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

import numpy as np
import torch

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




@dataclass
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     42
	_A								=     42
	_A								=     None




class  lowerCamelCase__							(				__lowercase     ,						__lowercase):




	'''simple docstring'''




	_A								=     2







	@register_to_config
	def __init__(					self		:int     ,   a		:float = 0.02     ,   a		:float = 1_0_0     ,   a		:float = 1.007     ,   a		:float = 8_0     ,   a		:float = 0.05     ,   a		:float = 5_0     ,   )		-> Dict:
								# standard deviation of the initial noise distribution
								__UpperCamelCase	:   Any			   =					sigma_max

								# setable values
								__UpperCamelCase	:   int			   =					None
								__UpperCamelCase	:   np.IntTensor			   =					None
								__UpperCamelCase	:   torch.FloatTensor			   =					None  # sigma(t_i)







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:torch.FloatTensor     ,   a		:Optional[int] = None						)		-> torch.FloatTensor:
								return sample







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:int     ,   a		:Union[str, torch.device] = None						)		-> Optional[Any]:
								__UpperCamelCase	:   str			   =					num_inference_steps
								__UpperCamelCase	:   int			   =					np.arange(0     ,   self.num_inference_steps						)[::-1].copy()
								__UpperCamelCase	:   Dict			   =					torch.from_numpy(a						).to(a						)
								__UpperCamelCase	:   Dict			   =					[
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								__UpperCamelCase	:   Dict			   =					torch.tensor(a     ,   dtype=torch.floataa     ,   device=a						)







	def    _lowerCamelCase				(					self		:Dict     ,   a		:torch.FloatTensor     ,   a		:float     ,   a		:Optional[torch.Generator] = None						)		-> Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
															__UpperCamelCase	:   Any			   =					min(self.config.s_churn / self.num_inference_steps     ,   2**0.5 - 1						)
								else:
															__UpperCamelCase	:   Tuple			   =					0

								# sample eps ~ N(0, S_noise^2 * I)
								__UpperCamelCase	:   Dict			   =					self.config.s_noise * randn_tensor(sample.shape     ,   generator=a						).to(sample.device						)
								__UpperCamelCase	:   Dict			   =					sigma + gamma * sigma
								__UpperCamelCase	:   str			   =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat







	def    _lowerCamelCase				(					self		:int     ,   a		:torch.FloatTensor     ,   a		:float     ,   a		:float     ,   a		:torch.FloatTensor     ,   a		:bool = True     ,   )		-> Union[KarrasVeOutput, Tuple]:
								__UpperCamelCase	:   str			   =					sample_hat + sigma_hat * model_output
								__UpperCamelCase	:   str			   =					(sample_hat - pred_original_sample) / sigma_hat
								__UpperCamelCase	:   int			   =					sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
															return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=a     ,   derivative=a     ,   pred_original_sample=a						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:torch.FloatTensor     ,   a		:float     ,   a		:float     ,   a		:torch.FloatTensor     ,   a		:torch.FloatTensor     ,   a		:torch.FloatTensor     ,   a		:bool = True     ,   )		-> Union[KarrasVeOutput, Tuple]:
								__UpperCamelCase	:   Union[str, Any]			   =					sample_prev + sigma_prev * model_output
								__UpperCamelCase	:   Tuple			   =					(sample_prev - pred_original_sample) / sigma_prev
								__UpperCamelCase	:   Any			   =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
															return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=a     ,   derivative=a     ,   pred_original_sample=a						)







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:Optional[Any]     ,   a		:Any     ,   a		:List[str]						)		-> int:
								raise NotImplementedError()

class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:int						)		-> Dict:
								__UpperCamelCase	:   Union[str, Any]			   =					{}







	def    _lowerCamelCase				(					self		:str						)		-> None:
								print(self.vertex						)
								for i in self.vertex:
															print(a     ,   " -> "     ,   " -> ".join([str(a						) for j in self.vertex[i]]						)						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:int     ,   a		:int						)		-> None:
								# check if vertex is already present,
								if from_vertex in self.vertex:
															self.vertex[from_vertex].append(a						)
								else:
															# else make a new vertex
															__UpperCamelCase	:   Optional[Any]			   =					[to_vertex]







	def    _lowerCamelCase				(					self		:Tuple						)		-> None:
								# visited array for storing already visited nodes
								__UpperCamelCase	:   Dict			   =					[False] * len(self.vertex						)

								# call the recursive helper function
								for i in range(len(self.vertex						)						):
															if not visited[i]:
																						self.dfs_recursive(a     ,   a						)







	def    _lowerCamelCase				(					self		:Any     ,   a		:int     ,   a		:list						)		-> None:
								# mark start vertex as visited
								__UpperCamelCase	:   int			   =					True

								print(a     ,   end=" "						)

								# Recur for all the vertices that are adjacent to this node
								for i in self.vertex:
															if not visited[i]:
																						self.dfs_recursive(a     ,   a						)


if __name__ == "__main__":
		lowercase  :					Dict													=       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()
		print('DFS:')
		g.dfs()

		# OUTPUT:
		# 0  ->  1 -> 2
		# 1  ->  2
		# 2  ->  0 -> 3
		# 3  ->  3
		# DFS:
		#  0 1 2 3