


'''simple docstring'''


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


lowerCamelCase__    	=     logging.get_logger(__name__)  # pylint: disable=invalid-name






def  __lowerCAmelCase (__lowerCAmelCase   ):
		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   ):
				_UpperCAmelCase		:       Union[str, Any]								=      [image]

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

				_UpperCAmelCase		:       int								=      [np.array(i.resize((w, h)      , resample=PIL_INTERPOLATION["lanczos"]   )   )[None, :] for i in image]
				_UpperCAmelCase		:       List[Any]								=      np.concatenate(__lowerCAmelCase      , axis=0   )
				_UpperCAmelCase		:       str								=      np.array(__lowerCAmelCase   ).astype(np.floataa   ) / 2_5_5.0
				_UpperCAmelCase		:       Optional[Any]								=      image.transpose(0      , 3      , 1      , 2   )
				_UpperCAmelCase		:       Optional[int]								=      2.0 * image - 1.0
				_UpperCAmelCase		:       Union[str, Any]								=      torch.from_numpy(__lowerCAmelCase   )
		elif isinstance(image[0]      , torch.Tensor   ):
				_UpperCAmelCase		:       Union[str, Any]								=      torch.cat(__lowerCAmelCase      , dim=0   )
		return image






def  __lowerCAmelCase (__lowerCAmelCase   ):
		if isinstance(__lowerCAmelCase      , torch.Tensor   ):
				return mask
		elif isinstance(__lowerCAmelCase      , PIL.Image.Image   ):
				_UpperCAmelCase		:       Optional[int]								=      [mask]

		if isinstance(mask[0]      , PIL.Image.Image   ):
				_UpperCAmelCase					,    _UpperCAmelCase		:       str								=      mask[0].size
				_UpperCAmelCase					,    _UpperCAmelCase		:       str								=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				_UpperCAmelCase		:       Tuple								=      [np.array(m.convert("L"   ).resize((w, h)      , resample=PIL_INTERPOLATION["nearest"]   )   )[None, :] for m in mask]
				_UpperCAmelCase		:       int								=      np.concatenate(__lowerCAmelCase      , axis=0   )
				_UpperCAmelCase		:       Optional[int]								=      mask.astype(np.floataa   ) / 2_5_5.0
				_UpperCAmelCase		:       Union[str, Any]								=      0
				_UpperCAmelCase		:       List[Any]								=      1
				_UpperCAmelCase		:       List[Any]								=      torch.from_numpy(__lowerCAmelCase   )
		elif isinstance(mask[0]      , torch.Tensor   ):
				_UpperCAmelCase		:       Tuple								=      torch.cat(__lowerCAmelCase      , dim=0   )
		return mask






class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	UNetaDModel
			lowerCAmelCase :	RePaintScheduler




			def __init__(		self  :     List[str]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     List[Any] )   ->Optional[int]:



					'''simple docstring'''







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






			@torch.no_grad()
			def __call__(		self  :     Union[str, Any]	,  lowerCamelCase__  :     Union[torch.Tensor, PIL.Image.Image]	,  lowerCamelCase__  :     Union[torch.Tensor, PIL.Image.Image]	,  lowerCamelCase__  :     int = 2_50	,  lowerCamelCase__  :     float = 0.0	,  lowerCamelCase__  :     int = 10	,  lowerCamelCase__  :     int = 10	,  lowerCamelCase__  :     Optional[Union[torch.Generator, List[torch.Generator]]] = None	,  lowerCamelCase__  :     Optional[str] = "pil"	,  lowerCamelCase__  :     bool = True	,  )   ->Union[ImagePipelineOutput, Tuple]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      image

					_UpperCAmelCase		:       Tuple								=      _preprocess_image(lowerCamelCase__ )
					_UpperCAmelCase		:       List[Any]								=      original_image.to(device=self.device	,  dtype=self.unet.dtype )
					_UpperCAmelCase		:       int								=      _preprocess_mask(lowerCamelCase__ )
					_UpperCAmelCase		:       List[Any]								=      mask_image.to(device=self.device	,  dtype=self.unet.dtype )

					_UpperCAmelCase		:       Union[str, Any]								=      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.""" )

					_UpperCAmelCase		:       int								=      original_image.shape
					_UpperCAmelCase		:       List[str]								=      randn_tensor(lowerCamelCase__	,  generator=lowerCamelCase__	,  device=self.device	,  dtype=self.unet.dtype )

					# set step values
					self.scheduler.set_timesteps(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  self.device )
					_UpperCAmelCase		:       str								=      eta

					_UpperCAmelCase		:       Optional[int]								=      self.scheduler.timesteps[0] + 1
					_UpperCAmelCase		:       Optional[int]								=      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
									_UpperCAmelCase		:       Any								=      self.unet(lowerCamelCase__	,  lowerCamelCase__ ).sample
									# compute previous image: x_t -> x_t-1
									_UpperCAmelCase		:       Optional[int]								=      self.scheduler.step(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ ).prev_sample

							else:
									# compute the reverse: x_t-1 -> x_t
									_UpperCAmelCase		:       Union[str, Any]								=      self.scheduler.undo_step(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      t

					_UpperCAmelCase		:       Optional[Any]								=      (image / 2 + 0.5).clamp(0	,  1 )
					_UpperCAmelCase		:       List[Any]								=      image.cpu().permute(0	,  2	,  3	,  1 ).numpy()
					if output_type == "pil":
							_UpperCAmelCase		:       Union[str, Any]								=      self.numpy_to_pil(lowerCamelCase__ )

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=lowerCamelCase__ )







'''simple docstring'''


from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase = None   ):
		if version.parse(hfh.__version__   ).release < version.parse("0.11.0"   ).release:
				# old versions of hfh don't url-encode the file path
				_UpperCAmelCase		:       str								=      quote(__lowerCAmelCase   )
		return hfh.hf_hub_url(__lowerCAmelCase      , __lowerCAmelCase      , repo_type="dataset"      , revision=__lowerCAmelCase   )



