'''simple docstring'''




import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
_A								=   logging.get_logger('transformers.models.encodec')

_A								=   {
    'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
    'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
    'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
    'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
_A								=   {
    'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
    'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
    'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
    'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
    'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
    'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
    'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
    'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
    'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
    'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
    'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
    'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
    'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
    'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
    'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
    'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
    'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
    'encoder.model.13.lstm': 'encoder.layers.13.lstm',
    'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
_A								=   {
    'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
    'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
    'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
    'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
    'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
    'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
    'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
    'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
    'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
    'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
    'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
    'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
    'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
    'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
    'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
    'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
    'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
    'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
_A								=   {
    'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
    'decoder.model.1.lstm': 'decoder.layers.1.lstm',
    'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
    'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
    'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
    'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
    'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
    'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
    'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
    'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
    'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
    'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
    'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
    'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
    'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
    'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
    'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
    'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
    'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
_A								=   {
    'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
    'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
    'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
    'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
    'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
    'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
    'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
    'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
    'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
    'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
    'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
    'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
    'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
    'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
    'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
    'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
    'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
    'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
_A								=   {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_A								=   {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_A								=   []
_A								=   []

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     for attribute in key.split('.'							):
          lowercase_					:       List[Any]     				=					getattr(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)

     if weight_type is not None:
          lowercase_					:       Dict     				=					getattr(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							).shape
     else:
          lowercase_					:       List[str]     				=					hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
              f''' {value.shape} for {full_name}'''							)

     if weight_type == "weight":
          lowercase_					:       str     				=					value
     elif weight_type == "weight_g":
          lowercase_					:       Union[str, Any]     				=					value
     elif weight_type == "weight_v":
          lowercase_					:       List[str]     				=					value
     elif weight_type == "bias":
          lowercase_					:       List[str]     				=					value
     elif weight_type == "running_mean":
          lowercase_					:       List[Any]     				=					value
     elif weight_type == "running_var":
          lowercase_					:       List[Any]     				=					value
     elif weight_type == "num_batches_tracked":
          lowercase_					:       str     				=					value
     elif weight_type == "weight_ih_l0":
          lowercase_					:       Tuple     				=					value
     elif weight_type == "weight_hh_l0":
          lowercase_					:       Any     				=					value
     elif weight_type == "bias_ih_l0":
          lowercase_					:       str     				=					value
     elif weight_type == "bias_hh_l0":
          lowercase_					:       List[str]     				=					value
     elif weight_type == "weight_ih_l1":
          lowercase_					:       Tuple     				=					value
     elif weight_type == "weight_hh_l1":
          lowercase_					:       List[Any]     				=					value
     elif weight_type == "bias_ih_l1":
          lowercase_					:       Optional[int]     				=					value
     elif weight_type == "bias_hh_l1":
          lowercase_					:       Optional[int]     				=					value
     else:
          lowercase_					:       int     				=					value

     logger.info(f'''{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'''							)

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     for key in ignore_keys:
          if key.endswith('.*'							):
               if name.startswith(key[:-1]							):
                    return True
          elif ".*." in key:
               lowercase_					:       int     				=					key.split('.*.'							)
               if prefix in name and suffix in name:
                    return True
          elif key in name:
               return True
     return False

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       Dict     				=					[]

     if model_name == "encodec_24khz" or "encodec_32khz":
          lowercase_					:       List[str]     				=					MAPPING_24K
     elif model_name == "encodec_48khz":
          lowercase_					:       Any     				=					MAPPING_48K
     else:
          raise ValueError(f'''Unsupported model: {model_name}'''							)

     for name, value in orig_dict.items():
          if should_ignore(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
               logger.info(f'''{name} was ignored'''							)
               continue

          lowercase_					:       Dict     				=					False
          for key, mapped_key in MAPPING.items():
               if "*" in key:
                    lowercase_					:       int     				=					key.split('.*.'							)
                    if prefix in name and suffix in name:
                         lowercase_					:       Any     				=					suffix

