code: 
  
  
  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 				__SCREAMING_SNAKE_CASE   :
  
  
  						def __init__(							self							,						SCREAMING_SNAKE_CASE__		):
  												lowercase			:   List[str]					   =		value
  
  
  						def __str__(							self		):
  												return self.to_string()
  
  
  						def        __lowerCamelCase			(							self		):
  												logger.error(
  												    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''		)
  												return self._value
  
  
  
  
  
  
  						def        __lowerCamelCase			(							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 				__SCREAMING_SNAKE_CASE   (			A__  ,						A__   ):
  
  
  						def        __lowerCamelCase			(							self		):
  												return self._value
  
  
  
  
  
  
  						def        __lowerCamelCase			(							self		):
  												return self._value
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   (			A__  ,						A__   ):
  
  
  						def __init__(							self							,						SCREAMING_SNAKE_CASE__		):
  												super().__init__(SCREAMING_SNAKE_CASE__		)
  
  												if not is_vision_available():
  																		raise ImportError('''PIL must be installed in order to handle images.'''		)
  
  												lowercase			:   Tuple					   =		None
  												lowercase			:   str					   =		None
  												lowercase			:   Dict					   =		None
  
  												if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
  																		lowercase			:   Tuple					   =		value
  												elif isinstance(SCREAMING_SNAKE_CASE__							,						(str, pathlib.Path)		):
  																		lowercase			:   List[str]					   =		value
  												elif isinstance(SCREAMING_SNAKE_CASE__							,						torch.Tensor		):
  																		lowercase			:   Dict					   =		value
  												else:
  																		raise ValueError(f"""Unsupported type for {self.__class__.__name__}: {type(SCREAMING_SNAKE_CASE__		)}"""		)
  
  
  						def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__=None							,						SCREAMING_SNAKE_CASE__=None		):
  												from IPython.display import Image, display
  
  												display(Image(self.to_string()		)		)
  
  
  						def        __lowerCamelCase			(							self		):
  												if self._raw is not None:
  																		return self._raw
  
  												if self._path is not None:
  																		lowercase			:   List[Any]					   =		Image.open(self._path		)
  																		return self._raw
  
  
  
  
  
  
  						def        __lowerCamelCase			(							self		):
  												if self._path is not None:
  																		return self._path
  
  												if self._raw is not None:
  																		lowercase			:   Tuple					   =		tempfile.mkdtemp()
  																		lowercase			:   List[str]					   =		os.path.join(SCREAMING_SNAKE_CASE__							,						str(uuid.uuida()		) + '''.png'''		)
  																		self._raw.save(self._path		)
  
  																		return self._path
  
  												if self._tensor is not None:
  																		lowercase			:   Optional[int]					   =		self._tensor.cpu().detach().numpy()
  
  																		# There is likely simpler than load into image into save
  																		lowercase			:   Tuple					   =		Image.fromarray((array * 255).astype(np.uinta		)		)
  
  																		lowercase			:   Union[str, Any]					   =		tempfile.mkdtemp()
  																		lowercase			:   Tuple					   =		os.path.join(SCREAMING_SNAKE_CASE__							,						str(uuid.uuida()		) + '''.png'''		)
  
  																		img.save(self._path		)
  
  																		return self._path
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   (			A__   ):
  
  
  						def __init__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__=16000		):
  												super().__init__(SCREAMING_SNAKE_CASE__		)
  
  												if not is_soundfile_availble():
  																		raise ImportError('''soundfile must be installed in order to handle audio.'''		)
  
  												lowercase			:   Union[str, Any]					   =		None
  												lowercase			:   List[Any]					   =		None
  
  												lowercase			:   Union[str, Any]					   =		samplerate
  
  												if isinstance(SCREAMING_SNAKE_CASE__							,						(str, pathlib.Path)		):
  																		lowercase			:   str					   =		value
  												elif isinstance(SCREAMING_SNAKE_CASE__							,						torch.Tensor		):
  																		lowercase			:   Dict					   =		value
  												else:
  																		raise ValueError(f"""Unsupported audio type: {type(SCREAMING_SNAKE_CASE__		)}"""		)
  
  
  						def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__=None							,						SCREAMING_SNAKE_CASE__=None		):
  												from IPython.display import Audio, display
  
  												display(Audio(self.to_string()							,						rate=self.samplerate		)		)
  
  
  						def        __lowerCamelCase			(							self		):
  												if self._tensor is not None:
  																		return self._tensor
  
  												if self._path is not None:
  																		lowercase			:   Dict					   =		sf.read(self._path		)
  																		lowercase			:   Dict					   =		torch.tensor(SCREAMING_SNAKE_CASE__		)
  																		return self._tensor
  
  
  
