





'''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


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




def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[List, PIL.Image.Image, torch.Tensor]				):
			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										=							[image]

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

						__lowercase										=							[np.array(i.resize((w, h)		,       resample=PIL_INTERPOLATION['''lanczos''']				)				)[None, :] for i in image]
						__lowercase										=							np.concatenate(lowerCamelCase_		,       axis=0				)
						__lowercase										=							np.array(lowerCamelCase_				).astype(np.floataa				) / 2_5_5.0
						__lowercase										=							image.transpose(0		,       3		,       1		,       2				)
						__lowercase										=							2.0 * image - 1.0
						__lowercase										=							torch.from_numpy(lowerCamelCase_				)
			elif isinstance(image[0]		,       torch.Tensor				):
						__lowercase										=							torch.cat(lowerCamelCase_		,       dim=0				)
			return image




def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[List, PIL.Image.Image, torch.Tensor]				):
			if isinstance(lowerCamelCase_		,       torch.Tensor				):
						return mask
			elif isinstance(lowerCamelCase_		,       PIL.Image.Image				):
						__lowercase										=							[mask]

			if isinstance(mask[0]		,       PIL.Image.Image				):
						__lowercase						,		__lowercase										=							mask[0].size
						__lowercase						,		__lowercase										=							(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
						__lowercase										=							[np.array(m.convert('''L'''				).resize((w, h)		,       resample=PIL_INTERPOLATION['''nearest''']				)				)[None, :] for m in mask]
						__lowercase										=							np.concatenate(lowerCamelCase_		,       axis=0				)
						__lowercase										=							mask.astype(np.floataa				) / 2_5_5.0
						__lowercase										=							0
						__lowercase										=							1
						__lowercase										=							torch.from_numpy(lowerCamelCase_				)
			elif isinstance(mask[0]		,       torch.Tensor				):
						__lowercase										=							torch.cat(lowerCamelCase_		,       dim=0				)
			return mask






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				UNetaDModel
	a    :				RePaintScheduler


	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





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


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







				'''simple docstring'''





				__lowercase										=							image

				__lowercase										=							_preprocess_image(_lowerCamelCase )
				__lowercase										=							original_image.to(device=self.device     ,dtype=self.unet.dtype )
				__lowercase										=							_preprocess_mask(_lowerCamelCase )
				__lowercase										=							mask_image.to(device=self.device     ,dtype=self.unet.dtype )

				__lowercase										=							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										=							original_image.shape
				__lowercase										=							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										=							eta

				__lowercase										=							self.scheduler.timesteps[0] + 1
				__lowercase										=							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										=							self.unet(_lowerCamelCase     ,_lowerCamelCase ).sample
										# compute previous image: x_t -> x_t-1
										__lowercase										=							self.scheduler.step(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase ).prev_sample

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

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

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_lowerCamelCase )












'''simple docstring'''



from __future__ import annotations

from typing import Any






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				__lowercase										=							num_of_nodes
				__lowercase										=							[]
				__lowercase										=							{}


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				self.m_edges.append([u_node, v_node, weight] )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





				if self.m_component[u_node] == u_node:
							return u_node
				return self.find_component(self.m_component[u_node] )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				if self.m_component[u_node] != u_node:
							for k in self.m_component:
										__lowercase										=							self.find_component(_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				if component_size[u_node] <= component_size[v_node]:
							__lowercase										=							v_node
							component_size[v_node] += component_size[u_node]
							self.set_component(_lowerCamelCase )

				elif component_size[u_node] >= component_size[v_node]:
							__lowercase										=							self.find_component(_lowerCamelCase )
							component_size[u_node] += component_size[v_node]
							self.set_component(_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							None:







				'''simple docstring'''





				__lowercase										=							[]
				__lowercase										=							0

				__lowercase										=							[-1] * self.m_num_of_nodes

				# A list of components (initialized to all of the nodes)
				for node in range(self.m_num_of_nodes ):
							self.m_component.update({node: node} )
							component_size.append(1 )

				__lowercase										=							self.m_num_of_nodes

				while num_of_components > 1:
							for edge in self.m_edges:
										__lowercase						,		__lowercase						,		__lowercase										=							edge

										__lowercase										=							self.m_component[u]
										__lowercase										=							self.m_component[v]

										if u_component != v_component:

													for component in (u_component, v_component):
																if (
																    minimum_weight_edge[component] == -1
																    or minimum_weight_edge[component][2] > w
																):
																			__lowercase										=							[u, v, w]

							for edge in minimum_weight_edge:
										if isinstance(_lowerCamelCase     ,_lowerCamelCase ):
													__lowercase						,		__lowercase						,		__lowercase										=							edge

													__lowercase										=							self.m_component[u]
													__lowercase										=							self.m_component[v]

													if u_component != v_component:
																mst_weight += w
																self.union(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )
																print(f"Added edge [{u} - {v}]\nAdded weight: {w}\n" )
																num_of_components -= 1

							__lowercase										=							[-1] * self.m_num_of_nodes
				print(f"The total weight of the minimal spanning tree is: {mst_weight}" )





def    _lowerCAmelCase						(				):
			pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()





