger.setLevel(log_level.upper()					)
														logger.root.setLevel(log_level.upper()					)
							return MultiProcessAdapter(snake_case							,    {}					)




