


import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase : Union[str, Any] 			=				logging.get_logger(__name__)


__UpperCAmelCase : Optional[int] 			=				"https://openaipublic.azureedge.net/jukebox/models/"
__UpperCAmelCase : Optional[Any] 			=				{
    "jukebox-1b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "1b_lyrics/prior_level_2.pth.tar",
    ],
    "jukebox-5b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "5b_lyrics/prior_level_2.pth.tar",
    ],
}


def      a	(							SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       if key.endswith('''.model.1.bias'''       ) and len(key.split('''.'''       )       ) > 1_0:
              UpperCamelCase    :	Any   	=		key.replace('''.model.1.bias'''							,			'''.conv1d_1.bias'''       )
       elif key.endswith('''.model.1.weight'''       ) and len(key.split('''.'''       )       ) > 1_0:
              UpperCamelCase    :	Optional[int]   	=		key.replace('''.model.1.weight'''							,			'''.conv1d_1.weight'''       )
       elif key.endswith('''.model.3.bias'''       ) and len(key.split('''.'''       )       ) > 1_0:
              UpperCamelCase    :	int   	=		key.replace('''.model.3.bias'''							,			'''.conv1d_2.bias'''       )
       elif key.endswith('''.model.3.weight'''       ) and len(key.split('''.'''       )       ) > 1_0:
              UpperCamelCase    :	Union[str, Any]   	=		key.replace('''.model.3.weight'''							,			'''.conv1d_2.weight'''       )

       if "conditioner_blocks.0." in key:
              UpperCamelCase    :	Union[str, Any]   	=		key.replace('''conditioner_blocks.0'''							,			'''conditioner_blocks'''       )

       if "prime_prior" in key:
              UpperCamelCase    :	Any   	=		key.replace('''prime_prior'''							,			'''encoder'''       )

       if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
              UpperCamelCase    :	Union[str, Any]   	=		key.replace('''.emb.'''							,			'''.'''       )

       if key.endswith('''k'''       ):  # replace vqvae.X.k with vqvae.X.codebook
              return key.replace('''.k'''							,			'''.codebook'''       )
       if "y_emb." in key:
              return key.replace('''y_emb.'''							,			'''metadata_embedding.'''       )

       if "x_emb.emb." in key:
              UpperCamelCase    :	str   	=		key.replace('''0.x_emb.emb'''							,			'''embed_tokens'''       )

       if "prime_state_ln" in key:
              return key.replace('''prime_state_ln'''							,			'''encoder.final_layer_norm'''       )
       if ".ln" in key:
              return key.replace('''.ln'''							,			'''.layer_norm'''       )
       if "_ln" in key:
              return key.replace('''_ln'''							,			'''_layer_norm'''       )

       if "prime_state_proj" in key:
              return key.replace('''prime_state_proj'''							,			'''encoder.proj_in'''       )
       if "prime_x_out" in key:
              return key.replace('''prime_x_out'''							,			'''encoder.lm_head'''       )
       if "prior.x_out" in key:
              return key.replace('''x_out'''							,			'''fc_proj_out'''       )
       if "x_emb" in key:
              return key.replace('''x_emb'''							,			'''embed_tokens'''       )

       return key


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[Any]							,			SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		Tuple       ):






       """simple docstring"""





       UpperCamelCase    :	str   	=		{}
       import re

       UpperCamelCase    :	int   	=		re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''       )
       UpperCamelCase    :	Any   	=		re.compile(
           R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''       )
       UpperCamelCase    :	List[str]   	=		re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''       )

       UpperCamelCase    :	Dict   	=		re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''       )
       UpperCamelCase    :	Union[str, Any]   	=		re.compile(
           R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''       )
       UpperCamelCase    :	Optional[Any]   	=		re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''       )

