



'''simple docstring'''

from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


_A					:     List[Any]      =logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
class 		lowerCamelCase__   (    UpperCAmelCase__  ):




					'''simple docstring'''



					A_         =    42






class 		lowerCamelCase__   (    UpperCAmelCase__						, UpperCAmelCase__  ):




					'''simple docstring'''



					A_         =    1




					@register_to_config
					def __init__(	self					:						Dict  ,	UpperCamelCase_					:						str = 40  ,	UpperCamelCase_					:						int = 0.0_02  ,	UpperCamelCase_					:						str = 80.0  ,	UpperCamelCase_					:						str = 0.5  ,	UpperCamelCase_					:						int = 1.0  ,	UpperCamelCase_					:						Dict = 7.0  ,	UpperCamelCase_					:						Any = True  ,	)	->							Dict:



						'''simple docstring'''





						_lowercase       :							List[Any]  	=		sigma_max

						_lowercase       :							Dict  	=		np.linspace(0  ,	1  ,	UpperCamelCase_       )
						_lowercase       :							Optional[int]  	=		self._convert_to_karras(UpperCamelCase_       )
						_lowercase       :							List[Any]  	=		self.sigma_to_t(UpperCamelCase_       )

						# setable values
						_lowercase       :							int  	=		None
						_lowercase       :							str  	=		torch.from_numpy(UpperCamelCase_       )
						_lowercase       :							Optional[int]  	=		torch.from_numpy(UpperCamelCase_       )
						_lowercase       :							str  	=		False
						_lowercase       :							List[str]  	=		False




					def     __UpperCAmelCase  (	self					:						Tuple  ,	UpperCamelCase_					:						List[str]  ,	UpperCamelCase_					:						List[str]=None       )	->							Optional[Any]:



						'''simple docstring'''





						if schedule_timesteps is None:
							_lowercase       :							List[Any]  	=		self.timesteps

						_lowercase       :							List[Any]  	=		(schedule_timesteps == timestep).nonzero()
						return indices.item()




					def     __UpperCAmelCase  (	self					:						str  ,	UpperCamelCase_					:						Any  ,	UpperCamelCase_					:						Dict       )	->							torch.FloatTensor:



						'''simple docstring'''





						if isinstance(UpperCamelCase_  ,	torch.Tensor       ):
							_lowercase       :							str  	=		timestep.to(self.timesteps.device       )
						_lowercase       :							Tuple  	=		self.index_for_timestep(UpperCamelCase_       )
						_lowercase       :							int  	=		self.sigmas[step_idx]

						_lowercase       :							int  	=		sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

						_lowercase       :							Dict  	=		True
						return sample




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						List[str]       )	->							Any:



						'''simple docstring'''





						if not isinstance(UpperCamelCase_  ,	np.ndarray       ):
							_lowercase       :							int  	=		np.array(UpperCamelCase_  ,	dtype=np.floataa       )

						_lowercase       :							Optional[int]  	=		1000 * 0.25 * np.log(sigmas + 1E-44       )

						return timesteps




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						List[str] = None  ,	UpperCamelCase_					:						List[str] = None  ,	UpperCamelCase_					:						Tuple = None  ,	)	->							Union[str, Any]:



						'''simple docstring'''





						if num_inference_steps is None and timesteps is None:
							raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'       )

						if num_inference_steps is not None and timesteps is not None:
							raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.'       )

						# Follow DDPMScheduler custom timesteps logic
						if timesteps is not None:
							for i in range(1  ,	len(UpperCamelCase_       )       ):
								if timesteps[i] >= timesteps[i - 1]:
									raise ValueError('`timesteps` must be in descending order.'       )

							if timesteps[0] >= self.config.num_train_timesteps:
								raise ValueError(
								    F'''`timesteps` must start before `self.config.train_timesteps`:'''
								    F''' {self.config.num_train_timesteps}.'''       )

							_lowercase       :							int  	=		np.array(UpperCamelCase_  ,	dtype=np.intaa       )
							_lowercase       :							List[str]  	=		True
						else:
							if num_inference_steps > self.config.num_train_timesteps:
								raise ValueError(
								    F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
								    F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
								    F''' maximal {self.config.num_train_timesteps} timesteps.'''       )

							_lowercase       :							int  	=		num_inference_steps

							_lowercase       :							Optional[int]  	=		self.config.num_train_timesteps // self.num_inference_steps
							_lowercase       :							str  	=		(np.arange(0  ,	UpperCamelCase_       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
							_lowercase       :							List[Any]  	=		False

