

import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


_a	 =     logging.get_logger(__name__)

if is_vision_available():
  import PIL.Image
  from PIL import Image
  from PIL.Image import Image as ImageType
else:
  _a	 =     object

if is_torch_available():
  import torch

if is_soundfile_availble():
  import soundfile as sf






class 							A_				:


     def __init__(							self    :	Tuple    ,						UpperCAmelCase    :	Optional[Any]      )							->			Tuple:
      __lowerCAmelCase:     Optional[int]     			=   value


     def __str__(							self    :	List[str]      )							->			Any:
      return self.to_string()


     def 						UpperCAmelCase						(							self    :	str      )							->			int:
      logger.error(
          'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'      )
      return self._value


     def 						UpperCAmelCase						(							self    :	Any      )							->			str:
      logger.error(
          'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'      )
      return str(self._value      )







class 							A_				(	snake_case__							,			snake_case__      ):


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Optional[Any]:
      return self._value


     def 						UpperCAmelCase						(							self    :	Optional[int]      )							->			Optional[int]:
      return self._value







class 							A_				(	snake_case__							,			snake_case__      ):


     def __init__(							self    :	Union[str, Any]    ,						UpperCAmelCase    :	Any      )							->			str:
      super().__init__(UpperCAmelCase      )

      if not is_vision_available():
       raise ImportError('PIL must be installed in order to handle images.'      )

      __lowerCAmelCase:     str     			=   None
      __lowerCAmelCase:     Optional[Any]     			=   None
      __lowerCAmelCase:     str     			=   None

      if isinstance(UpperCAmelCase    ,						UpperCAmelCase      ):
       __lowerCAmelCase:     Union[str, Any]     			=   value
      elif isinstance(UpperCAmelCase    ,						(str, pathlib.Path)      ):
       __lowerCAmelCase:     Dict     			=   value
      elif isinstance(UpperCAmelCase    ,						torch.Tensor      ):
       __lowerCAmelCase:     int     			=   value
      else:
       raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(UpperCAmelCase      )}'''      )


     def 						UpperCAmelCase						(							self    :	Optional[int]    ,						UpperCAmelCase    :	Dict=None    ,						UpperCAmelCase    :	str=None      )							->			Optional[Any]:
      from IPython.display import Image, display

      display(Image(self.to_string()      )      )


     def 						UpperCAmelCase						(							self    :	int      )							->			Union[str, Any]:
      if self._raw is not None:
       return self._raw

      if self._path is not None:
       __lowerCAmelCase:     List[Any]     			=   Image.open(self._path      )
       return self._raw


     def 						UpperCAmelCase						(							self    :	Optional[Any]      )							->			List[Any]:
      if self._path is not None:
       return self._path

      if self._raw is not None:
       __lowerCAmelCase:     Tuple     			=   tempfile.mkdtemp()
       __lowerCAmelCase:     Optional[Any]     			=   os.path.join(UpperCAmelCase    ,						str(uuid.uuida()      ) + '.png'      )
       self._raw.save(self._path      )

       return self._path

      if self._tensor is not None:
       __lowerCAmelCase:     int     			=   self._tensor.cpu().detach().numpy()

       # There is likely simpler than load into image into save
       __lowerCAmelCase:     Any     			=   Image.fromarray((array * 2_5_5).astype(np.uinta      )      )

       __lowerCAmelCase:     Tuple     			=   tempfile.mkdtemp()
       __lowerCAmelCase:     List[Any]     			=   os.path.join(UpperCAmelCase    ,						str(uuid.uuida()      ) + '.png'      )

       img.save(self._path      )

       return self._path







class 							A_				(	snake_case__      ):


     def __init__(							self    :	Dict    ,						UpperCAmelCase    :	Tuple    ,						UpperCAmelCase    :	List[str]=1_6_0_0_0      )							->			Optional[Any]:
      super().__init__(UpperCAmelCase      )

      if not is_soundfile_availble():
       raise ImportError('soundfile must be installed in order to handle audio.'      )

      __lowerCAmelCase:     List[Any]     			=   None
      __lowerCAmelCase:     Optional[int]     			=   None

      __lowerCAmelCase:     Optional[int]     			=   samplerate

      if isinstance(UpperCAmelCase    ,						(str, pathlib.Path)      ):
       __lowerCAmelCase:     Tuple     			=   value
      elif isinstance(UpperCAmelCase    ,						torch.Tensor      ):
       __lowerCAmelCase:     Any     			=   value
      else:
       raise ValueError(F'''Unsupported audio type: {type(UpperCAmelCase      )}'''      )


