


'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_xlnet import XLNetTokenizer
else:
 a_					:    Tuple          =    None


a_					:    Optional[Any]          =    logging.get_logger(__name__)

a_					:    List[str]          =    {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_					:    Optional[Any]          =    {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

a_					:    Union[str, Any]          =    {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

a_					:    Optional[Any]          =    "▁"

# Segments (not really needed)
a_					:    int          =    0
a_					:    List[Any]          =    1
a_					:    Optional[Any]          =    2
a_					:    List[str]          =    3
a_					:    int          =    4




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	VOCAB_FILES_NAMES
 _lowerCAmelCase 					=	PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase 					=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase 					=	"""left"""
 _lowerCAmelCase 					=	XLNetTokenizer
 def __init__( self      ,				__magic_name__=None      ,				__magic_name__=None      ,				__magic_name__=False      ,				__magic_name__=True      ,				__magic_name__=False      ,				__magic_name__="<s>"      ,				__magic_name__="</s>"      ,				__magic_name__="<unk>"      ,				__magic_name__="<sep>"      ,				__magic_name__="<pad>"      ,				__magic_name__="<cls>"      ,				__magic_name__="<mask>"      ,				__magic_name__=["<eop>", "<eod>"]      ,				**__magic_name__      ,				)					->							Optional[int]:
      # Mask token behave like a normal word, i.e. include the space before it
      _a			      =							AddedToken(__magic_name__      ,				lstrip=__magic_name__      ,				rstrip=__magic_name__						) if isinstance(__magic_name__      ,				__magic_name__						) else mask_token

      super().__init__(
          vocab_file=__magic_name__      ,				tokenizer_file=__magic_name__      ,				do_lower_case=__magic_name__      ,				remove_space=__magic_name__      ,				keep_accents=__magic_name__      ,				bos_token=__magic_name__      ,				eos_token=__magic_name__      ,				unk_token=__magic_name__      ,				sep_token=__magic_name__      ,				pad_token=__magic_name__      ,				cls_token=__magic_name__      ,				mask_token=__magic_name__      ,				additional_special_tokens=__magic_name__      ,				**__magic_name__      ,				)

      _a			      =							3
      _a			      =							do_lower_case
      _a			      =							remove_space
      _a			      =							keep_accents
      _a			      =							vocab_file
      _a			      =							False if not self.vocab_file else True
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None						)					->							List[int]:
      _a			      =							[self.sep_token_id]
      _a			      =							[self.cls_token_id]
      if token_ids_a is None:
           return token_ids_a + sep + cls
      return token_ids_a + sep + token_ids_a + sep + cls
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None						)					->							List[int]:
      _a			      =							[self.sep_token_id]
      _a			      =							[2]

      if token_ids_a is None:
           return len(token_ids_a + sep						) * [0] + cls_segment_id
      return len(token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1] + cls_segment_id




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None						)					->							Tuple[str]:
      if not self.can_save_slow_tokenizer:
           raise ValueError(
               'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
               'tokenizer.'						)

      if not os.path.isdir(__magic_name__						):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
           return
      _a			      =							os.path.join(
          __magic_name__      ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

      if os.path.abspath(self.vocab_file						) != os.path.abspath(__magic_name__						):
           copyfile(self.vocab_file      ,				__magic_name__						)

      return (out_vocab_file,)





'''simple docstring'''






from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""EncodecFeatureExtractor"""
 _lowerCAmelCase 					=	("""T5Tokenizer""", """T5TokenizerFast""")
 def __init__( self      ,				__magic_name__      ,				__magic_name__						)					->							Optional[Any]:
      super().__init__(__magic_name__      ,				__magic_name__						)
      _a			      =							self.feature_extractor
      _a			      =							False
 def 							__UpperCAmelCase		( self      ,				__magic_name__=None      ,				__magic_name__=None      ,				__magic_name__=True						)					->							Tuple:
      return self.tokenizer.get_decoder_prompt_ids(task=__magic_name__      ,				language=__magic_name__      ,				no_timestamps=__magic_name__						)
 def __call__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							List[str]:
      # For backward compatibility
      if self._in_target_context_manager:
           return self.current_processor(*__magic_name__      ,				**__magic_name__						)

      _a			      =							kwargs.pop('audio'      ,				__magic_name__						)
      _a			      =							kwargs.pop('sampling_rate'      ,				__magic_name__						)
      _a			      =							kwargs.pop('text'      ,				__magic_name__						)
      if len(__magic_name__						) > 0:
           _a			      =							args[0]
           _a			      =							args[1:]

      if audio is None and text is None:
           raise ValueError('You need to specify either an `audio` or `text` input to process.'						)

      if text is not None:
           _a			      =							self.tokenizer(__magic_name__      ,				**__magic_name__						)

      if audio is not None:
           _a			      =							self.feature_extractor(__magic_name__      ,				*__magic_name__      ,				sampling_rate=__magic_name__      ,				**__magic_name__						)

      if audio is None:
           return inputs

      elif text is None:
           return audio_inputs

      else:
           _a			      =							audio_inputs['input_values']
           if "padding_mask" in audio_inputs:
                _a			      =							audio_inputs['padding_mask']
           return inputs
 def 							__UpperCAmelCase		( self      ,				*__magic_name__      ,				**__magic_name__						)					->							Tuple:
      _a			      =							kwargs.pop('audio'      ,				__magic_name__						)
      _a			      =							kwargs.pop('padding_mask'      ,				__magic_name__						)

      if len(__magic_name__						) > 0:
           _a			      =							args[0]
           _a			      =							args[1:]

      if audio_values is not None:
           return self._decode_audio(__magic_name__      ,				padding_mask=__magic_name__						)
      else:
           return self.tokenizer.batch_decode(*__magic_name__      ,				**__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				*__magic_name__      ,				**__magic_name__						)					->							List[Any]:
      return self.tokenizer.decode(*__magic_name__      ,				**__magic_name__						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None						)					->							List[np.ndarray]:
      _a			      =							to_numpy(__magic_name__						)
      _a      ,   _a      ,   _a			      =							audio_values.shape

      if padding_mask is None:
           return list(__magic_name__						)

      _a			      =							to_numpy(__magic_name__						)

      # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
      # token (so that the generated audio values are **not** treated as padded tokens)
      _a			      =							seq_len - padding_mask.shape[-1]
      _a			      =							1 - self.feature_extractor.padding_value
      _a			      =							np.pad(__magic_name__      ,				((0, 0), (0, difference))      ,				'constant'      ,				constant_values=__magic_name__						)

      _a			      =							audio_values.tolist()
      for i in range(__magic_name__						):
           _a			      =							np.asarray(audio_values[i]						)[
               padding_mask[i][None, :] != self.feature_extractor.padding_value
           ]
           _a			      =							sliced_audio.reshape(__magic_name__      ,				-1						)

      return audio_values

