





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_snake_case  :   int						   =  logging.get_logger(__name__)

_snake_case  :   Optional[int]						   =  '▁'

_snake_case  :   int						   =  {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

_snake_case  :   Optional[Any]						   =  {
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

_snake_case  :   List[str]						   =  {'vinai/bartpho-syllable': 1024}



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			VOCAB_FILES_NAMES
  a_      =			PRETRAINED_VOCAB_FILES_MAP
  a_      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_      =			["""input_ids""", """attention_mask"""]






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		int="<s>" ,  lowerCAmelCase_			:		List[str]="</s>" ,  lowerCAmelCase_			:		int="</s>" ,  lowerCAmelCase_			:		Union[str, Any]="<s>" ,  lowerCAmelCase_			:		List[str]="<unk>" ,  lowerCAmelCase_			:		Any="<pad>" ,  lowerCAmelCase_			:		int="<mask>" ,  lowerCAmelCase_			:		Optional[Dict[str, Any]] = None ,  **lowerCAmelCase_			:		int ,  )							->						None:
     # Mask token behave like a normal word, i.e. include the space before it
     __lowerCAmelCase							     =     AddedToken(lowerCAmelCase_ ,  lstrip=lowerCAmelCase_ ,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					) else mask_token

     __lowerCAmelCase							     =     {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=lowerCAmelCase_ ,  eos_token=lowerCAmelCase_ ,  unk_token=lowerCAmelCase_ ,  sep_token=lowerCAmelCase_ ,  cls_token=lowerCAmelCase_ ,  pad_token=lowerCAmelCase_ ,  mask_token=lowerCAmelCase_ ,  sp_model_kwargs=self.sp_model_kwargs ,  **lowerCAmelCase_ ,  )

     __lowerCAmelCase							     =     vocab_file
     __lowerCAmelCase							     =     monolingual_vocab_file
     __lowerCAmelCase							     =     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
     self.sp_model.Load(str(lowerCAmelCase_					)					)

     # Load the reduced vocab

     # Keep order of special tokens for backward compatibility
     __lowerCAmelCase							     =     {}
     __lowerCAmelCase							     =     0
     for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
        if str(lowerCAmelCase_					) not in self.fairseq_tokens_to_ids:
           __lowerCAmelCase							     =     cnt
           cnt += 1
     with open(lowerCAmelCase_ ,  'r' ,  encoding='utf-8'					) as f:
        for line in f.readlines():
           __lowerCAmelCase							     =     line.strip().split()[0]
           __lowerCAmelCase							     =     len(self.fairseq_tokens_to_ids					)
     if str(lowerCAmelCase_					) not in self.fairseq_tokens_to_ids:
        __lowerCAmelCase							     =     len(self.fairseq_tokens_to_ids					)

     __lowerCAmelCase							     =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}






  def __getstate__(    self			:		str					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     self.__dict__.copy()
     __lowerCAmelCase							     =     None
     __lowerCAmelCase							     =     self.sp_model.serialized_model_proto()
     return state






  def __setstate__(    self			:		int ,  lowerCAmelCase_			:		str					)							->						List[str]:
     __lowerCAmelCase							     =     d

     # for backward compatibility
     if not hasattr(self ,  'sp_model_kwargs'					):
        __lowerCAmelCase							     =     {}

     __lowerCAmelCase							     =     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		List[int] ,  lowerCAmelCase_			:		Optional[List[int]] = None					)							->						List[int]:

     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     __lowerCAmelCase							     =     [self.cls_token_id]
     __lowerCAmelCase							     =     [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		List[int] ,  lowerCAmelCase_			:		Optional[List[int]] = None ,  lowerCAmelCase_			:		bool = False					)							->						List[int]:

     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=lowerCAmelCase_ ,  token_ids_a=lowerCAmelCase_ ,  already_has_special_tokens=lowerCAmelCase_					)

     if token_ids_a is None:
        return [1] + ([0] * len(lowerCAmelCase_					)) + [1]
     return [1] + ([0] * len(lowerCAmelCase_					)) + [1, 1] + ([0] * len(lowerCAmelCase_					)) + [1]






  def     lowercase		(    self			:		Optional[int] ,  lowerCAmelCase_			:		List[int] ,  lowerCAmelCase_			:		Optional[List[int]] = None					)							->						List[int]:
     __lowerCAmelCase							     =     [self.sep_token_id]
     __lowerCAmelCase							     =     [self.cls_token_id]

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






  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Optional[Any]:
     return len(self.fairseq_ids_to_tokens					)






  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[int]:
     __lowerCAmelCase							     =     {self.convert_ids_to_tokens(lowerCAmelCase_					): i for i in range(self.vocab_size					)}
     vocab.update(self.added_tokens_encoder					)
     return vocab






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		str					)							->						List[str]:
     return self.sp_model.encode(lowerCAmelCase_ ,  out_type=lowerCAmelCase_					)






  def     lowercase		(    self			:		Any ,  lowerCAmelCase_			:		List[Any]					)							->						int:
     if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
     else:
        return self.unk_token_id






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		Optional[int]					)							->						Tuple:
     return self.fairseq_ids_to_tokens[index]






  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		Any					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     ''.join(lowerCAmelCase_					).replace(lowerCAmelCase_ ,  ' '					).strip()
     return out_string