               if key in name:
                    # HACK otherwise .embed gets initialized with .embed_avg too
                    if key.endswith('embed'							) and name.endswith('embed_avg'							):
                         continue

                    lowercase_					:       Union[str, Any]     				=					True
                    if "*" in mapped_key:
                         lowercase_					:       Tuple     				=					name.split(SCREAMING_SNAKE_CASE_							)[0].split('.'							)[-2]
                         lowercase_					:       Optional[Any]     				=					mapped_key.replace('*'  ,       SCREAMING_SNAKE_CASE_							)
                    if "weight_g" in name:
                         lowercase_					:       List[str]     				=					'weight_g'
                    elif "weight_v" in name:
                         lowercase_					:       List[Any]     				=					'weight_v'
                    elif "weight_ih_l0" in name:
                         lowercase_					:       Union[str, Any]     				=					'weight_ih_l0'
                    elif "weight_hh_l0" in name:
                         lowercase_					:       Union[str, Any]     				=					'weight_hh_l0'
                    elif "bias_ih_l0" in name:
                         lowercase_					:       Optional[Any]     				=					'bias_ih_l0'
                    elif "bias_hh_l0" in name:
                         lowercase_					:       Optional[Any]     				=					'bias_hh_l0'
                    elif "weight_ih_l1" in name:
                         lowercase_					:       List[Any]     				=					'weight_ih_l1'
                    elif "weight_hh_l1" in name:
                         lowercase_					:       Union[str, Any]     				=					'weight_hh_l1'
                    elif "bias_ih_l1" in name:
                         lowercase_					:       Optional[int]     				=					'bias_ih_l1'
                    elif "bias_hh_l1" in name:
                         lowercase_					:       Any     				=					'bias_hh_l1'
                    elif "bias" in name:
                         lowercase_					:       int     				=					'bias'
                    elif "weight" in name:
                         lowercase_					:       Any     				=					'weight'
                    elif "running_mean" in name:
                         lowercase_					:       int     				=					'running_mean'
                    elif "running_var" in name:
                         lowercase_					:       Optional[int]     				=					'running_var'
                    elif "num_batches_tracked" in name:
                         lowercase_					:       Optional[Any]     				=					'num_batches_tracked'
                    else:
                         lowercase_					:       List[str]     				=					None
                    set_recursively(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
               continue
          if not is_used:
               unused_weights.append(SCREAMING_SNAKE_CASE_							)

     logger.warning(f'''Unused weights: {unused_weights}'''							)

@torch.no_grad()
def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=None  ,       SCREAMING_SNAKE_CASE_=None  ,       ):
     if config_path is not None:
          lowercase_					:       List[Any]     				=					EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE_							)
     else:
          lowercase_					:       Optional[int]     				=					EncodecConfig()

     if model_name == "encodec_24khz":
          pass  # config is already correct
     elif model_name == "encodec_32khz":
          lowercase_					:       Tuple     				=					[8, 5, 4, 4]
          lowercase_					:       Union[str, Any]     				=					[2.2]
          lowercase_					:       List[Any]     				=					64
          lowercase_					:       Dict     				=					32_000
          lowercase_					:       List[str]     				=					2_048
          lowercase_					:       str     				=					False
          lowercase_					:       str     				=					False
          lowercase_					:       List[Any]     				=					False
     elif model_name == "encodec_48khz":
          lowercase_					:       int     				=					[8, 5, 4, 2]
          lowercase_					:       str     				=					[3.0, 6.0, 12.0, 24.0]
          lowercase_					:       Tuple     				=					48_000
          lowercase_					:       Optional[Any]     				=					2
          lowercase_					:       Dict     				=					False
          lowercase_					:       List[Any]     				=					'time_group_norm'
          lowercase_					:       int     				=					True
          lowercase_					:       List[Any]     				=					1.0
          lowercase_					:       Tuple     				=					0.01
     else:
          raise ValueError(f'''Unknown model name: {model_name}'''							)

