code: 
  '''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
  
  
  __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       ,     lowerCamelCase__       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       value
  
  
  
  			def __str__(					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.to_string()
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									logger.error(
  									    """This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"""       )
  									return self._value
  
  
  
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									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							(       __UpperCAmelCase		,						__UpperCAmelCase				):
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self._value
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self._value
  
  
  
  
  
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().__init__(lowerCamelCase__       )
  
  									if not is_vision_available():
  															raise ImportError("""PIL must be installed in order to handle images."""       )
  
  									lowercase__							 =       None
  									lowercase__							 =       None
  									lowercase__							 =       None
  
  									if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  															lowercase__							 =       value
  									elif isinstance(lowerCamelCase__       ,     (str, pathlib.Path)       ):
  															lowercase__							 =       value
  									elif isinstance(lowerCamelCase__       ,     torch.Tensor       ):
  															lowercase__							 =       value
  									else:
  															raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(lowerCamelCase__       )}'''       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									from IPython.display import Image, display
  
  									display(Image(self.to_string()       )       )
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if self._raw is not None:
  															return self._raw
  
  									if self._path is not None:
  															lowercase__							 =       Image.open(self._path       )
  															return self._raw
  
  
  
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if self._path is not None:
  															return self._path
  
  									if self._raw is not None:
  															lowercase__							 =       tempfile.mkdtemp()
  															lowercase__							 =       os.path.join(lowerCamelCase__       ,     str(uuid.uuida()       ) + """.png"""       )
  															self._raw.save(self._path       )
  
  															return self._path
  
  									if self._tensor is not None:
  															lowercase__							 =       self._tensor.cpu().detach().numpy()
  
  															# There is likely simpler than load into image into save
  															lowercase__							 =       Image.fromarray((array * 255).astype(np.uinta       )       )
  
  															lowercase__							 =       tempfile.mkdtemp()
  															lowercase__							 =       os.path.join(lowerCamelCase__       ,     str(uuid.uuida()       ) + """.png"""       )
  
  															img.save(self._path       )
  
  															return self._path
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__=16_000       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().__init__(lowerCamelCase__       )
  
  									if not is_soundfile_availble():
  															raise ImportError("""soundfile must be installed in order to handle audio."""       )
  
  									lowercase__							 =       None
  									lowercase__							 =       None
  
  									lowercase__							 =       samplerate
  
  									if isinstance(lowerCamelCase__       ,     (str, pathlib.Path)       ):
  															lowercase__							 =       value
  									elif isinstance(lowerCamelCase__       ,     torch.Tensor       ):
  															lowercase__							 =       value
  									else:
  															raise ValueError(F'''Unsupported audio type: {type(lowerCamelCase__       )}'''       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__=None       ,     lowerCamelCase__=None       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									from IPython.display import Audio, display
  
  									display(Audio(self.to_string()       ,     rate=self.samplerate       )       )
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if self._tensor is not None:
  															return self._tensor
  
  									if self._path is not None:
  															lowercase__ ,						lowercase__							 =       sf.read(self._path       )
  															lowercase__							 =       torch.tensor(lowerCamelCase__       )
  															return self._tensor
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if self._path is not None:
  															return self._path
  
  									if self._tensor is not None:
  															lowercase__							 =       tempfile.mkdtemp()
  															lowercase__							 =       os.path.join(lowerCamelCase__       ,     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		(							*lowercase__		,		**lowercase__	):
  						lowercase__							 =       [(arg.to_raw() if isinstance(lowercase__		,		lowercase__	) else arg) for arg in args]
  						lowercase__							 =       {k: (v.to_raw() if isinstance(lowercase__		,		lowercase__	) else v) for k, v in kwargs.items()}
  						return args, kwargs
  
  
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=None	):
  						if isinstance(lowercase__		,		lowercase__	):
  												lowercase__							 =       {}
  												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__							 =       AGENT_TYPE_MAPPING[output_types[i]](lowercase__	)
  																								else:
  																														lowercase__							 =       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	):
  																																				lowercase__							 =       _v(lowercase__	)
  																								if k not in decoded_outputs:
  																														lowercase__							 =       AgentType[v]
  
  						elif isinstance(lowercase__		,		(list, tuple)	):
  												lowercase__							 =       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
  																								lowercase__							 =       False
  																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
  																														if isinstance(lowercase__		,		_k	):
  																																				decoded_outputs.append(_v(lowercase__	)	)
  																																				lowercase__							 =       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
  																		lowercase__							 =       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
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  def  _A		(							lowercase__	):
  
  						# validate:
  						if not isinstance(lowercase__		,		lowercase__	) or not all(
  						    isinstance(lowercase__		,		lowercase__	) and item >= 0 for item in citations	):
  												raise ValueError("""The citations should be a list of non negative integers."""	)
  
  						citations.sort()
  						lowercase__							 =       len(lowercase__	)
  
  						for i in range(lowercase__	):
  												if citations[len_citations - 1 - i] <= i:
  																		return i
  
  						return len_citations
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
  
  
  
  
  