


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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


SCREAMING_SNAKE_CASE       :      Union[str, Any]												= logging.get_logger(__name__)  # pylint: disable=invalid-name



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						Any:
    warnings.warn(
        'The preprocess method is deprecated and will be removed in a future version. Please'
        ' use VaeImageProcessor.preprocess instead'							, lowerCamelCase_							, )
    if isinstance(lowerCamelCase_							, torch.Tensor ):
        return image
    elif isinstance(lowerCamelCase_							, PIL.Image.Image ):
        _lowercase       :							int      		=		[image]

    if isinstance(image[0]							, PIL.Image.Image ):
        _lowercase		,				_lowercase       :							Dict      		=		image[0].size
        _lowercase		,				_lowercase       :							List[Any]      		=		(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

        _lowercase       :							Union[str, Any]      		=		[np.array(i.resize((w, h)							, resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
        _lowercase       :							Optional[Any]      		=		np.concatenate(lowerCamelCase_							, axis=0 )
        _lowercase       :							Optional[int]      		=		np.array(lowerCamelCase_ ).astype(np.floataa ) / 2_55.0
        _lowercase       :							List[Any]      		=		image.transpose(0							, 3							, 1							, 2 )
        _lowercase       :							str      		=		2.0 * image - 1.0
        _lowercase       :							Dict      		=		torch.from_numpy(lowerCamelCase_ )
    elif isinstance(image[0]							, torch.Tensor ):
        _lowercase       :							List[Any]      		=		torch.cat(lowerCamelCase_							, dim=0 )
    return image



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						str:
    if isinstance(lowerCamelCase_							, torch.Tensor ):
        return mask
    elif isinstance(lowerCamelCase_							, PIL.Image.Image ):
        _lowercase       :							Dict      		=		[mask]

    if isinstance(mask[0]							, PIL.Image.Image ):
        _lowercase		,				_lowercase       :							Dict      		=		mask[0].size
        _lowercase		,				_lowercase       :							Union[str, Any]      		=		(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
        _lowercase       :							List[str]      		=		[np.array(m.convert('L' ).resize((w, h)							, resample=PIL_INTERPOLATION['nearest'] ) )[None, :] for m in mask]
        _lowercase       :							Any      		=		np.concatenate(lowerCamelCase_							, axis=0 )
        _lowercase       :							Any      		=		mask.astype(np.floataa ) / 2_55.0
        _lowercase       :							List[str]      		=		0
        _lowercase       :							Dict      		=		1
        _lowercase       :							int      		=		torch.from_numpy(lowerCamelCase_ )
    elif isinstance(mask[0]							, torch.Tensor ):
        _lowercase       :							Tuple      		=		torch.cat(lowerCamelCase_							, dim=0 )
    return mask






class _lowerCamelCase(						_a   ):
       lowercase_ :	UNetaDModel
       lowercase_ :	RePaintScheduler
       def __init__(					self,      lowerCamelCase,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           super().__init__()
           self.register_modules(unet=lowerCamelCase,      scheduler=lowerCamelCase)




       @torch.no_grad()
       def __call__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = 2_50,      lowerCamelCase = 0.0,      lowerCamelCase = 10,      lowerCamelCase = 10,      lowerCamelCase = None,      lowerCamelCase = "pil",      lowerCamelCase = True,      )       ->   Union[ImagePipelineOutput, Tuple]:




           """simple docstring"""

           _lowercase       :							Tuple      		=		image

           _lowercase       :							List[str]      		=		_preprocess_image(lowerCamelCase)
           _lowercase       :							List[Any]      		=		original_image.to(device=self.device,      dtype=self.unet.dtype)
           _lowercase       :							int      		=		_preprocess_mask(lowerCamelCase)
           _lowercase       :							Dict      		=		mask_image.to(device=self.device,      dtype=self.unet.dtype)

           _lowercase       :							Optional[int]      		=		original_image.shape[0]

           # sample gaussian noise to begin the loop
           if isinstance(lowerCamelCase,      lowerCamelCase) and len(lowerCamelCase) != batch_size:
               raise ValueError(
                   F'''You have passed a list of generators of length {len(lowerCamelCase)}, but requested an effective batch'''
                   F''' size of {batch_size}. Make sure the batch size matches the length of the generators.''')

           _lowercase       :							int      		=		original_image.shape
           _lowercase       :							Dict      		=		randn_tensor(lowerCamelCase,      generator=lowerCamelCase,      device=self.device,      dtype=self.unet.dtype)

           # set step values
           self.scheduler.set_timesteps(lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      self.device)
           _lowercase       :							Optional[Any]      		=		eta

           _lowercase       :							Dict      		=		self.scheduler.timesteps[0] + 1
           _lowercase       :							Optional[Any]      		=		generator[0] if isinstance(lowerCamelCase,      lowerCamelCase) else generator
           for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
               if t < t_last:
                   # predict the noise residual
                   _lowercase       :							int      		=		self.unet(lowerCamelCase,      lowerCamelCase).sample
                   # compute previous image: x_t -> x_t-1
                   _lowercase       :							Optional[Any]      		=		self.scheduler.step(lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase).prev_sample

               else:
                   # compute the reverse: x_t-1 -> x_t
                   _lowercase       :							int      		=		self.scheduler.undo_step(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
               _lowercase       :							List[Any]      		=		t

           _lowercase       :							Dict      		=		(image / 2 + 0.5).clamp(0,      1)
           _lowercase       :							Optional[Any]      		=		image.cpu().permute(0,      2,      3,      1).numpy()
           if output_type == "pil":
               _lowercase       :							Tuple      		=		self.numpy_to_pil(lowerCamelCase)

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=lowerCamelCase)








# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class 						lowercase							(   UpperCamelCase__,UpperCamelCase__		):
     _a							 =						1

     @register_to_config
     def __init__(  self		,     _a=2000		,     _a=0.1		,     _a=20		,     _a=1e-3				)						->							List[Any]:
           _A							:     Dict			=						None
           _A							:     List[Any]			=						None
           _A							:     Dict			=						None

     def 					a__	(  self		,     _a		,     _a = None				)						->							Union[str, Any]:
           _A							:     Union[str, Any]			=						torch.linspace(1		,     self.config.sampling_eps		,     _a		,     device=_a				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a=None				)						->							Dict:
           if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""				)

           # TODO(Patrick) better comments + non-PyTorch
           # postprocess model score
           _A							:     Any			=						(
               -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
           )
           _A							:     List[Any]			=						torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff				)				)
           _A							:     List[str]			=						std.flatten()
           while len(std.shape				) < len(score.shape				):
                 _A							:     List[Any]			=						std.unsqueeze(-1				)
           _A							:     int			=						-score / std

           # compute
           _A							:     Tuple			=						-1.0 / len(self.timesteps				)

           _A							:     str			=						self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
           _A							:     List[str]			=						beta_t.flatten()
           while len(beta_t.shape				) < len(x.shape				):
                 _A							:     Union[str, Any]			=						beta_t.unsqueeze(-1				)
           _A							:     Tuple			=						-0.5 * beta_t * x

           _A							:     Tuple			=						torch.sqrt(_a				)
           _A							:     Dict			=						drift - diffusion**2 * score
           _A							:     Dict			=						x + drift * dt

           # add noise
           _A							:     Any			=						randn_tensor(x.shape		,     layout=x.layout		,     generator=_a		,     device=x.device		,     dtype=x.dtype				)
           _A							:     str			=						x_mean + diffusion * math.sqrt(-dt				) * noise

           return x, x_mean



     def __len__(  self				)						->							Optional[Any]:
           return self.config.num_train_timesteps

