





"""simple docstring"""




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        _SCREAMING_SNAKE_CASE						(     _lowerCAmelCase   ):







 '''simple docstring'''
 lowercase_		=      42
 lowercase_		=      42
 lowercase_		=      None



class        _SCREAMING_SNAKE_CASE						(     _lowerCAmelCase   ,							_lowerCAmelCase   ):







 '''simple docstring'''
 lowercase_		=      2

 @register_to_config
 def __init__(self    :       Any	,			UpperCAmelCase_    :       float = 0.02	,			UpperCAmelCase_    :       float = 100	,			UpperCAmelCase_    :       float = 1.007	,			UpperCAmelCase_    :       float = 80	,			UpperCAmelCase_    :       float = 0.05	,			UpperCAmelCase_    :       float = 50	,			)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =sigma_max

   # setable values
   lowerCamelCase__:				Union[str, Any]        =None
   lowerCamelCase__:				Union[str, Any]        =None
   lowerCamelCase__:				int        =None  # sigma(t_i)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			UpperCAmelCase_    :       torch.FloatTensor	,			UpperCAmelCase_    :       Optional[int] = None)	->str:



   '''simple docstring'''



   return sample

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       Union[str, torch.device] = None)	->Any:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =num_inference_steps
   lowerCamelCase__:				Optional[int]        =np.arange(0	,			self.num_inference_steps)[::-1].copy()
   lowerCamelCase__:				Any        =torch.from_numpy(_lowercase).to(_lowercase)
   lowerCamelCase__:				Optional[int]        =[
       (
           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
   ]
   lowerCamelCase__:				Optional[int]        =torch.tensor(_lowercase	,			dtype=torch.floataa	,			device=_lowercase)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			UpperCAmelCase_    :       torch.FloatTensor	,			UpperCAmelCase_    :       float	,			UpperCAmelCase_    :       Optional[torch.Generator] = None)	->Any:



   '''simple docstring'''



   if self.config.s_min <= sigma <= self.config.s_max:
     lowerCamelCase__:				Tuple        =min(self.config.s_churn / self.num_inference_steps	,			2**0.5 - 1)
   else:
     lowerCamelCase__:				List[Any]        =0

   # sample eps ~ N(0, S_noise^2 * I)
   lowerCamelCase__:				Any        =self.config.s_noise * randn_tensor(sample.shape	,			generator=_lowercase).to(sample.device)
   lowerCamelCase__:				Optional[int]        =sigma + gamma * sigma
   lowerCamelCase__:				Tuple        =sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

   return sample_hat, sigma_hat

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       torch.FloatTensor	,			UpperCAmelCase_    :       float	,			UpperCAmelCase_    :       float	,			UpperCAmelCase_    :       torch.FloatTensor	,			UpperCAmelCase_    :       bool = True	,			)	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =sample_hat + sigma_hat * model_output
   lowerCamelCase__:				Dict        =(sample_hat - pred_original_sample) / sigma_hat
   lowerCamelCase__:				Tuple        =sample_hat + (sigma_prev - sigma_hat) * derivative

   if not return_dict:
     return (sample_prev, derivative)

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

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       torch.FloatTensor	,			UpperCAmelCase_    :       float	,			UpperCAmelCase_    :       float	,			UpperCAmelCase_    :       torch.FloatTensor	,			UpperCAmelCase_    :       torch.FloatTensor	,			UpperCAmelCase_    :       torch.FloatTensor	,			UpperCAmelCase_    :       bool = True	,			)	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				int        =sample_prev + sigma_prev * model_output
   lowerCamelCase__:				Tuple        =(sample_prev - pred_original_sample) / sigma_prev
   lowerCamelCase__:				Optional[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=_lowercase	,			derivative=_lowercase	,			pred_original_sample=_lowercase)







 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       Tuple)	->Optional[Any]:



   '''simple docstring'''



   raise NotImplementedError()








from __future__ import annotations

from typing import Any



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self    :       Any	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   lowerCamelCase__:				int        =num_of_nodes
   lowerCamelCase__:				list[list[int]]        =[]
   lowerCamelCase__:				dict[int, int]        ={}

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       int)	->int:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   if self.m_component[u_node] != u_node:
     for k in self.m_component:
       lowerCamelCase__:				Dict        =self.find_component(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       list[int]	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int)	->None:



   '''simple docstring'''



   if component_size[u_node] <= component_size[v_node]:
     lowerCamelCase__:				Optional[int]        =v_node
     component_size[v_node] += component_size[u_node]
     self.set_component(UpperCAmelCase_)

   elif component_size[u_node] >= component_size[v_node]:
     lowerCamelCase__:				Tuple        =self.find_component(UpperCAmelCase_)
     component_size[u_node] += component_size[v_node]
     self.set_component(UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->None:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =[]
   lowerCamelCase__:				List[str]        =0

   lowerCamelCase__:				list[Any]        =[-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)

   lowerCamelCase__:				List[str]        =self.m_num_of_nodes

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

       lowerCamelCase__:				List[Any]        =self.m_component[u]
       lowerCamelCase__:				str        =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
           ):
             lowerCamelCase__:				Union[str, Any]        =[u, v, w]

     for edge in minimum_weight_edge:
       if isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
         lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Tuple        =edge

         lowerCamelCase__:				str        =self.m_component[u]
         lowerCamelCase__:				Any        =self.m_component[v]

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

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





def 					lowerCAmelCase_			(	)					->		None:







  """simple docstring"""







if __name__ == "__main__":
     import doctest

     doctest.testmod()