       UpperCamelCase    :	Optional[int]   	=		re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''       )
       UpperCamelCase    :	Tuple   	=		re.compile(
           R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''       )
       UpperCamelCase    :	Any   	=		re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'''       )

       for original_key, value in state_dict.items():
              # rename vqvae.encoder keys
              if re_encoder_block_conv_in.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Dict   	=		re_encoder_block_conv_in.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	List[Any]   	=		regex_match.groups()
                     UpperCamelCase    :	Union[str, Any]   	=		int(groups[2]       ) * 2 + int(groups[3]       )
                     UpperCamelCase    :	Dict   	=		F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
                     UpperCamelCase    :	str   	=		re_encoder_block_conv_in.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              elif re_encoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Dict   	=		re_encoder_block_resnet.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	List[Any]   	=		regex_match.groups()
                     UpperCamelCase    :	int   	=		int(groups[2]       ) * 2 + int(groups[3]       )
                     UpperCamelCase    :	Any   	=		{'''1''': 1, '''3''': 2}[groups[-2]]
                     UpperCamelCase    :	List[Any]   	=		F"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
                     UpperCamelCase    :	Any   	=		F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
                     UpperCamelCase    :	Any   	=		prefix + resnet_block
                     UpperCamelCase    :	List[str]   	=		re_encoder_block_resnet.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              elif re_encoder_block_proj_out.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Union[str, Any]   	=		re_encoder_block_proj_out.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	Union[str, Any]   	=		regex_match.groups()
                     UpperCamelCase    :	Tuple   	=		F"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
                     UpperCamelCase    :	str   	=		re_encoder_block_proj_out.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              # rename vqvae.decoder keys
              elif re_decoder_block_conv_out.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Dict   	=		re_decoder_block_conv_out.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	List[Any]   	=		regex_match.groups()
                     UpperCamelCase    :	Optional[Any]   	=		int(groups[2]       ) * 2 + int(groups[3]       ) - 2
                     UpperCamelCase    :	Optional[Any]   	=		F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
                     UpperCamelCase    :	List[str]   	=		re_decoder_block_conv_out.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              elif re_decoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Optional[Any]   	=		re_decoder_block_resnet.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	List[Any]   	=		regex_match.groups()
                     UpperCamelCase    :	Any   	=		int(groups[2]       ) * 2 + int(groups[3]       ) - 2
                     UpperCamelCase    :	int   	=		{'''1''': 1, '''3''': 2}[groups[-2]]
                     UpperCamelCase    :	str   	=		F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
                     UpperCamelCase    :	Tuple   	=		F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
                     UpperCamelCase    :	str   	=		prefix + resnet_block
                     UpperCamelCase    :	Optional[Any]   	=		re_decoder_block_resnet.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              elif re_decoder_block_proj_in.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Optional[int]   	=		re_decoder_block_proj_in.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	List[str]   	=		regex_match.groups()
                     UpperCamelCase    :	List[str]   	=		F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
                     UpperCamelCase    :	Optional[int]   	=		re_decoder_block_proj_in.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              # rename prior cond.model to upsampler.upsample_block and resnet
              elif re_prior_cond_conv_out.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Dict   	=		re_prior_cond_conv_out.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	Optional[int]   	=		regex_match.groups()
                     UpperCamelCase    :	str   	=		int(groups[1]       ) * 2 + int(groups[2]       ) - 2
                     UpperCamelCase    :	Optional[Any]   	=		F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
                     UpperCamelCase    :	int   	=		re_prior_cond_conv_out.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              elif re_prior_cond_resnet.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	Union[str, Any]   	=		re_prior_cond_resnet.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	Tuple   	=		regex_match.groups()
                     UpperCamelCase    :	List[Any]   	=		int(groups[1]       ) * 2 + int(groups[2]       ) - 2
                     UpperCamelCase    :	Optional[int]   	=		{'''1''': 1, '''3''': 2}[groups[-2]]
                     UpperCamelCase    :	str   	=		F"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
                     UpperCamelCase    :	Optional[int]   	=		F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
                     UpperCamelCase    :	Optional[Any]   	=		prefix + resnet_block
                     UpperCamelCase    :	List[str]   	=		re_prior_cond_resnet.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              elif re_prior_cond_proj_in.fullmatch(SCREAMING_SNAKE_CASE_       ):
                     UpperCamelCase    :	List[Any]   	=		re_prior_cond_proj_in.match(SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	Optional[Any]   	=		regex_match.groups()
                     UpperCamelCase    :	int   	=		F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
                     UpperCamelCase    :	Optional[Any]   	=		re_prior_cond_proj_in.sub(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