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	Any=None    ,						UpperCAmelCase    :	Tuple=None      )							->			Optional[int]:
      from IPython.display import Audio, display

      display(Audio(self.to_string()    ,						rate=self.samplerate      )      )


     def 						UpperCAmelCase						(							self    :	Optional[int]      )							->			Dict:
      if self._tensor is not None:
       return self._tensor

      if self._path is not None:
       __lowerCAmelCase			,							__lowerCAmelCase:     Tuple     			=   sf.read(self._path      )
       __lowerCAmelCase:     List[Any]     			=   torch.tensor(UpperCAmelCase      )
       return self._tensor


     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			Tuple:
      if self._path is not None:
       return self._path

      if self._tensor is not None:
       __lowerCAmelCase:     List[str]     			=   tempfile.mkdtemp()
       __lowerCAmelCase:     Dict     			=   os.path.join(UpperCAmelCase    ,						str(uuid.uuida()      ) + '.wav'      )
       sf.write(self._path    ,						self._tensor    ,						samplerate=self.samplerate      )
       return self._path







_a	 =     {'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
_a	 =     {str: AgentText}

if is_vision_available():
  _a	 =     AgentImage







def _a				( *SCREAMING_SNAKE_CASE	:    Optional[Any]					,      **SCREAMING_SNAKE_CASE	:    Dict	)					->					str:

 """simple docstring"""






 __lowerCAmelCase:     str     			=   [(arg.to_raw() if isinstance(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE	) else arg) for arg in args]
 __lowerCAmelCase:     int     			=   {k: (v.to_raw() if isinstance(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE	) else v) for k, v in kwargs.items()}
 return args, kwargs







def _a				( SCREAMING_SNAKE_CASE	:    Optional[int]					,      SCREAMING_SNAKE_CASE	:    Optional[int]=None	)					->					Dict:

 """simple docstring"""






 if isinstance(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE	):
  __lowerCAmelCase:     Union[str, Any]     			=   {}
  for i, (k, v) in enumerate(outputs.items()	):
   if output_types is not None:
    # If the class has defined outputs, we can map directly according to the class definition
    if output_types[i] in AGENT_TYPE_MAPPING:
     __lowerCAmelCase:     List[str]     			=   AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE	)
    else:
     __lowerCAmelCase:     Optional[Any]     			=   AgentType(SCREAMING_SNAKE_CASE	)

   else:
    # If the class does not have defined output, then we map according to the type
    for _k, _v in INSTANCE_TYPE_MAPPING.items():
     if isinstance(SCREAMING_SNAKE_CASE					,      _k	):
      __lowerCAmelCase:     int     			=   _v(SCREAMING_SNAKE_CASE	)
    if k not in decoded_outputs:
     __lowerCAmelCase:     Optional[Any]     			=   AgentType[v]

 elif isinstance(SCREAMING_SNAKE_CASE					,      (list, tuple)	):
  __lowerCAmelCase:     Tuple     			=   type(SCREAMING_SNAKE_CASE	)()
  for i, v in enumerate(SCREAMING_SNAKE_CASE	):
   if output_types is not None:
    # If the class has defined outputs, we can map directly according to the class definition
    if output_types[i] in AGENT_TYPE_MAPPING:
     decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE	)	)
    else:
     decoded_outputs.append(AgentType(SCREAMING_SNAKE_CASE	)	)
   else:
    # If the class does not have defined output, then we map according to the type
    __lowerCAmelCase:     Union[str, Any]     			=   False
    for _k, _v in INSTANCE_TYPE_MAPPING.items():
     if isinstance(SCREAMING_SNAKE_CASE					,      _k	):
      decoded_outputs.append(_v(SCREAMING_SNAKE_CASE	)	)
      __lowerCAmelCase:     Tuple     			=   True

    if not found:
     decoded_outputs.append(AgentType(SCREAMING_SNAKE_CASE	)	)

 else:
  if output_types[0] in AGENT_TYPE_MAPPING:
   # If the class has defined outputs, we can map directly according to the class definition
   __lowerCAmelCase:     Optional[int]     			=   AGENT_TYPE_MAPPING[output_types[0]](SCREAMING_SNAKE_CASE	)

  else:
   # If the class does not have defined output, then we map according to the type
   for _k, _v in INSTANCE_TYPE_MAPPING.items():
    if isinstance(SCREAMING_SNAKE_CASE					,      _k	):
     return _v(SCREAMING_SNAKE_CASE	)
   return AgentType(SCREAMING_SNAKE_CASE	)

