
'''simple docstring'''







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


lowercase__     :  Optional[Any]    					=			logging.get_logger(__name__)

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

if is_torch_available():
      import torch

if is_soundfile_availble():
      import soundfile as sf

class       lowerCamelCase_							:






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			value


   def __str__(  self ):
       return self.to_string()


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

class       lowerCamelCase_							(	__UpperCAmelCase					,    __UpperCAmelCase  ):






   '''simple docstring'''

   def 	_UpperCamelCase       (  self ):
       return self._value







   def 	_UpperCamelCase       (  self ):
       return self._value

class       lowerCamelCase_							(	__UpperCAmelCase					,    __UpperCAmelCase  ):






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase ):
       super().__init__(lowerCamelCase__ )

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

       UpperCamelCase__      =			None
       UpperCamelCase__      =			None
       UpperCamelCase__      =			None

       if isinstance(lowerCamelCase__     ,						lowerCamelCase__ ):
           UpperCamelCase__      =			value
       elif isinstance(lowerCamelCase__     ,						(str, pathlib.Path) ):
           UpperCamelCase__      =			value
       elif isinstance(lowerCamelCase__     ,						torch.Tensor ):
           UpperCamelCase__      =			value
       else:
           raise ValueError(f'Unsupported type for {self.__class__.__name__}: {type(lowerCamelCase__ )}' )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None ):
       from IPython.display import Image, display

       display(Image(self.to_string() ) )


   def 	_UpperCamelCase       (  self ):



       if self._raw is not None:
           return self._raw

       if self._path is not None:
           UpperCamelCase__      =			Image.open(self._path )
           return self._raw







   def 	_UpperCamelCase       (  self ):



       if self._path is not None:
           return self._path

       if self._raw is not None:
           UpperCamelCase__      =			tempfile.mkdtemp()
           UpperCamelCase__      =			os.path.join(lowerCamelCase__     ,						str(uuid.uuida() ) + ".png" )
           self._raw.save(self._path )

           return self._path

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

           # There is likely simpler than load into image into save
           UpperCamelCase__      =			Image.fromarray((array * 2_55).astype(np.uinta ) )

           UpperCamelCase__      =			tempfile.mkdtemp()
           UpperCamelCase__      =			os.path.join(lowerCamelCase__     ,						str(uuid.uuida() ) + ".png" )

           img.save(self._path )

           return self._path

class       lowerCamelCase_							(	__UpperCAmelCase  ):






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase=1_60_00 ):
       super().__init__(lowerCamelCase__ )

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

       UpperCamelCase__      =			None
       UpperCamelCase__      =			None

       UpperCamelCase__      =			samplerate

       if isinstance(lowerCamelCase__     ,						(str, pathlib.Path) ):
           UpperCamelCase__      =			value
       elif isinstance(lowerCamelCase__     ,						torch.Tensor ):
           UpperCamelCase__      =			value
       else:
           raise ValueError(f'Unsupported audio type: {type(lowerCamelCase__ )}' )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None ):
       from IPython.display import Audio, display

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


   def 	_UpperCamelCase       (  self ):



       if self._tensor is not None:
           return self._tensor

       if self._path is not None:
           UpperCamelCase__    ,  UpperCamelCase__      =			sf.read(self._path )
           UpperCamelCase__      =			torch.tensor(lowerCamelCase__ )
           return self._tensor







   def 	_UpperCamelCase       (  self ):



       if self._path is not None:
           return self._path

       if self._tensor is not None:
           UpperCamelCase__      =			tempfile.mkdtemp()
           UpperCamelCase__      =			os.path.join(lowerCamelCase__     ,						str(uuid.uuida() ) + ".wav" )
           sf.write(self._path     ,						self._tensor     ,						samplerate=self.samplerate )
           return self._path

lowercase__     :  Dict    					=			{'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
lowercase__     :  Any    					=			{str: AgentText}

if is_vision_available():
      lowercase__     :  Optional[Any]    					=			AgentImage





def 						snake_case__ (*lowerCAmelCase_					,  **lowerCAmelCase_	):
    UpperCamelCase__      =			[(arg.to_raw() if isinstance(lowercase__					,  lowercase__	) else arg) for arg in args]
    UpperCamelCase__      =			{k: (v.to_raw() if isinstance(lowercase__					,  lowercase__	) else v) for k, v in kwargs.items()}
    return args, kwargs





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_=None	):
    if isinstance(lowercase__					,  lowercase__	):
        UpperCamelCase__      =			{}
        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:
                    UpperCamelCase__      =			AGENT_TYPE_MAPPING[output_types[i]](lowercase__	)
                else:
                    UpperCamelCase__      =			AgentType(lowercase__	)

            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(lowercase__					,  _k	):
                        UpperCamelCase__      =			_v(lowercase__	)
                if k not in decoded_outputs:
                    UpperCamelCase__      =			AgentType[v]

    elif isinstance(lowercase__					,  (list, tuple)	):
        UpperCamelCase__      =			type(lowercase__	)()
        for i, v in enumerate(lowercase__	):
            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]](lowercase__	)	)
                else:
                    decoded_outputs.append(AgentType(lowercase__	)	)
            else:
                # If the class does not have defined output, then we map according to the type
                UpperCamelCase__      =			False
                for _k, _v in INSTANCE_TYPE_MAPPING.items():
                    if isinstance(lowercase__					,  _k	):
                        decoded_outputs.append(_v(lowercase__	)	)
                        UpperCamelCase__      =			True

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

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

        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(lowercase__					,  _k	):
                    return _v(lowercase__	)
            return AgentType(lowercase__	)

    return decoded_outputs









'''simple docstring'''







import heapq





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			[(0, start)]  # cost from start node,end node
    UpperCamelCase__      =			set()
    while heap:
        ((UpperCamelCase__)    ,  (UpperCamelCase__))      =			heapq.heappop(lowerCAmelCase_	)
        if u in visited:
            continue
        visited.add(lowerCAmelCase_	)
        if u == end:
            return cost
        for v, c in graph[u]:
            if v in visited:
                continue
            UpperCamelCase__      =			cost + c
            heapq.heappush(lowerCAmelCase_					,  (next_item, v)	)
    return -1


lowercase__     :  Any    					=			{
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
lowercase__     :  Optional[int]    					=			{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
lowercase__     :  Union[str, Any]    					=			{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

lowercase__     :  Union[str, Any]    					=			dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

lowercase__     :  Optional[int]    					=			dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

lowercase__     :  Any    					=			dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
      import doctest

      doctest.testmod()




