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							:			List[Any]				=object
  
  if is_torch_available():
    import torch
  
  if is_soundfile_availble():
    import soundfile as sf
  
  
  
  
  
  
  class      UpperCAmelCase			:
   def __init__(       self   :Optional[Any]		, lowercase_   :Optional[int]							)->							List[Any]:
       A__       =    value
   def __str__(       self   :Optional[int]							)->							Any:
       return self.to_string()
   def   UpperCAmelCase_							(       self   :str							)->							Dict:
       logger.error(
           "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"							)
       return self._value
  
  
  
   def   UpperCAmelCase_							(       self   :str							)->							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			( __SCREAMING_SNAKE_CASE    , __SCREAMING_SNAKE_CASE       ):
   def   UpperCAmelCase_							(       self   :Dict							)->							int:
       return self._value
  
  
  
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							int:
       return self._value
  
  
  
  
  
  
  class      UpperCAmelCase			( __SCREAMING_SNAKE_CASE    , __SCREAMING_SNAKE_CASE       ):
   def __init__(       self   :List[Any]		, lowercase_   :Optional[Any]							)->							Optional[int]:
       super().__init__(UpperCamelCase__							)
  
       if not is_vision_available():
           raise ImportError("PIL must be installed in order to handle images."							)
  
       A__       =    None
       A__       =    None
       A__       =    None
  
       if isinstance(UpperCamelCase__		, UpperCamelCase__							):
           A__       =    value
       elif isinstance(UpperCamelCase__		, (str, pathlib.Path)							):
           A__       =    value
       elif isinstance(UpperCamelCase__		, torch.Tensor							):
           A__       =    value
       else:
           raise ValueError(F"Unsupported type for {self.__class__.__name__}: {type(UpperCamelCase__							)}"							)
   def   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :Optional[Any]=None		, lowercase_   :Optional[int]=None							)->							List[Any]:
       from IPython.display import Image, display
  
       display(Image(self.to_string()							)							)
   def   UpperCAmelCase_							(       self   :Tuple							)->							Union[str, 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   UpperCAmelCase_							(       self   :str							)->							Union[str, Any]:
  
  
  
  
  
  
  
       if self._path is not None:
           return self._path
  
       if self._raw is not None:
           A__       =    tempfile.mkdtemp()
           A__       =    os.path.join(UpperCamelCase__		, 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 * 2_55).astype(np.uinta							)							)
  
           A__       =    tempfile.mkdtemp()
           A__       =    os.path.join(UpperCamelCase__		, str(uuid.uuida()							) + ".png"							)
  
           img.save(self._path							)
  
           return self._path
  
  
  
  
  
  
  class      UpperCAmelCase			( __SCREAMING_SNAKE_CASE       ):
   def __init__(       self   :int		, lowercase_   :Dict		, lowercase_   :Optional[int]=1_60_00							)->							Any:
       super().__init__(UpperCamelCase__							)
  
       if not is_soundfile_availble():
           raise ImportError("soundfile must be installed in order to handle audio."							)
  
       A__       =    None
       A__       =    None
  
       A__       =    samplerate
  
       if isinstance(UpperCamelCase__		, (str, pathlib.Path)							):
           A__       =    value
       elif isinstance(UpperCamelCase__		, torch.Tensor							):
           A__       =    value
       else:
           raise ValueError(F"Unsupported audio type: {type(UpperCamelCase__							)}"							)
   def   UpperCAmelCase_							(       self   :str		, lowercase_   :Optional[int]=None		, lowercase_   :Union[str, Any]=None							)->							Tuple:
       from IPython.display import Audio, display
  
       display(Audio(self.to_string()		, rate=self.samplerate							)							)
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							Tuple:
  
  
  
  
  
  
  
       if self._tensor is not None:
           return self._tensor
  
       if self._path is not None:
           A__       =    sf.read(self._path							)
           A__       =    torch.tensor(UpperCamelCase__							)
           return self._tensor
  
  
  
   def   UpperCAmelCase_							(       self   :Any							)->							Any:
  
  
  
  
  
  
  
       if self._path is not None:
           return self._path
  
       if self._tensor is not None:
           A__       =    tempfile.mkdtemp()
           A__       =    os.path.join(UpperCamelCase__		, str(uuid.uuida()							) + ".wav"							)
           sf.write(self._path		, self._tensor		, samplerate=self.samplerate							)
           return self._path
  
  __lowerCAmelCase							:			Optional[Any]				={"text": AgentText, "image": AgentImage, "audio": AgentAudio}
  __lowerCAmelCase							:			Any				={str: AgentText}
  
  if is_vision_available():
    __lowerCAmelCase							:			List[str]				=AgentImage
  
  
  
