



"""simple docstring"""
import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def      __magic_name__				(							lowercase							,			lowercase = True							,			lowercase = math.inf							,			lowercase = -math.inf							,			lowercase = math.inf							,			lowercase = -math.inf							,			lowercase = False							,			lowercase = 100							,			lowercase = 0.01							,			lowercase = 1							,			):
   SCREAMING_SNAKE_CASE_: int     			=False
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=search_prob
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=start_temperate
   SCREAMING_SNAKE_CASE_: List[str]     			=[]
   SCREAMING_SNAKE_CASE_: str     			=0
   SCREAMING_SNAKE_CASE_: Optional[int]     			=None

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








"""simple docstring"""
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase     =			{"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			["""DPTFeatureExtractor"""]
   _UpperCAmelCase     =			["""DPTImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DPTForDepthEstimation""",
       """DPTForSemanticSegmentation""",
       """DPTModel""",
       """DPTPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_dpt import DPTFeatureExtractor
      from .image_processing_dpt import DPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dpt import (
          DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPTForDepthEstimation,
          DPTForSemanticSegmentation,
          DPTModel,
          DPTPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