              # keep original key
              else:
                     UpperCamelCase    :	List[Any]   	=		original_key

              UpperCamelCase    :	List[str]   	=		replace_key(SCREAMING_SNAKE_CASE_       )

              if F"""{key_prefix}.{key}""" not in model_state_dict or key is None:
                     print(F"""failed converting {original_key} to {key}, does not match"""       )

              # handle missmatched shape
              elif value.shape != model_state_dict[F"""{key_prefix}.{key}"""].shape:
                     UpperCamelCase    :	Optional[int]   	=		model_state_dict[F"""{key_prefix}.{key}"""]
                     print(F"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""       )
                     UpperCamelCase    :	List[Any]   	=		original_key

              UpperCamelCase    :	str   	=		original_key
              UpperCamelCase    :	List[str]   	=		value

       return new_dict


@torch.no_grad()
def      a	(							SCREAMING_SNAKE_CASE_      :		Any=None							,			SCREAMING_SNAKE_CASE_      :		Dict=None       ):






       """simple docstring"""





       for file in MODEL_MAPPING[model_name]:
              if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split("/"       )[-1]}"""       ):
                     UpperCamelCase    :	Tuple   	=		requests.get(F"""{PREFIX}{file}"""							,			allow_redirects=SCREAMING_SNAKE_CASE_       )
                     os.makedirs(F"""{pytorch_dump_folder_path}/"""							,			exist_ok=SCREAMING_SNAKE_CASE_       )
                     open(F"""{pytorch_dump_folder_path}/{file.split("/"       )[-1]}"""							,			'''wb'''       ).write(r.content       )

       UpperCamelCase    :	int   	=		MODEL_MAPPING[model_name.split('''/'''       )[-1]]

       UpperCamelCase    :	Optional[Any]   	=		JukeboxConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	str   	=		JukeboxModel(SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	Tuple   	=		[]
       UpperCamelCase    :	Optional[Any]   	=		{}
       for i, dict_name in enumerate(SCREAMING_SNAKE_CASE_       ):
              UpperCamelCase    :	List[str]   	=		torch.load(F"""{pytorch_dump_folder_path}/{dict_name.split("/"       )[-1]}"""       )['''model''']

              UpperCamelCase    :	int   	=		{}
              for k in old_dic.keys():
                     if k.endswith('''.b'''       ):
                            UpperCamelCase    :	List[Any]   	=		old_dic[k]
                     elif k.endswith('''.w'''       ):
                            UpperCamelCase    :	Any   	=		old_dic[k]
                     elif "level_2" not in dict_name and "cond.model." in k:
                            UpperCamelCase    :	Optional[int]   	=		old_dic[k]
                     else:
                            UpperCamelCase    :	List[str]   	=		old_dic[k]

              UpperCamelCase    :	Union[str, Any]   	=		'''vqvae''' if i == 0 else F"""priors.{3 - i}"""
              UpperCamelCase    :	Optional[Any]   	=		fix_jukebox_keys(SCREAMING_SNAKE_CASE_							,			model.state_dict()							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
              weight_dict.append(SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	List[str]   	=		weight_dict.pop(0       )
       model.vqvae.load_state_dict(SCREAMING_SNAKE_CASE_       )
       for i in range(len(SCREAMING_SNAKE_CASE_       )       ):
              model.priors[i].load_state_dict(weight_dict[2 - i]       )

