
from queue import PriorityQueue
from typing import Any

import numpy as np




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    dict		,						_lowerCamelCase				:    str		,						_lowerCamelCase				:    set		,						_lowerCamelCase				:    set		,						_lowerCamelCase				:    dict		,						_lowerCamelCase				:    dict		,						_lowerCamelCase				:    PriorityQueue		,						_lowerCamelCase				:    dict		,						_lowerCamelCase				:    float | int		,						)      ->					float | int:






							'''simple docstring'''





							for nxt, d in graph[v]:
														if nxt in visited_forward:
																					continue
														__UpperCamelCase			:     Tuple         =      cst_fwd.get(_lowerCamelCase		,						np.inf)
														__UpperCamelCase			:     Dict         =      cst_fwd[v] + d
														if new_cost_f < old_cost_f:
																					queue.put((new_cost_f, nxt))
																					__UpperCamelCase			:     List[Any]         =      new_cost_f
																					__UpperCamelCase			:     Tuple         =      v
														if nxt in visited_backward:
																					if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																												__UpperCamelCase			:     int         =      cst_fwd[v] + d + cst_bwd[nxt]
							return shortest_distance




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    str		,						_lowerCamelCase				:    dict		,						_lowerCamelCase				:    dict)      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      -1

							__UpperCamelCase			:     Optional[Any]         =      set()
							__UpperCamelCase			:     Union[str, Any]         =      set()
							__UpperCamelCase			:     Any         =      {source: 0}
							__UpperCamelCase			:     int         =      {destination: 0}
							__UpperCamelCase			:     Optional[Any]         =      {source: None}
							__UpperCamelCase			:     Union[str, Any]         =      {destination: None}
							__UpperCamelCase			:     PriorityQueue[Any]         =      PriorityQueue()
							__UpperCamelCase			:     PriorityQueue[Any]         =      PriorityQueue()

							__UpperCamelCase			:     str         =      np.inf

							queue_forward.put((0, source))
							queue_backward.put((0, destination))

							if source == destination:
														return 0

							while not queue_forward.empty() and not queue_backward.empty():
														__UpperCamelCase     ,		__UpperCamelCase			:     Any         =      queue_forward.get()
														visited_forward.add(_lowerCamelCase)

														__UpperCamelCase     ,		__UpperCamelCase			:     str         =      queue_backward.get()
														visited_backward.add(_lowerCamelCase)

														__UpperCamelCase			:     Tuple         =      pass_and_relaxation(
														    _lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						)

														__UpperCamelCase			:     Union[str, Any]         =      pass_and_relaxation(
														    _lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						)

														if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
																					break

							if shortest_distance != np.inf:
														__UpperCamelCase			:     Any         =      shortest_distance
							return shortest_path_distance


lowercase						:      Union[str, Any]									= {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
lowercase						:      List[Any]									= {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
		import doctest

		doctest.testmod()

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


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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Tuple:






							'''simple docstring'''





							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			:     int         =      [image]

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

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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Optional[int]:






							'''simple docstring'''





							if isinstance(_lowerCamelCase		,						torch.Tensor):
														return mask
							elif isinstance(_lowerCamelCase		,						PIL.Image.Image):
														__UpperCamelCase			:     Any         =      [mask]

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




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  42
	_A			=  42







	def __init__(      self	:Optional[Any]  ,     a	:str  ,     a	:List[str]		)	->				Union[str, Any]:
								super().__init__()
								self.register_modules(unet=a  ,     scheduler=a		)







	@torch.no_grad()
	def __call__(      self	:Union[str, Any]  ,     a	:Union[torch.Tensor, PIL.Image.Image]  ,     a	:Union[torch.Tensor, PIL.Image.Image]  ,     a	:int = 2_5_0  ,     a	:float = 0.0  ,     a	:int = 1_0  ,     a	:int = 1_0  ,     a	:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     a	:Optional[str] = "pil"  ,     a	:bool = True  ,     )	->				Union[ImagePipelineOutput, Tuple]:
								__UpperCamelCase			:     Tuple         =      image

								__UpperCamelCase			:     Optional[Any]         =      _preprocess_image(a		)
								__UpperCamelCase			:     Optional[int]         =      original_image.to(device=self.device  ,     dtype=self.unet.dtype		)
								__UpperCamelCase			:     Dict         =      _preprocess_mask(a		)
								__UpperCamelCase			:     Any         =      mask_image.to(device=self.device  ,     dtype=self.unet.dtype		)

								__UpperCamelCase			:     str         =      original_image.shape[0]

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

								__UpperCamelCase			:     List[str]         =      original_image.shape
								__UpperCamelCase			:     List[Any]         =      randn_tensor(a  ,     generator=a  ,     device=self.device  ,     dtype=self.unet.dtype		)

								# set step values
								self.scheduler.set_timesteps(a  ,     a  ,     a  ,     self.device		)
								__UpperCamelCase			:     Tuple         =      eta

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

															else:
																						# compute the reverse: x_t-1 -> x_t
																						__UpperCamelCase			:     List[str]         =      self.scheduler.undo_step(a  ,     a  ,     a		)
															__UpperCamelCase			:     Dict         =      t

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

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a		)