





"""simple docstring"""




# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class        _SCREAMING_SNAKE_CASE						(     __UpperCamelCase   ):







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





def 					lowerCAmelCase_			(	__a     ,   __a=0.9_9_9     ,   __a="cosine"     ,   )					->		Any:







  """simple docstring"""





  if alpha_transform_type == "cosine":

    def alpha_bar_fn(__a		):
      return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2		) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(__a		):
      return math.exp(t * -1_2.0		)

  else:
    raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""		)

  lowerCamelCase__:				Optional[Any]        =[]
  for i in range(__SCREAMING_SNAKE_CASE		):
    lowerCamelCase__:				int        =i / num_diffusion_timesteps
    lowerCamelCase__:				Dict        =(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(__SCREAMING_SNAKE_CASE		) / alpha_bar_fn(__SCREAMING_SNAKE_CASE		)     ,   __SCREAMING_SNAKE_CASE		)		)
  return torch.tensor(__SCREAMING_SNAKE_CASE     ,   dtype=torch.floataa		)






class        _SCREAMING_SNAKE_CASE						(     __UpperCamelCase   ,							__UpperCamelCase   ):







 '''simple docstring'''
 lowercase_		=      1

 @register_to_config
 def __init__(self    :       Optional[Any]	,			UpperCAmelCase_    :       int = 1_000	,			UpperCAmelCase_    :       float = 0.0001	,			UpperCAmelCase_    :       float = 0.02	,			UpperCAmelCase_    :       str = "linear"	,			UpperCAmelCase_    :       Optional[Union[np.ndarray, List[float]]] = None	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       int = 0	,			UpperCAmelCase_    :       str = "epsilon"	,			UpperCAmelCase_    :       float = 1.0	,			**UpperCAmelCase_    :       List[Any]	,			)	->int:



   '''simple docstring'''



   if kwargs.get("set_alpha_to_one"	,			_lowerCAmelCase) is not None:
     lowerCamelCase__:				str        =(
         """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
     )
     deprecate("set_alpha_to_one"	,			"1.0.0"	,			_lowerCAmelCase	,			standard_warn=_lowerCAmelCase)
     lowerCamelCase__:				List[Any]        =kwargs["""set_alpha_to_one"""]
   if trained_betas is not None:
     lowerCamelCase__:				Optional[int]        =torch.tensor(_lowerCAmelCase	,			dtype=torch.floataa)
   elif beta_schedule == "linear":
     lowerCamelCase__:				Tuple        =torch.linspace(_lowerCAmelCase	,			_lowerCAmelCase	,			_lowerCAmelCase	,			dtype=torch.floataa)
   elif beta_schedule == "scaled_linear":
     # this schedule is very specific to the latent diffusion model.
     lowerCamelCase__:				List[Any]        =(
         torch.linspace(beta_start**0.5	,			beta_end**0.5	,			_lowerCAmelCase	,			dtype=torch.floataa) ** 2
     )
   elif beta_schedule == "squaredcos_cap_v2":
     # Glide cosine schedule
     lowerCamelCase__:				int        =betas_for_alpha_bar(_lowerCAmelCase)
   else:
     raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

   lowerCamelCase__:				Any        =1.0 - self.betas
   lowerCamelCase__:				List[Any]        =torch.cumprod(self.alphas	,			dim=0)

   # At every step in inverted ddim, we are looking into the next alphas_cumprod
   # For the final step, there is no next alphas_cumprod, and the index is out of bounds
   # `set_alpha_to_zero` decides whether we set this parameter simply to zero
   # in this case, self.step() just output the predicted noise
   # or whether we use the final alpha of the "non-previous" one.
   lowerCamelCase__:				int        =torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]

   # standard deviation of the initial noise distribution
   lowerCamelCase__:				Dict        =1.0

   # setable values
   lowerCamelCase__:				List[str]        =None
   lowerCamelCase__:				str        =torch.from_numpy(np.arange(0	,			_lowerCAmelCase).copy().astype(np.intaa))

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



   '''simple docstring'''



   return sample

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



   '''simple docstring'''



   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.""")

   lowerCamelCase__:				Any        =num_inference_steps
   lowerCamelCase__:				Any        =self.config.num_train_timesteps // self.num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # casting to int to avoid issues when num_inference_step is power of 3
   lowerCamelCase__:				str        =(np.arange(0	,			_lowerCAmelCase) * step_ratio).round().copy().astype(np.intaa)
   lowerCamelCase__:				Optional[int]        =torch.from_numpy(_lowerCAmelCase).to(_lowerCAmelCase)
   self.timesteps += self.config.steps_offset

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       torch.FloatTensor	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       torch.FloatTensor	,			UpperCAmelCase_    :       float = 0.0	,			UpperCAmelCase_    :       bool = False	,			UpperCAmelCase_    :       Optional[torch.FloatTensor] = None	,			UpperCAmelCase_    :       bool = True	,			)	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				str        =timestep + self.config.num_train_timesteps // self.num_inference_steps

   # 2. compute alphas, betas
   # change original implementation to exactly match noise levels for analogous forward process
   lowerCamelCase__:				Any        =self.alphas_cumprod[timestep]
   lowerCamelCase__:				Union[str, Any]        =(
       self.alphas_cumprod[prev_timestep]
       if prev_timestep < self.config.num_train_timesteps
       else self.final_alpha_cumprod
   )

   lowerCamelCase__:				Optional[int]        =1 - alpha_prod_t

   # 3. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   if self.config.prediction_type == "epsilon":
     lowerCamelCase__:				List[str]        =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     lowerCamelCase__:				int        =model_output
   elif self.config.prediction_type == "sample":
     lowerCamelCase__:				Any        =model_output
     lowerCamelCase__:				List[Any]        =(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
   elif self.config.prediction_type == "v_prediction":
     lowerCamelCase__:				List[str]        =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
     lowerCamelCase__:				Any        =(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
   else:
     raise ValueError(
         F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
         " `v_prediction`")

   # 4. Clip or threshold "predicted x_0"
   if self.config.clip_sample:
     lowerCamelCase__:				Tuple        =pred_original_sample.clamp(
         -self.config.clip_sample_range	,			self.config.clip_sample_range)

   # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   lowerCamelCase__:				int        =(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

   # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   lowerCamelCase__:				Tuple        =alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

   if not return_dict:
     return (prev_sample, pred_original_sample)
   return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase	,			pred_original_sample=_lowerCAmelCase)







 def __len__(self    :       int)	->int:



   '''simple docstring'''



   return self.config.num_train_timesteps








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()

