






from __future__ import annotations

from collections import Counter
from random import random




class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self   )			->       List[str]:
								snake_case_           =				{}




	def 				_UpperCamelCase     (			self			,     a   )			->       None:
								snake_case_           =				{}




	def 				_UpperCamelCase     (			self			,     a			,     a			,     a   )			->       None:
								if nodea not in self.connections:
															self.add_node(a   )
								if nodea not in self.connections:
															self.add_node(a   )
								snake_case_           =				probability




	def 				_UpperCamelCase     (			self   )			->       list[str]:
								return list(self.connections   )






	def 				_UpperCamelCase     (			self			,     a   )			->       str:
								snake_case_           =				0
								snake_case_           =				random()

								for dest in self.connections[node]:
															current_probability += self.connections[node][dest]
															if current_probability > random_value:
																						return dest
								return ""






def 		__UpperCAmelCase     (	a_				,	a_				,	a_):
							snake_case_           =				MarkovChainGraphUndirectedUnweighted()

							for nodea, nodea, probability in transitions:
														graph.add_transition_probability(a_				,	a_				,	a_)

							snake_case_           =				Counter(graph.get_nodes())
							snake_case_           =				start

							for _ in range(a_):
														snake_case_           =				graph.transition(a_)
														visited[node] += 1

							return visited


if __name__ == "__main__":
	import doctest

	doctest.testmod()













from typing import List, Optional, Tuple, Union

import torch

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




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       Optional[Any]:
								super().__init__()

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

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






	@torch.no_grad()
	def __call__(			self			,     a = 1			,     a = None			,     a = 0.0			,     a = 50			,     a = None			,     a = "pil"			,     a = True			,     )			->       Union[ImagePipelineOutput, Tuple]:

								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size			,     a   ):
															snake_case_           =				(
															    batch_size,
															    self.unet.config.in_channels,
															    self.unet.config.sample_size,
															    self.unet.config.sample_size,
															)
								else:
															snake_case_           =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								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.'''   )

								snake_case_           =				randn_tensor(a			,     generator=a			,     device=self.device			,     dtype=self.unet.dtype   )

								# set step values
								self.scheduler.set_timesteps(a   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
															# 1. predict noise model_output
															snake_case_           =				self.unet(a			,     a   ).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
															snake_case_           =				self.scheduler.step(
															    a			,     a			,     a			,     eta=a			,     use_clipped_model_output=a			,     generator=a   ).prev_sample

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

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a   )





