



import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
lowerCamelCase__    :		Any          =   logging.getLogger(__name__)

def 		UpperCamelCase	(      lowercase_   )		->	Any:


  '''simple docstring'''






  lowercase__  :   Dict    		=      git.Repo(search_parent_directories=__SCREAMING_SNAKE_CASE   )
  lowercase__  :   Optional[int]    		=      {
      "repo_id": str(__SCREAMING_SNAKE_CASE   ),
      "repo_sha": str(repo.head.object.hexsha   ),
      "repo_branch": str(repo.active_branch   ),
  }

  with open(os.path.join(__SCREAMING_SNAKE_CASE , """git_log.json"""   ) , """w"""   ) as f:
    json.dump(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , indent=4   )

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


  '''simple docstring'''






  if params.n_gpu <= 0:
    lowercase__  :   int    		=      0
    lowercase__  :   int    		=      -1
    lowercase__  :   str    		=      True
    lowercase__  :   Union[str, Any]    		=      False
    return

  assert torch.cuda.is_available()

  logger.info("""Initializing GPUs"""   )
  if params.n_gpu > 1:
    assert params.local_rank != -1

    lowercase__  :   Dict    		=      int(os.environ["""WORLD_SIZE"""]   )
    lowercase__  :   int    		=      int(os.environ["""N_GPU_NODE"""]   )
    lowercase__  :   Optional[Any]    		=      int(os.environ["""RANK"""]   )

    # number of nodes / node ID
    lowercase__  :   Optional[Any]    		=      params.world_size // params.n_gpu_per_node
    lowercase__  :   List[Any]    		=      params.global_rank // params.n_gpu_per_node
    lowercase__  :   List[str]    		=      True

    assert params.n_nodes == int(os.environ["""N_NODES"""]   )
    assert params.node_id == int(os.environ["""NODE_RANK"""]   )

  # local job (single GPU)
  else:
    assert params.local_rank == -1

    lowercase__  :   int    		=      1
    lowercase__  :   List[str]    		=      0
    lowercase__  :   List[Any]    		=      0
    lowercase__  :   Optional[Any]    		=      0
    lowercase__  :   Dict    		=      1
    lowercase__  :   str    		=      1
    lowercase__  :   str    		=      False

  # sanity checks
  assert params.n_nodes >= 1
  assert 0 <= params.node_id < params.n_nodes
  assert 0 <= params.local_rank <= params.global_rank < params.world_size
  assert params.world_size == params.n_nodes * params.n_gpu_per_node

  # define whether this is the master process / if we are in multi-node distributed mode
  lowercase__  :   int    		=      params.node_id == 0 and params.local_rank == 0
  lowercase__  :   Optional[Any]    		=      params.n_nodes > 1

  # summary
  lowercase__  :   Tuple    		=      F'--- Global rank: {params.global_rank} - '
  logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes   )
  logger.info(PREFIX + """Node ID        : %i""" % params.node_id   )
  logger.info(PREFIX + """Local rank     : %i""" % params.local_rank   )
  logger.info(PREFIX + """World size     : %i""" % params.world_size   )
  logger.info(PREFIX + """GPUs per node  : %i""" % params.n_gpu_per_node   )
  logger.info(PREFIX + """Master         : %s""" % str(params.is_master   )   )
  logger.info(PREFIX + """Multi-node     : %s""" % str(params.multi_node   )   )
  logger.info(PREFIX + """Multi-GPU      : %s""" % str(params.multi_gpu   )   )
  logger.info(PREFIX + """Hostname       : %s""" % socket.gethostname()   )

  # set GPU device
  torch.cuda.set_device(params.local_rank   )

  # initialize multi-GPU
  if params.multi_gpu:
    logger.info("""Initializing PyTorch distributed"""   )
    torch.distributed.init_process_group(
        init_method="""env://""" , backend="""nccl""" , )



def 		UpperCamelCase	(      lowercase_   )		->	str:


  '''simple docstring'''






  np.random.seed(args.seed   )
  torch.manual_seed(args.seed   )
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed   )








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()}'''
     )