     lowercase_					:       Any     				=					EncodecModel(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       List[str]     				=					EncodecFeatureExtractor(
         feature_size=config.audio_channels  ,       sampling_rate=config.sampling_rate  ,       chunk_length_s=config.chunk_length_s  ,       overlap=config.overlap  ,       )
     feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       List[str]     				=					torch.load(SCREAMING_SNAKE_CASE_							)
     if "best_state" in original_checkpoint:
          # we might have a training state saved, in which case discard the yaml results and just retain the weights
          lowercase_					:       Optional[int]     				=					original_checkpoint['best_state']
     recursively_load_weights(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
     model.save_pretrained(SCREAMING_SNAKE_CASE_							)

     if repo_id:
          print('Pushing to the hub...'							)
          feature_extractor.push_to_hub(SCREAMING_SNAKE_CASE_							)
          model.push_to_hub(SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
    _A								=   argparse.ArgumentParser()
    parser.add_argument(
        '--model',
        default='encodec_24khz',
        type=str,
        help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
    )
    parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
    parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
    parser.add_argument(
        '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
    )

    _A								=   parser.parse_args()
    convert_checkpoint(
        args.model,
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )





'''simple docstring'''




import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


_A								=   logging.get_logger(__name__)

_A								=   '▁'

_A								=   {'vocab_file': 'vocab.txt', 'sentencepiece_model_ckpt': 'sentencepiece.bpe.model'}

_A								=   {
    'sentencepiece_model_file': 'sentencepiece.bpe.model',
    'vocab_file': 'vocab.txt',
}

_A								=   {
    'vocab_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
    },
    'sentencepiece_model_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
    },
}

_A								=   {
    'ernie-m-base': 5_1_4,
    'ernie-m-large': 5_1_4,
}

_A								=   {
    'ernie-m-base': {'do_lower_case': False},
    'ernie-m-large': {'do_lower_case': False},
}


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       List[str]     =    ["input_ids"]

   A							:       Optional[Any]     =    VOCAB_FILES_NAMES
   A							:       Dict     =    PRETRAINED_INIT_CONFIGURATION
   A							:       int     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A							:       List[str]     =    PRETRAINED_VOCAB_FILES_MAP
   A							:       Any     =    RESOURCE_FILES_NAMES
   def __init__(self       ,				_a       ,				_a=None       ,				_a=False       ,				_a="utf8"       ,				_a="[UNK]"       ,				_a="[SEP]"       ,				_a="[PAD]"       ,				_a="[CLS]"       ,				_a="[MASK]"       ,				_a = None       ,				**_a       ,				)    ->     None:
        # Mask token behave like a normal word, i.e. include the space before it and
        # is included in the raw text, there should be a match in a non-normalized sentence.

        lowercase_					:       Optional[Any]     				=					{} if sp_model_kwargs is None else sp_model_kwargs
        super().__init__(
            do_lower_case=_a       ,				unk_token=_a       ,				sep_token=_a       ,				pad_token=_a       ,				cls_token=_a       ,				mask_token=_a       ,				vocab_file=_a       ,				encoding=_a       ,				sp_model_kwargs=self.sp_model_kwargs       ,				**_a       ,				)
        lowercase_					:       int     				=					do_lower_case
        lowercase_					:       str     				=					sentencepiece_model_ckpt
        lowercase_					:       Tuple     				=					spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(_a				)

        # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
        if vocab_file is not None:
             lowercase_					:       Union[str, Any]     				=					self.load_vocab(filepath=_a				)
        else:
             lowercase_					:       Any     				=					{self.sp_model.id_to_piece(_a				): id for id in range(self.sp_model.get_piece_size()				)}
        lowercase_					:       Optional[int]     				=					{v: k for k, v in self.vocab.items()}
   def 		_lowerCamelCase (self       ,				_a				)    ->     Any:
        if text is None:
             return None

        lowercase_					:       Dict     				=					self.tokenize(_a				)
        lowercase_				,lowercase_					:       Dict     				=					'', []

