



import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Union[str, Any]     ,				_SCREAMING_SNAKE_CASE   :	bool = True     ,				_SCREAMING_SNAKE_CASE   :	float = math.inf     ,				_SCREAMING_SNAKE_CASE   :	float = -math.inf     ,				_SCREAMING_SNAKE_CASE   :	float = math.inf     ,				_SCREAMING_SNAKE_CASE   :	float = -math.inf     ,				_SCREAMING_SNAKE_CASE   :	bool = False     ,				_SCREAMING_SNAKE_CASE   :	float = 1_0_0     ,				_SCREAMING_SNAKE_CASE   :	float = 0.01     ,				_SCREAMING_SNAKE_CASE   :	float = 1     ,				)->Any:
      _lowerCAmelCase 			=  False
      _lowerCAmelCase 			=  search_prob
      _lowerCAmelCase 			=  start_temperate
      _lowerCAmelCase 			=  []
      _lowerCAmelCase 			=  0
      _lowerCAmelCase 			=  None

      while not search_end:
            _lowerCAmelCase 			=  current_state.score()
            if best_state is None or current_score > best_state.score():
                  _lowerCAmelCase 			=  current_state
            scores.append(_SCREAMING_SNAKE_CASE				)
            iterations += 1
            _lowerCAmelCase 			=  None
            _lowerCAmelCase 			=  current_state.get_neighbors()
            while (
                next_state is None and neighbors
            ):  # till we do not find a neighbor that we can move to
                  _lowerCAmelCase 			=  random.randint(0     ,				len(_SCREAMING_SNAKE_CASE				) - 1				)  # picking a random neighbor
                  _lowerCAmelCase 			=  neighbors.pop(_SCREAMING_SNAKE_CASE				)
                  _lowerCAmelCase 			=  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:
                        _lowerCAmelCase 			=  change * -1  # in case we are finding minimum
                  if change > 0:  # improves the solution
                        _lowerCAmelCase 			=  picked_neighbor
                  else:
                        _lowerCAmelCase 			=  (math.e) ** (
                            change / current_temp
                        )  # probability generation function
                        if random.random() < probability:  # random number within probability
                              _lowerCAmelCase 			=  picked_neighbor
            _lowerCAmelCase 			=  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
                  _lowerCAmelCase 			=  True
            else:
                  _lowerCAmelCase 			=  next_state

      if visualization:
            from matplotlib import pyplot as plt

            plt.plot(range(_SCREAMING_SNAKE_CASE				)     ,				_SCREAMING_SNAKE_CASE				)
            plt.xlabel('''Iterations'''				)
            plt.ylabel('''Function values'''				)
            plt.show()
      return best_state


if __name__ == "__main__":
    def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	Dict				)->Any:
          return (x**2) + (y**2)

    # starting the problem with initial coordinates (12, 47)
    UpperCAmelCase_									=							SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
    UpperCAmelCase_									=							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)
    UpperCAmelCase_									=							SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
    UpperCAmelCase_									=							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__		(				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	List[str]				)->Optional[int]:
          return (3 * x**2) - (6 * y)

    UpperCAmelCase_									=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    UpperCAmelCase_									=							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()}"""
    )

    UpperCAmelCase_									=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    UpperCAmelCase_									=							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()}"""
    )











import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available
def 							UpperCAmelCase__		(				*_SCREAMING_SNAKE_CASE   :	Tuple				)->List[Any]:
      if not isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				):
            _lowerCAmelCase 			=  list(_SCREAMING_SNAKE_CASE				)
      for i in range(len(_SCREAMING_SNAKE_CASE				)				):
            _lowerCAmelCase 			=  None
      gc.collect()
      if is_xpu_available():
            torch.xpu.empty_cache()
      elif is_npu_available():
            torch.npu.empty_cache()
      else:
            torch.cuda.empty_cache()
      return objects
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Exception				)->bool:
      _lowerCAmelCase 			=  [
          '''CUDA out of memory.''',  # CUDA OOM
          '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
          '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
      ]
      if isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				) and len(exception.args				) == 1:
            return any(err in exception.args[0] for err in _statements				)
      return False


def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	callable = None     ,				_SCREAMING_SNAKE_CASE   :	int = 1_2_8				)->Optional[int]:
      if function is None:
            return functools.partial(_SCREAMING_SNAKE_CASE     ,				starting_batch_size=_SCREAMING_SNAKE_CASE				)

      _lowerCAmelCase 			=  starting_batch_size

      def decorator(*_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				**_SCREAMING_SNAKE_CASE   :	Optional[Any]				):
            nonlocal batch_size
            gc.collect()
            if is_xpu_available():
                  torch.xpu.empty_cache()
            elif is_npu_available():
                  torch.npu.empty_cache()
            else:
                  torch.cuda.empty_cache()
            _lowerCAmelCase 			=  list(inspect.signature(_SCREAMING_SNAKE_CASE				).parameters.keys()				)
            # Guard against user error
            if len(_SCREAMING_SNAKE_CASE				) < (len(_SCREAMING_SNAKE_CASE				) + 1):
                  _lowerCAmelCase 			=  ''', '''.join([f'''{arg}={value}''' for arg, value in zip(params[1:]     ,				args[1:]				)]				)
                  raise TypeError(
                      f'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
                      f'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`'''				)
            while True:
                  if batch_size == 0:
                        raise RuntimeError('''No executable batch size found, reached zero.'''				)
                  try:
                        return function(_SCREAMING_SNAKE_CASE     ,				*_SCREAMING_SNAKE_CASE     ,				**_SCREAMING_SNAKE_CASE				)
                  except Exception as e:
                        if should_reduce_batch_size(_SCREAMING_SNAKE_CASE				):
                              gc.collect()
                              if is_xpu_available():
                                    torch.xpu.empty_cache()
                              elif is_npu_available():
                                    torch.npu.empty_cache()
                              else:
                                    torch.cuda.empty_cache()
                              batch_size //= 2
                        else:
                              raise

      return decorator