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				:	List[Any]				    =       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 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      int      ,      _lowerCAmelCase						:      List[Any]      ):
  			A							  =      value
  
  
  
  
  
  
  
  		def __str__(self						:      List[str]      ):
  			return self.to_string()
  
  
  
  
  
  
  
  		def        A     (self						:      List[Any]      ):
  			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						:      List[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 							__UpperCAmelCase  (       A__		,					A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ):
  			return self._value
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ):
  			return self._value
  
  
  
  
  
  
  class 							__UpperCAmelCase  (       A__		,					A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      str      ,      _lowerCAmelCase						:      Dict      ):
  			super().__init__(_lowerCAmelCase      )
  
  			if not is_vision_available():
  				raise ImportError("""PIL must be installed in order to handle images."""      )
  
  			A							  =      None
  			A							  =      None
  			A							  =      None
  
  			if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ):
  				A							  =      value
  			elif isinstance(_lowerCAmelCase      ,      (str, pathlib.Path)      ):
  				A							  =      value
  			elif isinstance(_lowerCAmelCase      ,      torch.Tensor      ):
  				A							  =      value
  			else:
  				raise ValueError(F"""Unsupported type for {self.__class__.__name__}: {type(_lowerCAmelCase      )}"""      )
  
  
  
  
  
  
  
  		def        A     (self						:      str      ,      _lowerCAmelCase						:      List[Any]=None      ,      _lowerCAmelCase						:      Optional[int]=None      ):
  			from IPython.display import Image, display
  
  			display(Image(self.to_string()      )      )
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ):
  			if self._raw is not None:
  				return self._raw
  
  			if self._path is not None:
  				A							  =      Image.open(self._path      )
  				return self._raw
  
  
  
  
  
  
  
  		def        A     (self						:      List[Any]      ):
  			if self._path is not None:
  				return self._path
  
  			if self._raw is not None:
  				A							  =      tempfile.mkdtemp()
  				A							  =      os.path.join(_lowerCAmelCase      ,      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(_lowerCAmelCase      ,      str(uuid.uuida()      ) + """.png"""      )
  
  				img.save(self._path      )
  
  				return self._path
  
  
  
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      Tuple      ,      _lowerCAmelCase						:      List[Any]      ,      _lowerCAmelCase						:      List[Any]=1_6000      ):
  			super().__init__(_lowerCAmelCase      )
  
  			if not is_soundfile_availble():
  				raise ImportError("""soundfile must be installed in order to handle audio."""      )
  
  			A							  =      None
  			A							  =      None
  
  			A							  =      samplerate
  
  			if isinstance(_lowerCAmelCase      ,      (str, pathlib.Path)      ):
  				A							  =      value
  			elif isinstance(_lowerCAmelCase      ,      torch.Tensor      ):
  				A							  =      value
  			else:
  				raise ValueError(F"""Unsupported audio type: {type(_lowerCAmelCase      )}"""      )
  
  
  
  
  
  
  
  		def        A     (self						:      int      ,      _lowerCAmelCase						:      Optional[int]=None      ,      _lowerCAmelCase						:      List[Any]=None      ):
  			from IPython.display import Audio, display
  
  			display(Audio(self.to_string()      ,      rate=self.samplerate      )      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ):
  			if self._tensor is not None:
  				return self._tensor
  
  			if self._path is not None:
  				A  ,    A							  =      sf.read(self._path      )
  				A							  =      torch.tensor(_lowerCAmelCase      )
  				return self._tensor
  
  
  
  
  
  
  
  		def        A     (self						:      int      ):
  			if self._path is not None:
  				return self._path
  
  			if self._tensor is not None:
  				A							  =      tempfile.mkdtemp()
  				A							  =      os.path.join(_lowerCAmelCase      ,      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				:	Any				    =       {str: AgentText}
  
  if is_vision_available():
  				_lowerCamelCase				:	Optional[Any]				    =       AgentImage
  
  
  
  
  def    __a (     *UpperCAmelCase       ,			**UpperCAmelCase    )      ->List[str]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      [(arg.to_raw() if isinstance(UpperCAmelCase       ,			UpperCAmelCase    ) else arg) for arg in args]
  	A							  =      {k: (v.to_raw() if isinstance(UpperCAmelCase       ,			UpperCAmelCase    ) else v) for k, v in kwargs.items()}
  	return args, kwargs
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase=None    )      ->Tuple:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if isinstance(UpperCAmelCase       ,			UpperCAmelCase    ):
  		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]](UpperCAmelCase    )
  				else:
  					A							  =      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    ):
  						A							  =      _v(UpperCAmelCase    )
  				if k not in decoded_outputs:
  					A							  =      AgentType[v]
  
  	elif isinstance(UpperCAmelCase       ,			(list, tuple)    ):
  		A							  =      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
  				A							  =      False
  				for _k, _v in INSTANCE_TYPE_MAPPING.items():
  					if isinstance(UpperCAmelCase       ,			_k    ):
  						decoded_outputs.append(_v(UpperCAmelCase    )    )
  						A							  =      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
  			A							  =      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: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from typing import Dict, List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  _lowerCamelCase				:	int				    =       logging.get_logger(__name__)
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    ['''input_ids''', '''attention_mask''']
  
