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
  
  
  lowerCamelCase_     :     Tuple  		=						logging.get_logger(__name__)
  
  if is_vision_available():
         import PIL.Image
         from PIL import Image
         from PIL.Image import Image as ImageType
  else:
         lowerCamelCase_     :     Dict  		=						object
  
  if is_torch_available():
         import torch
  
  if is_soundfile_availble():
         import soundfile as sf
  
  
  
  
  
  
  class    __A       :
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A		)      ->  Union[str, Any]:
       a					=value
  
  
   def __str__(	self		)      ->  Any:
       return self.to_string()
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
       logger.error(
           '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''		)
       return self._value
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  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       ( _SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
       return self._value
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
       return self._value
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A		)      ->  Optional[Any]:
       super().__init__(__A		)
  
       if not is_vision_available():
           raise ImportError('''PIL must be installed in order to handle images.'''		)
  
       a					=None
       a					=None
       a					=None
  
       if isinstance(__A				,					__A		):
           a					=value
       elif isinstance(__A				,					(str, pathlib.Path)		):
           a					=value
       elif isinstance(__A				,					torch.Tensor		):
           a					=value
       else:
           raise ValueError(f'''Unsupported type for {self.__class__.__name__}: {type(__A		)}'''		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A=None				,					__A=None		)      ->  str:
       from IPython.display import Image, display
  
       display(Image(self.to_string()		)		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
       if self._raw is not None:
           return self._raw
  
       if self._path is not None:
           a					=Image.open(self._path		)
           return self._raw
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[Any]:
       if self._path is not None:
           return self._path
  
       if self._raw is not None:
           a					=tempfile.mkdtemp()
           a					=os.path.join(__A				,					str(uuid.uuida()		) + '''.png'''		)
           self._raw.save(self._path		)
  
           return self._path
  
       if self._tensor is not None:
           a					=self._tensor.cpu().detach().numpy()
  
           # There is likely simpler than load into image into save
           a					=Image.fromarray((array * 255).astype(np.uinta		)		)
  
           a					=tempfile.mkdtemp()
           a					=os.path.join(__A				,					str(uuid.uuida()		) + '''.png'''		)
  
           img.save(self._path		)
  
           return self._path
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A				,					__A=1_6000		)      ->  Optional[Any]:
       super().__init__(__A		)
  
       if not is_soundfile_availble():
           raise ImportError('''soundfile must be installed in order to handle audio.'''		)
  
       a					=None
       a					=None
  
       a					=samplerate
  
       if isinstance(__A				,					(str, pathlib.Path)		):
           a					=value
       elif isinstance(__A				,					torch.Tensor		):
           a					=value
       else:
           raise ValueError(f'''Unsupported audio type: {type(__A		)}'''		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A=None				,					__A=None		)      ->  Optional[Any]:
       from IPython.display import Audio, display
  
       display(Audio(self.to_string()				,					rate=self.samplerate		)		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
       if self._tensor is not None:
           return self._tensor
  
       if self._path is not None:
           a	,   a					=sf.read(self._path		)
           a					=torch.tensor(__A		)
           return self._tensor
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
       if self._path is not None:
           return self._path
  
       if self._tensor is not None:
           a					=tempfile.mkdtemp()
           a					=os.path.join(__A				,					str(uuid.uuida()		) + '''.wav'''		)
           sf.write(self._path				,					self._tensor				,					samplerate=self.samplerate		)
           return self._path
  
  lowerCamelCase_     :     Any  		=						{"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
  lowerCamelCase_     :     Optional[Any]  		=						{str: AgentText}
  
  if is_vision_available():
         lowerCamelCase_     :     Dict  		=						AgentImage
  def _A		(		*lowercase ,					**lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=[(arg.to_raw() if isinstance(lowercase ,					lowercase	) else arg) for arg in args]
      a					={k: (v.to_raw() if isinstance(lowercase ,					lowercase	) else v) for k, v in kwargs.items()}
      return args, kwargs
  
  
  
  
  
  def _A		(		lowercase ,					lowercase=None	):
  
  
  
  
  
  
  
      """simple docstring"""
      if isinstance(lowercase ,					lowercase	):
          a					={}
          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:
                      a					=AGENT_TYPE_MAPPING[output_types[i]](lowercase	)
                  else:
                      a					=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	):
                          a					=_v(lowercase	)
                  if k not in decoded_outputs:
                      a					=AgentType[v]
  
      elif isinstance(lowercase ,					(list, tuple)	):
          a					=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
                  a					=False
                  for _k, _v in INSTANCE_TYPE_MAPPING.items():
                      if isinstance(lowercase ,					_k	):
                          decoded_outputs.append(_v(lowercase	)	)
                          a					=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
              a					=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"""
  
  
  
  
  
  
  
  import copy
  
  from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
  
  
  lowerCamelCase_     :     int    		=						logging.get_logger(__name__)
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"hybrid-clip"
   __lowerCAmelCase				      =						True
  
  
   def __init__(	self				,					__A=512				,					**__A		)      ->  Any:
       super().__init__(**__A		)
  
       if "text_config" not in kwargs:
           raise ValueError('''`text_config` can not be `None`.'''		)
  
       if "vision_config" not in kwargs:
           raise ValueError('''`vision_config` can not be `None`.'''		)
  
       a					=kwargs.pop('''text_config'''		)
       a					=kwargs.pop('''vision_config'''		)
  
       a					=text_config.pop('''model_type'''		)
       a					=vision_config.pop('''model_type'''		)
  
       from transformers import AutoConfig
  
       a					=AutoConfig.for_model(__A				,					**__A		)
  
       if vision_model_type == "clip":
           a					=AutoConfig.for_model(__A				,					**__A		).vision_config
       elif vision_model_type == "clip_vision_model":
           from transformers import CLIPVisionConfig
  
           a					=CLIPVisionConfig(**__A		)
       else:
           a					=AutoConfig.for_model(__A				,					**__A		)
  
       a					=projection_dim
       a					=1.0
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A				,					**__A		)      ->  Tuple:
       return cls(text_config=text_config.to_dict()				,					vision_config=vision_config.to_dict()				,					**__A		)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       a					=copy.deepcopy(self.__dict__		)
       a					=self.text_config.to_dict()
       a					=self.vision_config.to_dict()
       a					=self.__class__.model_type
       return output