       Path(SCREAMING_SNAKE_CASE_       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_       )
       with open(F"""{pytorch_dump_folder_path}/mapping.json"""							,			'''w'''       ) as txtfile:
              json.dump(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

       print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""       )
       model.save_pretrained(SCREAMING_SNAKE_CASE_       )

       return weight_dict


if __name__ == "__main__":
  __UpperCAmelCase : Optional[int] 			=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="jukebox-5b-lyrics",
      type=str,
      help="Name of the model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default="jukebox-5b-lyrics-converted",
      type=str,
      help="Path to the output PyTorch model directory.",
  )
  __UpperCAmelCase : Tuple 			=				parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__UpperCAmelCase : Dict 			=				logging.get_logger(__name__)

__UpperCAmelCase : Optional[Any] 			=				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase : Dict 			=				{
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

__UpperCAmelCase : Tuple 			=				{
    "junnyu/roformer_chinese_small": 1536,
    "junnyu/roformer_chinese_base": 1536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


__UpperCAmelCase : Any 			=				{
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     str							=					VOCAB_FILES_NAMES
       __UpperCamelCase      :     Optional[int]							=					PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase      :     Union[str, Any]							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase      :     int							=					PRETRAINED_INIT_CONFIGURATION
       __UpperCamelCase      :     Any							=					RoFormerTokenizer






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE="[UNK]" ,					__SCREAMING_SNAKE_CASE="[SEP]" ,					__SCREAMING_SNAKE_CASE="[PAD]" ,					__SCREAMING_SNAKE_CASE="[CLS]" ,					__SCREAMING_SNAKE_CASE="[MASK]" ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              super().__init__(
                  __SCREAMING_SNAKE_CASE ,					tokenizer_file=__SCREAMING_SNAKE_CASE ,					do_lower_case=__SCREAMING_SNAKE_CASE ,					unk_token=__SCREAMING_SNAKE_CASE ,					sep_token=__SCREAMING_SNAKE_CASE ,					pad_token=__SCREAMING_SNAKE_CASE ,					cls_token=__SCREAMING_SNAKE_CASE ,					mask_token=__SCREAMING_SNAKE_CASE ,					tokenize_chinese_chars=__SCREAMING_SNAKE_CASE ,					strip_accents=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE ,					)

              UpperCamelCase    :	List[Any]   	=		json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
              if (
                  pre_tok_state.get('''lowercase''' ,					__SCREAMING_SNAKE_CASE						) != do_lower_case
                  or pre_tok_state.get('''strip_accents''' ,					__SCREAMING_SNAKE_CASE						) != strip_accents
              ):
                     UpperCamelCase    :	List[Any]   	=		getattr(__SCREAMING_SNAKE_CASE ,					pre_tok_state.pop('''type'''						)						)
                     UpperCamelCase    :	Optional[int]   	=		do_lower_case
                     UpperCamelCase    :	Optional[Any]   	=		strip_accents
                     UpperCamelCase    :	List[Any]   	=		pre_tok_class(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[Any]   	=		do_lower_case






       def __getstate__(     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		self.__dict__.copy()
              UpperCamelCase    :	Any   	=		BertPreTokenizer()
              return state






       def __setstate__(     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		d
              UpperCamelCase    :	List[str]   	=		self.__dict__['''_tokenizer'''].get_vocab()
              UpperCamelCase    :	Any   	=		PreTokenizer.custom(JiebaPreTokenizer(__SCREAMING_SNAKE_CASE						)						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

              if token_ids_a:
                     output += token_ids_a + [self.sep_token_id]

              return output






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		[self.sep_token_id]
              UpperCamelCase    :	int   	=		[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						) * [0] + len(token_ids_a + sep						) * [1]






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		self._tokenizer.model.save(__SCREAMING_SNAKE_CASE ,					name=__SCREAMING_SNAKE_CASE						)
              return tuple(__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=False ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		BertPreTokenizer()
              return super().save_pretrained(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)