  
  
  
  
  
  
  		def __init__(self						:      Tuple      ,      _lowerCAmelCase						:      str="[PAD]"      ,      _lowerCAmelCase						:      Dict="[BOS]"      ,      _lowerCAmelCase						:      Any="[EOS]"      ,      _lowerCAmelCase						:      List[Any]="[MASK]"      ,      _lowerCAmelCase						:      int="[CLS]"      ,      _lowerCAmelCase						:      Optional[Any]="[SEP]"      ,      _lowerCAmelCase						:      List[str]=2048      ,      **_lowerCAmelCase						:      Dict      ,      ):
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else pad_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else bos_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else eos_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else mask_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else cls_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else sep_token
  
  			super().__init__(
  			    pad_token=_lowerCAmelCase      ,      bos_token=_lowerCAmelCase      ,      eos_token=_lowerCAmelCase      ,      mask_token=_lowerCAmelCase      ,      cls_token=_lowerCAmelCase      ,      sep_token=_lowerCAmelCase      ,      model_max_length=_lowerCAmelCase      ,      **_lowerCAmelCase      ,      )
  
  			A							  =      2**8  # utf is 8 bits
  
  			# define special tokens dict
  			A							  =      {
  			    self.pad_token: 0,
  			    self.bos_token: 1,
  			    self.eos_token: 2,
  			    self.mask_token: 3,
  			    self.cls_token: 4,
  			    self.sep_token: 5,
  			}
  			A							  =      len(self.special_tokens_encoder      )
  			A							  =      {v: k for k, v in self.special_tokens_encoder.items()}
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ):
  			A							  =      self.special_tokens_encoder.copy()
  			vocab.update(self.added_tokens_encoder      )
  			for i in range(self._utf_vocab_size      ):
  				A							  =      chr(_lowerCAmelCase      )
  				A							  =      i + len(self.special_tokens_encoder      )
  			return vocab
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      Tuple      ):
  			return self._utf_vocab_size + self._num_special_tokens
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      List[int]      ,      _lowerCAmelCase						:      Optional[List[int]] = None      ,      _lowerCAmelCase						:      bool = False      ):
  			if already_has_special_tokens:
  				return super().get_special_tokens_mask(
  				    token_ids_a=_lowerCAmelCase      ,      token_ids_a=_lowerCAmelCase      ,      already_has_special_tokens=_lowerCAmelCase      )
  
  			# normal case: some special tokens
  			if token_ids_a is None:
  				return [1] + [0] * len(_lowerCAmelCase      ) + [1]
  			return [1] + ([0] * len(_lowerCAmelCase      )) + [1] + ([0] * len(_lowerCAmelCase      )) + [1]
  
  
  
  
  
  
  
  		def        A     (self						:      List[Any]      ,      _lowerCAmelCase						:      List[int]      ,      _lowerCAmelCase						:      Optional[List[int]] = None      ):
  			if token_ids_a is None:
  				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  			else:
  				return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]
  
  
  
  
  
  
  
  		def        A     (self						:      str      ,      _lowerCAmelCase						:      str      ):
  			A							  =      [chr(_lowerCAmelCase      ) for i in text.encode("""utf-8"""      )]
  			return tokens
  
  
  
  
  
  
  
  		def        A     (self						:      int      ,      _lowerCAmelCase						:      List[str]      ):
  			if token in self.special_tokens_encoder:
  				A							  =      self.special_tokens_encoder[token]
  			elif token in self.added_tokens_encoder:
  				A							  =      self.added_tokens_encoder[token]
  			elif len(_lowerCAmelCase      ) != 1:
  				A							  =      self.unk_token_id
  			else:
  				A							  =      ord(_lowerCAmelCase      ) + self._num_special_tokens
  			return token_id
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      Any      ):
  			if index in self.special_tokens_decoder:
  				A							  =      self.special_tokens_decoder[index]
  			elif index in self.added_tokens_decoder:
  				A							  =      self.added_tokens_decoder[index]
  			else:
  				A							  =      chr(index - self._num_special_tokens      )
  			return token
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      List[str]      ):
  			A							  =      b""""""
  			for token in tokens:
  				if token in self.special_tokens_decoder:
  					A							  =      self.special_tokens_decoder[token].encode("""utf-8"""      )
  				elif token in self.added_tokens_decoder:
  					A							  =      self.special_tokens_decoder[token].encode("""utf-8"""      )
  				elif token in self.special_tokens_encoder:
  					A							  =      token.encode("""utf-8"""      )
  				elif token in self.added_tokens_encoder:
  					A							  =      token.encode("""utf-8"""      )
  				else:
  					A							  =      bytes([ord(_lowerCAmelCase      )]      )
  				bstring += tok_string
  			A							  =      bstring.decode("""utf-8"""      ,      errors="""replace"""      )
  			return string
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Optional[str] = None      ):
  			return ()
  
  