 return decoded_outputs








import math






class 							A_				:


     def __init__(							self    :	Any    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	Any      )							->			Any:
      __lowerCAmelCase:     Optional[Any]     			=   x
      __lowerCAmelCase:     Any     			=   y
      __lowerCAmelCase:     Tuple     			=   step_size
      __lowerCAmelCase:     Dict     			=   function_to_optimize


     def 						UpperCAmelCase						(							self    :	Dict      )							->			int:
      return self.function(self.x    ,						self.y      )


     def 						UpperCAmelCase						(							self    :	str      )							->			int:
      __lowerCAmelCase:     Union[str, Any]     			=   self.step_size
      return [
          SearchProblem(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						self.function      )
          for x, y in (
              (self.x - step_size, self.y - step_size),
              (self.x - step_size, self.y),
              (self.x - step_size, self.y + step_size),
              (self.x, self.y - step_size),
              (self.x, self.y + step_size),
              (self.x + step_size, self.y - step_size),
              (self.x + step_size, self.y),
              (self.x + step_size, self.y + step_size),
          )
      ]


     def __hash__(							self    :	Optional[Any]      )							->			int:
      return hash(str(self      )      )


     def __eq__(							self    :	Dict    ,						UpperCAmelCase    :	Tuple      )							->			Any:
      if isinstance(UpperCAmelCase    ,						UpperCAmelCase      ):
       return hash(str(self      )      ) == hash(str(UpperCAmelCase      )      )
      return False


     def __str__(							self    :	Optional[Any]      )							->			Dict:
      return F'''x: {self.x} y: {self.y}'''







def _a				( SCREAMING_SNAKE_CASE	:    List[str]					,      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	:    int = 1_00_00					,      )					->					SearchProblem:

 """simple docstring"""






 __lowerCAmelCase:     Dict     			=   search_prob
 __lowerCAmelCase:     str     			=   []  # list to store the current score at each iteration
 __lowerCAmelCase:     Optional[int]     			=   0
 __lowerCAmelCase:     int     			=   False
 __lowerCAmelCase:     Any     			=   set()
 while not solution_found and iterations < max_iter:
  visited.add(SCREAMING_SNAKE_CASE	)
  iterations += 1
  __lowerCAmelCase:     int     			=   current_state.score()
  scores.append(SCREAMING_SNAKE_CASE	)
  __lowerCAmelCase:     Any     			=   current_state.get_neighbors()
  __lowerCAmelCase:     Tuple     			=   -math.inf
  __lowerCAmelCase:     Any     			=   math.inf
  __lowerCAmelCase:     Dict     			=   None  # to hold the next best neighbor
  for neighbor in neighbors:
   if neighbor in visited:
    continue  # do not want to visit the same state again
   if (
       neighbor.x > max_x
       or neighbor.x < min_x
       or neighbor.y > max_y
       or neighbor.y < min_y
   ):
    continue  # neighbor outside our bounds
   __lowerCAmelCase:     List[str]     			=   neighbor.score() - current_score
   if find_max:  # finding max
    # going to direction with greatest ascent
    if change > max_change and change > 0:
     __lowerCAmelCase:     int     			=   change
     __lowerCAmelCase:     int     			=   neighbor
   else:  # finding min
    # to direction with greatest descent
    if change < min_change and change < 0:
     __lowerCAmelCase:     str     			=   change
     __lowerCAmelCase:     str     			=   neighbor
  if next_state is not None:
   # we found at least one neighbor which improved the current state
   __lowerCAmelCase:     Optional[Any]     			=   next_state
  else:
   # since we have no neighbor that improves the solution we stop the search
   __lowerCAmelCase:     Dict     			=   True

 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 current_state


if __name__ == "__main__":
  import doctest

  doctest.testmod()







  def _a				( SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    List[str]	)					->					Optional[Any]:

   """simple docstring"""






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

  # starting the problem with initial coordinates (3, 4)
  _a	 =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  _a	 =     hill_climbing(prob, find_max=False)
  print(
      '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
      f"{local_min.score()}"
  )

  # starting the problem with initial coordinates (12, 47)
  _a	 =     SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
  _a	 =     hill_climbing(
      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()}"
  )







  def _a				( SCREAMING_SNAKE_CASE	:    Tuple					,      SCREAMING_SNAKE_CASE	:    List[Any]	)					->					Dict:

   """simple docstring"""






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

  _a	 =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  _a	 =     hill_climbing(prob, find_max=True)



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