  
  
  
  						def        __lowerCamelCase			(							self		):
  												if self._path is not None:
  																		return self._path
  
  												if self._tensor is not None:
  																		lowercase			:   Optional[Any]					   =		tempfile.mkdtemp()
  																		lowercase			:   int					   =		os.path.join(SCREAMING_SNAKE_CASE__							,						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 			__lowercase	(						*_UpperCamelCase,    **_UpperCamelCase     )    ->str:
  
  
  
  
  
  
  
  						"""simple docstring"""
  
  						lowercase			:   List[Any]					   =		[(arg.to_raw() if isinstance(_UpperCamelCase,    _UpperCamelCase     ) else arg) for arg in args]
  						lowercase			:   str					   =		{k: (v.to_raw() if isinstance(_UpperCamelCase,    _UpperCamelCase     ) else v) for k, v in kwargs.items()}
  						return args, kwargs
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase=None     )    ->Optional[int]:
  
  
  
  
  
  
  
  						"""simple docstring"""
  
  						if isinstance(_UpperCamelCase,    _UpperCamelCase     ):
  												lowercase			:   Tuple					   =		{}
  												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:
  																														lowercase			:   Optional[int]					   =		AGENT_TYPE_MAPPING[output_types[i]](_UpperCamelCase     )
  																								else:
  																														lowercase			:   str					   =		AgentType(_UpperCamelCase     )
  
  																		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(_UpperCamelCase,    _k     ):
  																																				lowercase			:   List[Any]					   =		_v(_UpperCamelCase     )
  																								if k not in decoded_outputs:
  																														lowercase			:   Optional[Any]					   =		AgentType[v]
  
  						elif isinstance(_UpperCamelCase,    (list, tuple)     ):
  												lowercase			:   str					   =		type(_UpperCamelCase     )()
  												for i, v in enumerate(_UpperCamelCase     ):
  																		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]](_UpperCamelCase     )     )
  																								else:
  																														decoded_outputs.append(AgentType(_UpperCamelCase     )     )
  																		else:
  																								# If the class does not have defined output, then we map according to the type
  																								lowercase			:   Union[str, Any]					   =		False
  																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
  																														if isinstance(_UpperCamelCase,    _k     ):
  																																				decoded_outputs.append(_v(_UpperCamelCase     )     )
  																																				lowercase			:   Dict					   =		True
  
  																								if not found:
  																														decoded_outputs.append(AgentType(_UpperCamelCase     )     )
  
  						else:
  												if output_types[0] in AGENT_TYPE_MAPPING:
  																		# If the class has defined outputs, we can map directly according to the class definition
  																		lowercase			:   Any					   =		AGENT_TYPE_MAPPING[output_types[0]](_UpperCamelCase     )
  
  												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(_UpperCamelCase,    _k     ):
  																														return _v(_UpperCamelCase     )
  																		return AgentType(_UpperCamelCase     )
  
  						return decoded_outputs
  
  
style_context: 
  
  
  import numpy as np
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[Any]					   =		np.array(_UpperCamelCase     )
        lowercase			:   Dict					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   List[Any]					   =		abs(predict - actual     )
        lowercase			:   int					   =		difference.mean()
  
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Tuple					   =		np.array(_UpperCamelCase     )
        lowercase			:   str					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   List[str]					   =		predict - actual
        lowercase			:   Tuple					   =		np.square(_UpperCamelCase     )
  
        lowercase			:   Union[str, Any]					   =		square_diff.mean()
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->str:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Union[str, Any]					   =		np.array(_UpperCamelCase     )
        lowercase			:   Optional[Any]					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   Tuple					   =		predict - actual
        lowercase			:   Dict					   =		np.square(_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		square_diff.mean()
        lowercase			:   Union[str, Any]					   =		np.sqrt(_UpperCamelCase     )
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[str]					   =		np.array(_UpperCamelCase     )
        lowercase			:   List[Any]					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   Dict					   =		np.log(predict + 1     )
        lowercase			:   List[str]					   =		np.log(actual + 1     )
  
        lowercase			:   int					   =		log_predict - log_actual
        lowercase			:   Tuple					   =		np.square(_UpperCamelCase     )
        lowercase			:   List[Any]					   =		square_diff.mean()
  
        lowercase			:   int					   =		np.sqrt(_UpperCamelCase     )
  
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[Any]					   =		np.array(_UpperCamelCase     )
        lowercase			:   Optional[int]					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   Optional[int]					   =		predict - actual
        lowercase			:   Optional[int]					   =		np.sum(_UpperCamelCase     ) / len(_UpperCamelCase     )
        lowercase			:   Dict					   =		np.sum(_UpperCamelCase     ) / len(_UpperCamelCase     )
        # print(numerator, denumerator)
        lowercase			:   int					   =		float(_UpperCamelCase     ) / denumerator * 100
  
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        return np.mean(np.array(_UpperCamelCase     ) == np.array(_UpperCamelCase     )     )
  
  