


"""simple docstring"""







from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCAmelCase__      				=   transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def 					a__			(				SCREAMING_SNAKE_CASE    :    Dict				):


  '''simple docstring'''







  if isinstance(_snake_case				,      torch.Tensor				):
    return image
  elif isinstance(_snake_case				,      PIL.Image.Image				):
    lowerCAmelCase     :     Union[str, Any]								=							[image]

  lowerCAmelCase     :     str								=							[trans(img.convert("RGB"				)				) for img in image]
  lowerCAmelCase     :     str								=							torch.stack(_snake_case				)
  return image





class       SCREAMING_SNAKE_CASE__				(		lowerCAmelCase_				):
       """simple docstring"""


       def __init__(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         super().__init__()

         # make sure scheduler can always be converted to DDIM
         lowerCAmelCase     :     int								=							DDIMScheduler.from_config(scheduler.config						)

         self.register_modules(unet=__snake_case		,     scheduler=__snake_case						)

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         if strength < 0 or strength > 1:
           raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}"""						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							min(int(num_inference_steps * strength						)		,     __snake_case						)

         lowerCAmelCase     :     int								=							max(num_inference_steps - init_timestep		,     0						)
         lowerCAmelCase     :     List[Any]								=							self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__=None						):






         """simple docstring"""





         if not isinstance(__snake_case		,     (torch.Tensor, PIL.Image.Image, list)						):
           raise ValueError(
               f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__snake_case						)}"""						)

         lowerCAmelCase     :     Dict								=							image.to(device=__snake_case		,     dtype=__snake_case						)

         if isinstance(__snake_case		,     __snake_case						) and len(__snake_case						) != batch_size:
           raise ValueError(
               f"""You have passed a list of generators of length {len(__snake_case						)}, but requested an effective batch"""
               f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""						)

         lowerCAmelCase     :     Any								=							init_latents.shape
         lowerCAmelCase     :     Optional[Any]								=							randn_tensor(__snake_case		,     generator=__snake_case		,     device=__snake_case		,     dtype=__snake_case						)

         # get latents
         print("add noise to latents at timestep"		,     __snake_case						)
         lowerCAmelCase     :     List[Any]								=							self.scheduler.add_noise(__snake_case		,     __snake_case		,     __snake_case						)
         lowerCAmelCase     :     Tuple								=							init_latents

         return latents







       @torch.no_grad()
       def __call__(						self		,     snake_case__ = None		,     snake_case__ = 0.8		,     snake_case__ = 1		,     snake_case__ = None		,     snake_case__ = 0.0		,     snake_case__ = 50		,     snake_case__ = None		,     snake_case__ = "pil"		,     snake_case__ = True		,     ):






         """simple docstring"""





         self.check_inputs(__snake_case						)

         # 2. Preprocess image
         lowerCAmelCase     :     Tuple								=							preprocess(__snake_case						)

         # 3. set timesteps
         self.scheduler.set_timesteps(__snake_case		,     device=self.device						)
         lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							self.get_timesteps(__snake_case		,     __snake_case		,     self.device						)
         lowerCAmelCase     :     Optional[Any]								=							timesteps[:1].repeat(__snake_case						)

         # 4. Prepare latent variables
         lowerCAmelCase     :     List[str]								=							self.prepare_latents(__snake_case		,     __snake_case		,     __snake_case		,     self.unet.dtype		,     self.device		,     __snake_case						)
         lowerCAmelCase     :     List[Any]								=							latents

         # 5. Denoising loop
         for t in self.progress_bar(__snake_case						):
           # 1. predict noise model_output
           lowerCAmelCase     :     Tuple								=							self.unet(__snake_case		,     __snake_case						).sample

           # 2. predict previous mean of image x_t-1 and add variance depending on eta
           # eta corresponds to η in paper and should be between [0, 1]
           # do x_t -> x_t-1
           lowerCAmelCase     :     Optional[int]								=							self.scheduler.step(
               __snake_case		,     __snake_case		,     __snake_case		,     eta=__snake_case		,     use_clipped_model_output=__snake_case		,     generator=__snake_case		,     ).prev_sample