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		Optional[str] = None					)							->						Tuple[str]:
     if not os.path.isdir(lowerCAmelCase_					):
        logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
        return
     __lowerCAmelCase							     =     os.path.join(
         lowerCAmelCase_ ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)
     __lowerCAmelCase							     =     os.path.join(
         lowerCAmelCase_ ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file'] ,  )

     if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase_					) and os.path.isfile(self.vocab_file					):
        copyfile(self.vocab_file ,  lowerCAmelCase_					)
     elif not os.path.isfile(self.vocab_file					):
        with open(lowerCAmelCase_ ,  'wb'					) as fi:
           __lowerCAmelCase							     =     self.sp_model.serialized_model_proto()
           fi.write(lowerCAmelCase_					)

     if os.path.abspath(self.monolingual_vocab_file					) != os.path.abspath(
         lowerCAmelCase_					) and os.path.isfile(self.monolingual_vocab_file					):
        copyfile(self.monolingual_vocab_file ,  lowerCAmelCase_					)
     elif not os.path.isfile(self.monolingual_vocab_file					):
        with open(lowerCAmelCase_ ,  'w' ,  encoding='utf-8'					) as fp:
           for token in self.fairseq_tokens_to_ids:
              if token not in self.all_special_tokens:
                 fp.write(f"""{str(lowerCAmelCase_					)} \n"""					)

     return out_vocab_file, out_monolingual_vocab_file








import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case  :   List[str]						   =  logging.get_logger(__name__)

_snake_case  :   Dict						   =  {
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			"""encodec"""






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[Any]=[1.5, 3.0, 6.0, 12.0, 24.0] ,  lowerCAmelCase_			:		Optional[Any]=2_4_0_0_0 ,  lowerCAmelCase_			:		int=1 ,  lowerCAmelCase_			:		Union[str, Any]=False ,  lowerCAmelCase_			:		Optional[Any]=None ,  lowerCAmelCase_			:		Union[str, Any]=None ,  lowerCAmelCase_			:		Optional[Any]=1_2_8 ,  lowerCAmelCase_			:		List[Any]=3_2 ,  lowerCAmelCase_			:		Optional[Any]=1 ,  lowerCAmelCase_			:		Optional[int]=[8, 5, 4, 2] ,  lowerCAmelCase_			:		Optional[Any]="weight_norm" ,  lowerCAmelCase_			:		Tuple=7 ,  lowerCAmelCase_			:		Optional[int]=7 ,  lowerCAmelCase_			:		Dict=3 ,  lowerCAmelCase_			:		Optional[Any]=2 ,  lowerCAmelCase_			:		List[Any]=True ,  lowerCAmelCase_			:		Dict="reflect" ,  lowerCAmelCase_			:		List[Any]=2 ,  lowerCAmelCase_			:		int=2 ,  lowerCAmelCase_			:		Dict=1.0 ,  lowerCAmelCase_			:		str=1_0_2_4 ,  lowerCAmelCase_			:		Dict=None ,  lowerCAmelCase_			:		List[Any]=True ,  **lowerCAmelCase_			:		Optional[Any] ,  )							->						Union[str, Any]:
     __lowerCAmelCase							     =     target_bandwidths
     __lowerCAmelCase							     =     sampling_rate
     __lowerCAmelCase							     =     audio_channels
     __lowerCAmelCase							     =     normalize
     __lowerCAmelCase							     =     chunk_length_s
     __lowerCAmelCase							     =     overlap
     __lowerCAmelCase							     =     hidden_size
     __lowerCAmelCase							     =     num_filters
     __lowerCAmelCase							     =     num_residual_layers
     __lowerCAmelCase							     =     upsampling_ratios
     __lowerCAmelCase							     =     norm_type
     __lowerCAmelCase							     =     kernel_size
     __lowerCAmelCase							     =     last_kernel_size
     __lowerCAmelCase							     =     residual_kernel_size
     __lowerCAmelCase							     =     dilation_growth_rate
     __lowerCAmelCase							     =     use_causal_conv
     __lowerCAmelCase							     =     pad_mode
     __lowerCAmelCase							     =     compress
     __lowerCAmelCase							     =     num_lstm_layers
     __lowerCAmelCase							     =     trim_right_ratio
     __lowerCAmelCase							     =     codebook_size
     __lowerCAmelCase							     =     codebook_dim if codebook_dim is not None else hidden_size
     __lowerCAmelCase							     =     use_conv_shortcut

     if self.norm_type not in ["weight_norm", "time_group_norm"]:
        raise ValueError(
            f"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""					)

     super().__init__(**lowerCAmelCase_					)






  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Optional[int]:
     if self.chunk_length_s is None:
        return None
     else:
        return int(self.chunk_length_s * self.sampling_rate					)






  @property
  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[int]:
     if self.chunk_length_s is None or self.overlap is None:
        return None
     else:
        return max(1 ,  int((1.0 - self.overlap) * self.chunk_length					)					)






  @property
  def     lowercase		(    self			:		Any					)							->						int:
     __lowerCAmelCase							     =     np.prod(self.upsampling_ratios					)
     return math.ceil(self.sampling_rate / hop_length					)






  @property
  def     lowercase		(    self			:		str					)							->						int:
     return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0)					)