        for i, ch in enumerate(_a				):
             if ch in self.SP_CHAR_MAPPING:
                  lowercase_					:       str     				=					self.SP_CHAR_MAPPING.get(_a				)
             else:
                  lowercase_					:       List[str]     				=					unicodedata.normalize('NFKC'       ,				_a				)
             if self.is_whitespace(_a				):
                  continue
             normalized_text += ch
             char_mapping.extend([i] * len(_a				)				)

        lowercase_				,lowercase_				,lowercase_					:       int     				=					normalized_text, [], 0

        if self.do_lower_case:
             lowercase_					:       Optional[Any]     				=					text.lower()

        for token in split_tokens:
             if token[:1] == "▁":
                  lowercase_					:       Tuple     				=					token[1:]
             lowercase_					:       List[str]     				=					text[offset:].index(_a				) + offset
             lowercase_					:       Any     				=					start + len(_a				)

             token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)				)
             lowercase_					:       Tuple     				=					end
        return token_mapping
   @property
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        return len(self.vocab				)
   def 		_lowerCamelCase (self				)    ->     int:
        return dict(self.vocab       ,				**self.added_tokens_encoder				)
   def __getstate__(self				)    ->     Optional[Any]:
        lowercase_					:       Dict     				=					self.__dict__.copy()
        lowercase_					:       List[str]     				=					None
        return state
   def __setstate__(self       ,				_a				)    ->     Any:
        lowercase_					:       Optional[int]     				=					d

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

        lowercase_					:       Optional[int]     				=					spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(self.sentencepiece_model_ckpt				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     str:
        return "".join((self.SP_CHAR_MAPPING.get(_a       ,				_a				) for c in text)				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a=False       ,				_a=64       ,				_a=0.1				)    ->     Optional[int]:

        if self.sp_model_kwargs.get('enable_sampling'				) is True:
             lowercase_					:       Optional[int]     				=					True
        if self.sp_model_kwargs.get('alpha'				) is not None:
             lowercase_					:       Union[str, Any]     				=					self.sp_model_kwargs.get('alpha'				)
        if self.sp_model_kwargs.get('nbest_size'				) is not None:
             lowercase_					:       Optional[int]     				=					self.sp_model_kwargs.get('nbest_size'				)

        if not enable_sampling:
             lowercase_					:       Optional[Any]     				=					self.sp_model.EncodeAsPieces(_a				)
        else:
             lowercase_					:       List[Any]     				=					self.sp_model.SampleEncodeAsPieces(_a       ,				_a       ,				_a				)
        lowercase_					:       int     				=					[]
        for pi, piece in enumerate(_a				):
             if piece == SPIECE_UNDERLINE:
                  if not pieces[pi + 1].startswith(_a				) and pi != 0:
                       new_pieces.append(_a				)
                       continue
                  else:
                       continue
             lowercase_					:       Optional[Any]     				=					0
             for i, chunk in enumerate(_a				):
                  if chunk == SPIECE_UNDERLINE:
                       continue
                  if self.is_ch_char(_a				) or self.is_punct(_a				):
                       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                            new_pieces.append(piece[lst_i:i]				)
                       new_pieces.append(_a				)
                       lowercase_					:       List[Any]     				=					i + 1
                  elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                            new_pieces.append(piece[lst_i:i]				)
                       lowercase_					:       Optional[Any]     				=					i
                  elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                            new_pieces.append(piece[lst_i:i]				)
                       lowercase_					:       int     				=					i
             if len(_a				) > lst_i:
                  new_pieces.append(piece[lst_i:]				)
        return new_pieces
   def 		_lowerCamelCase (self       ,				_a				)    ->     Union[str, Any]:
        lowercase_					:       Optional[Any]     				=					''.join(_a				).replace(_a       ,				' '				).strip()
        return out_string
   def 		_lowerCamelCase (self       ,				_a				)    ->     Optional[int]:
        lowercase_					:       Optional[Any]     				=					self.convert_ids_to_tokens(_a				)
        lowercase_					:       Tuple     				=					''.join(_a				).replace(_a       ,				' '				).strip()
        return out_string
   def 		_lowerCamelCase (self       ,				_a				)    ->     Optional[int]:
        return self.vocab.get(_a       ,				self.vocab.get(self.unk_token				)				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     int:
        return self.reverse_vocab.get(_a       ,				self.unk_token				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a=None				)    ->     Any:
        if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        lowercase_					:       Tuple     				=					[self.cls_token_id]
        lowercase_					:       int     				=					[self.sep_token_id]
        return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
   def 		_lowerCamelCase (self       ,				_a       ,				_a=None				)    ->     Optional[Any]:
        if offset_mapping_a is None:
             return [(0, 0)] + offset_mapping_a + [(0, 0)]

