



'''simple docstring'''





import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase = True     ,   _lowerCAmelCase = math.inf     ,   _lowerCAmelCase = -math.inf     ,   _lowerCAmelCase = math.inf     ,   _lowerCAmelCase = -math.inf     ,   _lowerCAmelCase = False     ,   _lowerCAmelCase = 100     ,   _lowerCAmelCase = 0.01     ,   _lowerCAmelCase = 1     ,   )		-> Any:
      snake_case__  :    Union[str, Any]    						=      False
      snake_case__  :    Optional[Any]    						=      search_prob
      snake_case__  :    Dict    						=      start_temperate
      snake_case__  :    Optional[int]    						=      []
      snake_case__  :    Optional[Any]    						=      0
      snake_case__  :    Union[str, Any]    						=      None

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

      if visualization:
            from matplotlib import pyplot as plt

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


if __name__ == "__main__":





       def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
             return (x**2) + (y**2)

       # starting the problem with initial coordinates (12, 47)
       __a          =		SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __a          =		simulated_annealing(
           prob, find_max=False, max_x=100, min_x=5, max_y=50, 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)
       __a          =		SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __a          =		simulated_annealing(
           prob, find_max=True, max_x=100, min_x=5, max_y=50, 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 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Union[str, Any]:
             return (3 * x**2) - (6 * y)

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

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




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__   	=							{
    '''configuration_efficientnet''': [
        '''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientNetConfig''',
        '''EfficientNetOnnxConfig''',
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							['''EfficientNetImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__   	=							[
      '''EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''EfficientNetForImageClassification''',
      '''EfficientNetModel''',
      '''EfficientNetPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_efficientnet import (
      EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
      EfficientNetConfig,
      EfficientNetOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_efficientnet import EfficientNetImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_efficientnet import (
        EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        EfficientNetForImageClassification,
        EfficientNetModel,
        EfficientNetPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__   	=							_LazyModule(__name__, globals()['''__file__'''], _import_structure)