						# Map timesteps to Karras sigmas directly for multistep sampling
						# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
						_lowercase       :							Union[str, Any]  	=		self.config.num_train_timesteps
						_lowercase       :							Union[str, Any]  	=		timesteps[::-1].copy()
						_lowercase       :							Optional[Any]  	=		ramp / (num_train_timesteps - 1)
						_lowercase       :							Optional[int]  	=		self._convert_to_karras(UpperCamelCase_       )
						_lowercase       :							List[str]  	=		self.sigma_to_t(UpperCamelCase_       )

						_lowercase       :							List[Any]  	=		np.concatenate([sigmas, [self.sigma_min]]       ).astype(np.floataa       )
						_lowercase       :							Optional[Any]  	=		torch.from_numpy(UpperCamelCase_       ).to(device=UpperCamelCase_       )

						if str(UpperCamelCase_       ).startswith('mps'       ):
							# mps does not support float64
							_lowercase       :							int  	=		torch.from_numpy(UpperCamelCase_       ).to(UpperCamelCase_  ,	dtype=torch.floataa       )
						else:
							_lowercase       :							Optional[Any]  	=		torch.from_numpy(UpperCamelCase_       ).to(device=UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						str       )	->							List[str]:



						'''simple docstring'''





						_lowercase       :							float  	=		self.config.sigma_min
						_lowercase       :							float  	=		self.config.sigma_max

						_lowercase       :							str  	=		self.config.rho
						_lowercase       :							str  	=		sigma_min ** (1 / rho)
						_lowercase       :							Dict  	=		sigma_max ** (1 / rho)
						_lowercase       :							Optional[Any]  	=		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
						return sigmas




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						Optional[Any]       )	->							Union[str, Any]:



						'''simple docstring'''





						_lowercase       :							Dict  	=		self.config.sigma_data

						_lowercase       :							Any  	=		sigma_data**2 / (sigma**2 + sigma_data**2)
						_lowercase       :							List[Any]  	=		sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
						return c_skip, c_out




					def     __UpperCAmelCase  (	self					:						Dict  ,	UpperCamelCase_					:						Dict       )	->							Union[str, Any]:



						'''simple docstring'''





						_lowercase       :							Any  	=		self.config.sigma_min
						_lowercase       :							List[Any]  	=		self.config.sigma_data

						_lowercase       :							List[Any]  	=		sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
						_lowercase       :							Dict  	=		(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
						return c_skip, c_out




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						Union[str, Any]  ,	UpperCamelCase_					:						Any  ,	UpperCamelCase_					:						Optional[Any]  ,	UpperCamelCase_					:						Optional[int] = None  ,	UpperCamelCase_					:						Union[str, Any] = True  ,	)	->							Union[CMStochasticIterativeSchedulerOutput, Tuple]:



						'''simple docstring'''





						if (
						    isinstance(UpperCamelCase_  ,	UpperCamelCase_       )
						    or isinstance(UpperCamelCase_  ,	torch.IntTensor       )
						    or isinstance(UpperCamelCase_  ,	torch.LongTensor       )
						):
							raise ValueError(
							    (
							        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
							        F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
							        ' one of the `scheduler.timesteps` as a timestep.'
							    )  ,	)

						if not self.is_scale_input_called:
							logger.warning(
							    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
							    'See `StableDiffusionPipeline` for a usage example.'       )

						if isinstance(UpperCamelCase_  ,	torch.Tensor       ):
							_lowercase       :							Any  	=		timestep.to(self.timesteps.device       )

						_lowercase       :							Dict  	=		self.config.sigma_min
						_lowercase       :							List[Any]  	=		self.config.sigma_max

						_lowercase       :							Any  	=		self.index_for_timestep(UpperCamelCase_       )

						# sigma_next corresponds to next_t in original implementation
						_lowercase       :							Tuple  	=		self.sigmas[step_index]
						if step_index + 1 < self.config.num_train_timesteps:
							_lowercase       :							Union[str, Any]  	=		self.sigmas[step_index + 1]
						else:
							# Set sigma_next to sigma_min
							_lowercase       :							Dict  	=		self.sigmas[-1]

						# Get scalings for boundary conditions
						_lowercase       :							Dict  	=		self.get_scalings_for_boundary_condition(UpperCamelCase_       )