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

         if not return_dict:
           return (image, latent_timestep.item())

         return ImagePipelineOutput(images=__snake_case						)








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






def        UpperCamelCase_(							_snake_case   :  Union[List, PIL.Image.Image, torch.Tensor]	):




			"""simple docstring"""






			warnings.warn(
			    'The preprocess method is deprecated and will be removed in a future version. Please'
			    ' use VaeImageProcessor.preprocess instead'       ,     _snake_case       ,     )
			if isinstance(_snake_case       ,     torch.Tensor	):
						return image
			elif isinstance(_snake_case       ,     PIL.Image.Image	):
						__a        =[image]

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

						__a        =[np.array(i.resize((w, h)       ,     resample=PIL_INTERPOLATION['lanczos']	)	)[None, :] for i in image]
						__a        =np.concatenate(_snake_case       ,     axis=0	)
						__a        =np.array(_snake_case	).astype(np.floataa	) / 255.0
						__a        =image.transpose(0       ,     3       ,     1       ,     2	)
						__a        =2.0 * image - 1.0
						__a        =torch.from_numpy(_snake_case	)
			elif isinstance(image[0]       ,     torch.Tensor	):
						__a        =torch.cat(_snake_case       ,     dim=0	)
			return image






def        UpperCamelCase_(							_snake_case   :  Union[List, PIL.Image.Image, torch.Tensor]	):




			"""simple docstring"""






			if isinstance(_snake_case       ,     torch.Tensor	):
						return mask
			elif isinstance(_snake_case       ,     PIL.Image.Image	):
						__a        =[mask]

			if isinstance(mask[0]       ,     PIL.Image.Image	):
						__a      ,    __a        =mask[0].size
						__a      ,    __a        =(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						__a        =[np.array(m.convert('L'	).resize((w, h)       ,     resample=PIL_INTERPOLATION['nearest']	)	)[None, :] for m in mask]
						__a        =np.concatenate(_snake_case       ,     axis=0	)
						__a        =mask.astype(np.floataa	) / 255.0
						__a        =0
						__a        =1
						__a        =torch.from_numpy(_snake_case	)
			elif isinstance(mask[0]       ,     torch.Tensor	):
						__a        =torch.cat(_snake_case       ,     dim=0	)
			return mask






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						42
			SCREAMING_SNAKE_CASE							   =						42





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> Union[str, Any]:




						'''simple docstring'''
						super().__init__()
						self.register_modules(unet=__snake_case					,  scheduler=__snake_case					)





			@torch.no_grad()
			def __call__(		self					,  __snake_case					,  __snake_case					,  __snake_case = 250					,  __snake_case = 0.0					,  __snake_case = 10					,  __snake_case = 10					,  __snake_case = None					,  __snake_case = "pil"					,  __snake_case = True					,  )   -> Union[ImagePipelineOutput, Tuple]:




						'''simple docstring'''
						__a        =image

						__a        =_preprocess_image(__snake_case					)
						__a        =original_image.to(device=self.device					,  dtype=self.unet.dtype					)
						__a        =_preprocess_mask(__snake_case					)
						__a        =mask_image.to(device=self.device					,  dtype=self.unet.dtype					)

						__a        =original_image.shape[0]

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

						__a        =original_image.shape
						__a        =randn_tensor(__snake_case					,  generator=__snake_case					,  device=self.device					,  dtype=self.unet.dtype					)

						# set step values
						self.scheduler.set_timesteps(__snake_case					,  __snake_case					,  __snake_case					,  self.device					)
						__a        =eta

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

									else:
												# compute the reverse: x_t-1 -> x_t
												__a        =self.scheduler.undo_step(__snake_case					,  __snake_case					,  __snake_case					)
									__a        =t

						__a        =(image / 2 + 0.5).clamp(0					,  1					)
						__a        =image.cpu().permute(0					,  2					,  3					,  1					).numpy()
						if output_type == "pil":
									__a        =self.numpy_to_pil(__snake_case					)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__snake_case					)






