


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)






def 				UpperCamelCase_(      lowerCamelCase_ )    ->						int:
    if not numbers:
        return 0

    if not isinstance(lowerCamelCase_							, (list, tuple) ) or not all(
        isinstance(lowerCamelCase_							, lowerCamelCase_ ) for number in numbers ):
        raise ValueError('numbers must be an iterable of integers' )

    _lowercase       :							int      		=		numbers[0]

    for i in range(1							, len(lowerCamelCase_ ) ):
        # update the maximum and minimum subarray products
        _lowercase       :							Union[str, Any]      		=		numbers[i]
        if number < 0:
            _lowercase		,				_lowercase       :							Any      		=		min_till_now, max_till_now
        _lowercase       :							Union[str, Any]      		=		max(lowerCamelCase_							, max_till_now * number )
        _lowercase       :							Union[str, Any]      		=		min(lowerCamelCase_							, min_till_now * number )

        # update the maximum product found till now
        _lowercase       :							Optional[Any]      		=		max(lowerCamelCase_							, lowerCamelCase_ )

    return max_prod