						# 1. Denoise model output using boundary conditions
						_lowercase       :							Dict  	=		c_out * model_output + c_skip * sample
						if self.config.clip_denoised:
							_lowercase       :							Union[str, Any]  	=		denoised.clamp(-1  ,	1       )

						# 2. Sample z ~ N(0, s_noise^2 * I)
						# Noise is not used for onestep sampling.
						if len(self.timesteps       ) > 1:
							_lowercase       :							Optional[Any]  	=		randn_tensor(
							    model_output.shape  ,	dtype=model_output.dtype  ,	device=model_output.device  ,	generator=UpperCamelCase_       )
						else:
							_lowercase       :							Optional[int]  	=		torch.zeros_like(UpperCamelCase_       )
						_lowercase       :							Union[str, Any]  	=		noise * self.config.s_noise

						_lowercase       :							Union[str, Any]  	=		sigma_next.clamp(min=UpperCamelCase_  ,	max=UpperCamelCase_       )

						# 3. Return noisy sample
						# tau = sigma_hat, eps = sigma_min
						_lowercase       :							Any  	=		denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

						if not return_dict:
							return (prev_sample,)

						return CMStochasticIterativeSchedulerOutput(prev_sample=UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						List[str]  ,	UpperCamelCase_					:						Tuple  ,	UpperCamelCase_					:						List[str]  ,	UpperCamelCase_					:						Any  ,	)	->							torch.FloatTensor:



						'''simple docstring'''





						_lowercase       :							Tuple  	=		self.sigmas.to(device=original_samples.device  ,	dtype=original_samples.dtype       )
						if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase_       ):
							# mps does not support float64
							_lowercase       :							Any  	=		self.timesteps.to(original_samples.device  ,	dtype=torch.floataa       )
							_lowercase       :							Tuple  	=		timesteps.to(original_samples.device  ,	dtype=torch.floataa       )
						else:
							_lowercase       :							List[Any]  	=		self.timesteps.to(original_samples.device       )
							_lowercase       :							Optional[Any]  	=		timesteps.to(original_samples.device       )

						_lowercase       :							str  	=		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

						_lowercase       :							str  	=		sigmas[step_indices].flatten()
						while len(sigma.shape       ) < len(original_samples.shape       ):
							_lowercase       :							Any  	=		sigma.unsqueeze(-1       )

						_lowercase       :							Union[str, Any]  	=		original_samples + noise * sigma
						return noisy_samples




					def __len__(	self					:						Tuple       )	->							Any:



						'''simple docstring'''





						return self.config.num_train_timesteps


from __future__ import annotations

from queue import Queue


class 	UpperCamelCase__       :

						'''simple docstring'''



						def __init__(  self	)  ->None:
									UpperCAmelCase__		:dict[int, list[int]] 		=    {}


						def    A__       (  self	)  ->None:
									for i in self.vertices:
												print(A  , ' : '  , ' -> '.join([str(A	) for j in self.vertices[i]]	)	)


						def    A__       (  self  , A  , A	)  ->None:
									if from_vertex in self.vertices:
												self.vertices[from_vertex].append(A	)
									else:
												UpperCAmelCase__		:Union[str, Any] 		=    [to_vertex]


						def    A__       (  self  , A	)  ->set[int]:
									UpperCAmelCase__		:str 		=    set()

									# create a first in first out queue to store all the vertices for BFS
									UpperCAmelCase__		:Queue 		=    Queue()

									# mark the source node as visited and enqueue it
									visited.add(A	)
									queue.put(A	)

									while not queue.empty():
												UpperCAmelCase__		:List[str] 		=    queue.get()

												# loop through all adjacent vertex and enqueue it if not yet visited
												for adjacent_vertex in self.vertices[vertex]:
															if adjacent_vertex not in visited:
																		queue.put(A	)
																		visited.add(A	)
									return visited


if __name__ == "__main__":
						from doctest import testmod

						testmod(verbose=True)

						__snake_case					:							Any           =							Graph()
						g.add_edge(0, 1)
						g.add_edge(0, 2)
						g.add_edge(1, 2)
						g.add_edge(2, 0)
						g.add_edge(2, 3)
						g.add_edge(3, 3)

						g.print_graph()
						# 0  :  1 -> 2
						# 1  :  2
						# 2  :  0 -> 3
						# 3  :  3

						assert sorted(g.bfs(2)) == [0, 1, 2, 3]