  
  
  def 		UpperCamelCase       (							*_lowerCamelCase      :      int		, **_lowerCamelCase      :      Optional[Any] ):
      A__       =    [(arg.to_raw() if isinstance(SCREAMING_SNAKE_CASE__		, SCREAMING_SNAKE_CASE__ ) else arg) for arg in args]
      A__       =    {k: (v.to_raw() if isinstance(SCREAMING_SNAKE_CASE__		, SCREAMING_SNAKE_CASE__ ) else v) for k, v in kwargs.items()}
      return args, kwargs
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      int=None ):
  
  
      if isinstance(SCREAMING_SNAKE_CASE__		, SCREAMING_SNAKE_CASE__ ):
          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]](SCREAMING_SNAKE_CASE__ )
                  else:
                      A__       =    AgentType(SCREAMING_SNAKE_CASE__ )
  
              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(SCREAMING_SNAKE_CASE__		, _k ):
                          A__       =    _v(SCREAMING_SNAKE_CASE__ )
                  if k not in decoded_outputs:
                      A__       =    AgentType[v]
  
      elif isinstance(SCREAMING_SNAKE_CASE__		, (list, tuple) ):
          A__       =    type(SCREAMING_SNAKE_CASE__ )()
          for i, v in enumerate(SCREAMING_SNAKE_CASE__ ):
              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]](SCREAMING_SNAKE_CASE__ ) )
                  else:
                      decoded_outputs.append(AgentType(SCREAMING_SNAKE_CASE__ ) )
              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(SCREAMING_SNAKE_CASE__		, _k ):
                          decoded_outputs.append(_v(SCREAMING_SNAKE_CASE__ ) )
                          A__       =    True
  
                  if not found:
                      decoded_outputs.append(AgentType(SCREAMING_SNAKE_CASE__ ) )
  
      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]](SCREAMING_SNAKE_CASE__ )
  
          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(SCREAMING_SNAKE_CASE__		, _k ):
                      return _v(SCREAMING_SNAKE_CASE__ )
              return AgentType(SCREAMING_SNAKE_CASE__ )
  
      return decoded_outputs
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import os
  import tempfile
  import unittest
  from typing import List
  
  from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
  from transformers.testing_utils import require_tokenizers
  from transformers.tokenization_utils import PreTrainedTokenizer
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  
  
  
  
  
  
  @require_tokenizers
  class      UpperCAmelCase			( unittest.TestCase       ):
   __lowercase     	=       EsmTokenizer
   def   UpperCAmelCase_							(       self   :Any							)->							int:
       super().setUp()
       A__       =    tempfile.mkdtemp()
       # fmt: off
       A__       =    ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
       # fmt: on
       A__       =    os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES["vocab_file"]							)
       with open(self.vocab_file		, "w"		, encoding="utf-8"							) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]							)							)
   def   UpperCAmelCase_							(       self   :Any		, **lowercase_   :int							)->							List[PreTrainedTokenizerBase]:
       return [self.get_tokenizer(**lowercase_							)]
   def   UpperCAmelCase_							(       self   :Any		, **lowercase_   :Dict							)->							PreTrainedTokenizer:
       return self.tokenizer_class.from_pretrained(self.tmpdirname		, **lowercase_							)
   def   UpperCAmelCase_							(       self   :int							)->							Dict:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    tokenizer.tokenize("LAGVS"							)
       self.assertListEqual(lowercase_		, ["L", "A", "G", "V", "S"]							)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_							)		, [4, 5, 6, 7, 8]							)
   def   UpperCAmelCase_							(       self   :Tuple							)->							Tuple:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    "LAGVS"
       self.assertListEqual(tokenizer.encode(lowercase_							)		, [0, 4, 5, 6, 7, 8, 2]							)
   def   UpperCAmelCase_							(       self   :Any							)->							Tuple:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    ["LAGVS", "WCB"]
       A__       =    tokenizer(lowercase_		, padding=lowercase_							)["input_ids"]
  
       self.assertListEqual(lowercase_		, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]							)
   def   UpperCAmelCase_							(       self   :str							)->							Any:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    ["LAGVS", "WCB"]
       A__       =    tokenizer(lowercase_		, padding=lowercase_							)["input_ids"]
  
       self.assertListEqual(lowercase_		, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]							)
  
  
  
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							List[str]:
       A__       =    self.get_tokenizers(fast=lowercase_							)
       for tokenizer in tokenizers:
           with self.subTest(F"{tokenizer.__class__.__name__}"							):
               A__       =    "<unk>"
               A__       =    "<mask>"
  
               A__       =    tokenizer.tokenize(lowercase_							)
               A__       =    tokenizer.tokenize(lowercase_							)
  
               self.assertEqual(len(lowercase_							)		, 1							)
               self.assertEqual(len(lowercase_							)		, 1							)
               self.assertEqual(token_a[0]		, lowercase_							)
               self.assertEqual(token_a[0]		, lowercase_							)
  
  
  
  