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   ,			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 argparse
  import logging
  
  import numpy as np
  import torch
  
  from transformers import AutoModelForCausalLM, AutoTokenizer
  
  
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
      datefmt='''%m/%d/%Y %H:%M:%S''',
      level=logging.INFO,
  )
  __a   				=      logging.getLogger(__name__)
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Any:
  
  
  
  
  
  
  
        """simple docstring"""
  
        np.random.seed(args.seed     )
        torch.manual_seed(args.seed     )
        if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed     )
  
  
  
  
  def 			__lowercase	(						)    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[str]					   =		argparse.ArgumentParser()
        parser.add_argument(
            '''--model_name_or_path''',    default=_UpperCamelCase,    type=_UpperCamelCase,    required=_UpperCamelCase,    )
        parser.add_argument('''--prompt''',    type=_UpperCamelCase,    default=''''''     )
        parser.add_argument('''--length''',    type=_UpperCamelCase,    default=20     )
        parser.add_argument('''--stop_token''',    type=_UpperCamelCase,    default=_UpperCamelCase,    help='''Token at which text generation is stopped'''     )
        parser.add_argument(
            '''--temperature''',    type=_UpperCamelCase,    default=1.0,    help='''temperature of 1.0 has no effect, lower tend toward greedy sampling''',    )
        parser.add_argument(
            '''--repetition_penalty''',    type=_UpperCamelCase,    default=1.0,    help='''primarily useful for CTRL model; in that case, use 1.2'''     )
        parser.add_argument('''--k''',    type=_UpperCamelCase,    default=0     )
        parser.add_argument('''--penalty_alpha''',    type=_UpperCamelCase,    default=0.0     )
        parser.add_argument('''--p''',    type=_UpperCamelCase,    default=0.9     )
  
        parser.add_argument('''--prefix''',    type=_UpperCamelCase,    default='''''',    help='''Text added prior to input.'''     )
        parser.add_argument('''--padding_text''',    type=_UpperCamelCase,    default='''''',    help='''Deprecated, the use of `--prefix` is preferred.'''     )
        parser.add_argument('''--xlm_language''',    type=_UpperCamelCase,    default='''''',    help='''Optional language when used with the XLM model.'''     )
  
        parser.add_argument('''--seed''',    type=_UpperCamelCase,    default=42,    help='''random seed for initialization'''     )
        parser.add_argument('''--no_cuda''',    action='''store_true''',    help='''Avoid using CUDA when available'''     )
        parser.add_argument(
            '''--fp16''',    action='''store_true''',    help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''',    )
        lowercase			:   Any					   =		parser.parse_args()
  
        lowercase			:   int					   =		torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''     )
        lowercase			:   List[str]					   =		0 if args.no_cuda else torch.cuda.device_count()
  
        logger.warning(f"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""     )
  
        set_seed(_UpperCamelCase     )
  
        # Initialize the model and tokenizer
        lowercase			:   Optional[Any]					   =		AutoTokenizer.from_pretrained(args.model_name_or_path     )
        lowercase			:   List[Any]					   =		AutoModelForCausalLM.from_pretrained(args.model_name_or_path     )
  
        # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
        # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
        model.to(args.device     )
  
        if args.fpaa:
              model.half()
  
        logger.info(_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		args.prompt if args.prompt else input('''Model prompt >>> '''     )
  
        lowercase			:   List[Any]					   =		tokenizer(_UpperCamelCase,    return_tensors='''pt''',    add_special_tokens=_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		{key: value.to(args.device     ) for key, value in inputs.items()}
  
        lowercase			:   Optional[int]					   =		model.generate(
            **_UpperCamelCase,    max_length=args.length + len(inputs['''input_ids'''][0]     ),    penalty_alpha=args.penalty_alpha,    top_k=args.k,    )
  
        lowercase			:   Union[str, Any]					   =		[]
        for generated_sequence_idx, generated_sequence in enumerate(_UpperCamelCase     ):
              print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""     )
              lowercase			:   Optional[Any]					   =		generated_sequence.tolist()
  
              # Decode text
              lowercase			:   int					   =		tokenizer.decode(_UpperCamelCase,    clean_up_tokenization_spaces=_UpperCamelCase,    add_special_tokens=_UpperCamelCase     )
  
              # Remove all text after the stop token
              lowercase			:   Optional[int]					   =		text[: text.find(args.stop_token     ) if args.stop_token else None]
  
              # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
              lowercase			:   Optional[Any]					   =		(
                  prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0],    clean_up_tokenization_spaces=_UpperCamelCase     )     ) :]
              )
  
              generated_sequences.append(_UpperCamelCase     )
              print(_UpperCamelCase     )
  
        return generated_sequences
  
  
  if __name__ == "__main__":
    main()
  
  