


'''simple docstring'''






import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_					:    Any          =    logging.get_logger(__name__)

a_					:    Optional[Any]          =    {"vocab_file": "vocab.json"}

a_					:    str          =    {
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

a_					:    Dict          =    {"mgp-str": 2_7}




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	VOCAB_FILES_NAMES
 _lowerCAmelCase 					=	PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase 					=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 def __init__( self      ,				__magic_name__      ,				__magic_name__="[GO]"      ,				__magic_name__="[GO]"      ,				__magic_name__="[s]"      ,				__magic_name__="[GO]"      ,				**__magic_name__						)					->							List[Any]:
      super().__init__(
          unk_token=__magic_name__      ,				bos_token=__magic_name__      ,				eos_token=__magic_name__      ,				pad_token=__magic_name__      ,				**__magic_name__      ,				)

      with open(__magic_name__      ,				encoding='utf-8'						) as vocab_handle:
           _a			      =							json.load(__magic_name__						)
      _a			      =							{v: k for k, v in self.vocab.items()}
 @property
 def 							__UpperCAmelCase		( self						)					->							List[Any]:
      return len(self.vocab						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      return dict(self.vocab      ,				**self.added_tokens_encoder						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							int:
      _a			      =							[]
      for s in text:
           char_tokens.extend(__magic_name__						)
      return char_tokens
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[Any]:
      return self.vocab.get(__magic_name__      ,				self.vocab.get(self.unk_token						)						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Tuple:
      return self.decoder.get(__magic_name__						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None						)					->							Tuple[str]:
      if not os.path.isdir(__magic_name__						):
           logger.error('Vocabulary path ({}) should be a directory'.format(__magic_name__						)						)
           return
      _a			      =							os.path.join(
          __magic_name__      ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

      with open(__magic_name__      ,				'w'      ,				encoding='utf-8'						) as f:
           f.write(json.dumps(self.vocab      ,				indent=2      ,				sort_keys=__magic_name__      ,				ensure_ascii=__magic_name__						) + '\n'						)

      return (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

