
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       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     torch.FloatTensor
 __lowerCAmelCase  :     torch.FloatTensor
 __lowerCAmelCase  :     Optional[torch.FloatTensor]        =			None



class       _snake_case   (	UpperCAmelCase_  ,						UpperCAmelCase_							):
 __lowerCAmelCase  :     Union[str, Any]        =			2


 @register_to_config
 def __init__(					self					,  SCREAMING_SNAKE_CASE_ = 0.0_2					,  SCREAMING_SNAKE_CASE_ = 1_00					,  SCREAMING_SNAKE_CASE_ = 1.0_0_7					,  SCREAMING_SNAKE_CASE_ = 80					,  SCREAMING_SNAKE_CASE_ = 0.0_5					,  SCREAMING_SNAKE_CASE_ = 50					,  ):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      sigma_max

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


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   return sample


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




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


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




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

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

   return sample_hat, sigma_hat


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = True					,  ):


   '''simple docstring'''




   lowercase__  :   str    		=      sample_hat + sigma_hat * model_output
   lowercase__  :   str    		=      (sample_hat - pred_original_sample) / sigma_hat
   lowercase__  :   Optional[Any]    		=      sample_hat + (sigma_prev - sigma_hat) * derivative

   if not return_dict:
     return (sample_prev, derivative)

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


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_ = True					,  ):


   '''simple docstring'''




   lowercase__  :   str    		=      sample_prev + sigma_prev * model_output
   lowercase__  :   Dict    		=      (sample_prev - pred_original_sample) / sigma_prev
   lowercase__  :   Optional[int]    		=      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=SCREAMING_SNAKE_CASE_					,  derivative=SCREAMING_SNAKE_CASE_					,  pred_original_sample=SCREAMING_SNAKE_CASE_)



 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   raise NotImplementedError()








import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def 		UpperCamelCase	(      lowercase_ , lowercase_ = True , lowercase_ = math.inf , lowercase_ = -math.inf , lowercase_ = math.inf , lowercase_ = -math.inf , lowercase_ = False , lowercase_ = 1_00 , lowercase_ = 0.01 , lowercase_ = 1 , )		->	Any:


  '''simple docstring'''






  lowercase__  :   Any    		=      False
  lowercase__  :   Union[str, Any]    		=      search_prob
  lowercase__  :   int    		=      start_temperate
  lowercase__  :   Union[str, Any]    		=      []
  lowercase__  :   List[str]    		=      0
  lowercase__  :   Optional[int]    		=      None

  while not search_end:
    lowercase__  :   int    		=      current_state.score()
    if best_state is None or current_score > best_state.score():
      lowercase__  :   str    		=      current_state
    scores.append(lowercase_   )
    iterations += 1
    lowercase__  :   Union[str, Any]    		=      None
    lowercase__  :   int    		=      current_state.get_neighbors()
    while (
        next_state is None and neighbors
    ):  # till we do not find a neighbor that we can move to
      lowercase__  :   List[str]    		=      random.randint(0 , len(lowercase_   ) - 1   )  # picking a random neighbor
      lowercase__  :   Optional[int]    		=      neighbors.pop(lowercase_   )
      lowercase__  :   Dict    		=      picked_neighbor.score() - current_score

      if (
          picked_neighbor.x > max_x
          or picked_neighbor.x < min_x
          or picked_neighbor.y > max_y
          or picked_neighbor.y < min_y
      ):
        continue  # neighbor outside our bounds

      if not find_max:
        lowercase__  :   Any    		=      change * -1  # in case we are finding minimum
      if change > 0:  # improves the solution
        lowercase__  :   Tuple    		=      picked_neighbor
      else:
        lowercase__  :   Union[str, Any]    		=      (math.e) ** (
            change / current_temp
        )  # probability generation function
        if random.random() < probability:  # random number within probability
          lowercase__  :   Optional[int]    		=      picked_neighbor
    lowercase__  :   Dict    		=      current_temp - (current_temp * rate_of_decrease)

    if current_temp < threshold_temp or next_state is None:
      # temperature below threshold, or could not find a suitable neighbor
      lowercase__  :   Optional[Any]    		=      True
    else:
      lowercase__  :   List[Any]    		=      next_state

  if visualization:
    from matplotlib import pyplot as plt

    plt.plot(range(lowercase_   ) , lowercase_   )
    plt.xlabel("""Iterations"""   )
    plt.ylabel("""Function values"""   )
    plt.show()
  return best_state


if __name__ == "__main__":

     def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	str:


       '''simple docstring'''






       return (x**2) + (y**2)

     # starting the problem with initial coordinates (12, 47)
     lowerCamelCase__    :		str          =   SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		List[Any]          =   simulated_annealing(
         prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
     )
     print(
         """The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
         f'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
     )

     # starting the problem with initial coordinates (12, 47)
     lowerCamelCase__    :		List[Any]          =   SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		Optional[int]          =   simulated_annealing(
         prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
     )
     print(
         """The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
         f'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
     )

     def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Union[str, Any]:


       '''simple docstring'''






       return (3 * x**2) - (6 * y)

     lowerCamelCase__    :		Tuple          =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		Union[str, Any]          =   simulated_annealing(prob, find_max=False, visualization=True)
     print(
         """The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
         f'''{local_min.score()}'''
     )

     lowerCamelCase__    :		Dict          =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		Tuple          =   simulated_annealing(prob, find_max=True, visualization=True)



     print(
         """The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
         f'''{local_min.score()}'''
     )






