
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


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


@dataclass
class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						__a     :    torch.FloatTensor


class 	UpperCamelCase__       (  UpperCAmelCase__						,			UpperCAmelCase__):

						'''simple docstring'''



						__a     :    Optional[int]												=			1


						@register_to_config
						def __init__(  self  , A = 40  , A = 0.002  , A = 80.0  , A = 0.5  , A = 1.0  , A = 7.0  , A = True  , )  ->Dict:
									# standard deviation of the initial noise distribution
									UpperCAmelCase__		:List[Any] 		=    sigma_max

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

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


						def    A__       (  self  , A  , A=None	)  ->Optional[Any]:
									if schedule_timesteps is None:
												UpperCAmelCase__		:List[Any] 		=    self.timesteps

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


						def    A__       (  self  , A  , A	)  ->torch.FloatTensor:
									# Get sigma corresponding to timestep
									if isinstance(A  , torch.Tensor	):
												UpperCAmelCase__		:str 		=    timestep.to(self.timesteps.device	)
									UpperCAmelCase__		:Tuple 		=    self.index_for_timestep(A	)
									UpperCAmelCase__		:int 		=    self.sigmas[step_idx]

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

									UpperCAmelCase__		:Dict 		=    True
									return sample


						def    A__       (  self  , A	)  ->Any:
									if not isinstance(A  , np.ndarray	):
												UpperCAmelCase__		:int 		=    np.array(A  , dtype=np.floataa	)

									UpperCAmelCase__		:Optional[int] 		=    10_00 * 0.25 * np.log(sigmas + 1e-44	)

									return timesteps


						def    A__       (  self  , A = None  , A = None  , A = None  , )  ->Union[str, Any]:
									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(A	)	):
															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}."""	)

												UpperCAmelCase__		:int 		=    np.array(A  , dtype=np.intaa	)
												UpperCAmelCase__		: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."""	)

												UpperCAmelCase__		:int 		=    num_inference_steps

												UpperCAmelCase__		:Optional[int] 		=    self.config.num_train_timesteps // self.num_inference_steps
												UpperCAmelCase__		:str 		=    (np.arange(0  , A	) * step_ratio).round()[::-1].copy().astype(np.intaa	)
												UpperCAmelCase__		: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
									UpperCAmelCase__		:Union[str, Any] 		=    self.config.num_train_timesteps
									UpperCAmelCase__		:Union[str, Any] 		=    timesteps[::-1].copy()
									UpperCAmelCase__		:Optional[Any] 		=    ramp / (num_train_timesteps - 1)
									UpperCAmelCase__		:Optional[int] 		=    self._convert_to_karras(A	)
									UpperCAmelCase__		:List[str] 		=    self.sigma_to_t(A	)

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

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


						def    A__       (  self  , A	)  ->List[str]:
									UpperCAmelCase__		:float 		=    self.config.sigma_min
									UpperCAmelCase__		:float 		=    self.config.sigma_max

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


						def    A__       (  self  , A	)  ->Union[str, Any]:
									UpperCAmelCase__		:Dict 		=    self.config.sigma_data

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


						def    A__       (  self  , A	)  ->Union[str, Any]:
									UpperCAmelCase__		:Any 		=    self.config.sigma_min
									UpperCAmelCase__		:List[Any] 		=    self.config.sigma_data

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


						def    A__       (  self  , A  , A  , A  , A = None  , A = True  , )  ->Union[CMStochasticIterativeSchedulerOutput, Tuple]:

									if (
									    isinstance(A  , A	)
									    or isinstance(A  , torch.IntTensor	)
									    or isinstance(A  , 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(A  , torch.Tensor	):
												UpperCAmelCase__		:Any 		=    timestep.to(self.timesteps.device	)

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

									UpperCAmelCase__		:Any 		=    self.index_for_timestep(A	)

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

									# Get scalings for boundary conditions
									UpperCAmelCase__		,       UpperCAmelCase__		:Dict 		=    self.get_scalings_for_boundary_condition(A	)

									# 1. Denoise model output using boundary conditions
									UpperCAmelCase__		:Dict 		=    c_out * model_output + c_skip * sample
									if self.config.clip_denoised:
												UpperCAmelCase__		: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:
												UpperCAmelCase__		:Optional[Any] 		=    randn_tensor(
												    model_output.shape  , dtype=model_output.dtype  , device=model_output.device  , generator=A	)
									else:
												UpperCAmelCase__		:Optional[int] 		=    torch.zeros_like(A	)
									UpperCAmelCase__		:Union[str, Any] 		=    noise * self.config.s_noise

									UpperCAmelCase__		:Union[str, Any] 		=    sigma_next.clamp(min=A  , max=A	)

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

									if not return_dict:
												return (prev_sample,)

									return CMStochasticIterativeSchedulerOutput(prev_sample=A	)


						def    A__       (  self  , A  , A  , A  , )  ->torch.FloatTensor:
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									UpperCAmelCase__		:Tuple 		=    self.sigmas.to(device=original_samples.device  , dtype=original_samples.dtype	)
									if original_samples.device.type == "mps" and torch.is_floating_point(A	):
												# mps does not support float64
												UpperCAmelCase__		:Any 		=    self.timesteps.to(original_samples.device  , dtype=torch.floataa	)
												UpperCAmelCase__		:Tuple 		=    timesteps.to(original_samples.device  , dtype=torch.floataa	)
									else:
												UpperCAmelCase__		:List[Any] 		=    self.timesteps.to(original_samples.device	)
												UpperCAmelCase__		:Optional[Any] 		=    timesteps.to(original_samples.device	)

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

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

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


						def __len__(  self	)  ->Any:
									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]