        return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
   def 		_lowerCamelCase (self       ,				_a       ,				_a=None       ,				_a=False				)    ->     Union[str, Any]:

        if already_has_special_tokens:
             if token_ids_a is not None:
                  raise ValueError(
                      'You should not supply a second sequence if the provided sequence of '
                      'ids is already formatted with special tokens for the model.'				)
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

        if token_ids_a is not None:
             return [1] + ([0] * len(_a				)) + [1, 1] + ([0] * len(_a				)) + [1]
        return [1] + ([0] * len(_a				)) + [1]
   def 		_lowerCamelCase (self       ,				_a       ,				_a = None				)    ->     List[int]:
        # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
        if token_ids_a is None:
             # [CLS] X [SEP]
             return (len(_a				) + 2) * [0]

        # [CLS] A [SEP] [SEP] B [SEP]
        return [0] * (len(_a				) + 1) + [1] * (len(_a				) + 3)
   def 		_lowerCamelCase (self       ,				_a				)    ->     int:
        if "\u4e00" <= char <= "\u9fff":
             return True
        return False
   def 		_lowerCamelCase (self       ,				_a				)    ->     List[str]:
        if ("a" <= char <= "z") or ("A" <= char <= "Z"):
             return True
        return False
   def 		_lowerCamelCase (self       ,				_a				)    ->     int:
        if char in ",;:.?!~，；：。？！《》【】":
             return True
        return False
   def 		_lowerCamelCase (self       ,				_a				)    ->     List[Any]:
        if char == " " or char == "\t" or char == "\n" or char == "\r":
             return True
        if len(_a				) == 1:
             lowercase_					:       Optional[Any]     				=					unicodedata.category(_a				)
             if cat == "Zs":
                  return True
        return False
   def 		_lowerCamelCase (self       ,				_a				)    ->     Tuple:
        lowercase_					:       List[Any]     				=					{}
        with io.open(_a       ,				'r'       ,				encoding='utf-8'				) as f:
             for index, line in enumerate(_a				):
                  lowercase_					:       Dict     				=					line.rstrip('\n'				)
                  lowercase_					:       Dict     				=					int(_a				)

        return token_to_idx





   def 		_lowerCamelCase (self       ,				_a       ,				_a = None				)    ->     Tuple[str]:
        lowercase_					:       Tuple     				=					0
        if os.path.isdir(_a				):
             lowercase_					:       Any     				=					os.path.join(
                 _a       ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
        else:
             lowercase_					:       Tuple     				=					(filename_prefix + '-' if filename_prefix else '') + save_directory
        with open(_a       ,				'w'       ,				encoding='utf-8'				) as writer:
             for token, token_index in sorted(self.vocab.items()       ,				key=lambda _a				: kv[1]				):
                  if index != token_index:
                       logger.warning(
                           f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                           ' Please check that the vocabulary is not corrupted!'				)
                       lowercase_					:       Tuple     				=					token_index
                  writer.write(token + '\n'				)
                  index += 1

        lowercase_					:       List[str]     				=					os.path.join(_a       ,				'sentencepiece.bpe.model'				)
        with open(_a       ,				'wb'				) as fi:
             lowercase_					:       Tuple     				=					self.sp_model.serialized_model_proto()
             fi.write(_a				)

        return (vocab_file,)




