






"""simple docstring"""



import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
A_    =							logging.get_logger(__name__)

set_seed(770)


A_    =							{
    '''c_attn''': '''att_proj''',
    '''c_proj''': '''out_proj''',
    '''c_fc''': '''in_proj''',
    '''transformer.''': '''''',
    '''h.''': '''layers.''',
    '''ln_1''': '''layernorm_1''',
    '''ln_2''': '''layernorm_2''',
    '''ln_f''': '''layernorm_final''',
    '''wpe''': '''position_embeds_layer''',
    '''wte''': '''input_embeds_layer''',
}


A_    =							{
    '''text_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text.pt''',
    },
    '''coarse_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse.pt''',
    },
    '''fine_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine.pt''',
    },
    '''text''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text_2.pt''',
    },
    '''coarse''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse_2.pt''',
    },
    '''fine''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine_2.pt''',
    },
}

A_    =							os.path.dirname(os.path.abspath(__file__))
A_    =							os.path.join(os.path.expanduser('''~'''), '''.cache''')
A_    =							os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					Optional[Any]=False      )		->Dict:
						A__						:  Optional[int]		  =  model_type
						if use_small:
												key += "_small"
						return os.path.join(UpperCAmelCase__,						REMOTE_MODEL_PATHS[key]["""file_name"""]      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Any      )		->str:
						os.makedirs(UpperCAmelCase__,						exist_ok=UpperCAmelCase__      )
						hf_hub_download(repo_id=UpperCAmelCase__,						filename=UpperCAmelCase__,						local_dir=UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Dict=False,						UpperCAmelCase__       :					Tuple="text"      )		->Optional[Any]:
						if model_type == "text":
												A__						:  str		  =  BarkSemanticModel
												A__						:  int		  =  BarkSemanticConfig
												A__						:  Union[str, Any]		  =  BarkSemanticGenerationConfig
						elif model_type == "coarse":
												A__						:  Dict		  =  BarkCoarseModel
												A__						:  Optional[Any]		  =  BarkCoarseConfig
												A__						:  List[Any]		  =  BarkCoarseGenerationConfig
						elif model_type == "fine":
												A__						:  List[Any]		  =  BarkFineModel
												A__						:  List[str]		  =  BarkFineConfig
												A__						:  Union[str, Any]		  =  BarkFineGenerationConfig
						else:
												raise NotImplementedError()
						A__						:  Optional[Any]		  =  f'{model_type}_small' if use_small else model_type
						A__						:  Optional[Any]		  =  REMOTE_MODEL_PATHS[model_key]
						if not os.path.exists(UpperCAmelCase__      ):
												logger.info(f'{model_type} model not found, downloading into `{CACHE_DIR}`.'      )
												_download(model_info["""repo_id"""],						model_info["""file_name"""]      )
						A__						:  List[Any]		  =  torch.load(UpperCAmelCase__,						map_location=UpperCAmelCase__      )
						# this is a hack
						A__						:  Optional[int]		  =  checkpoint["""model_args"""]
						if "input_vocab_size" not in model_args:
												A__						:  int		  =  model_args["""vocab_size"""]
												A__						:  Dict		  =  model_args["""vocab_size"""]
												del model_args["vocab_size"]

						# convert Bark model arguments to HF Bark model arguments
						A__						:  List[str]		  =  model_args.pop("""n_head"""      )
						A__						:  List[Any]		  =  model_args.pop("""n_embd"""      )
						A__						:  Optional[Any]		  =  model_args.pop("""n_layer"""      )

						A__						:  Union[str, Any]		  =  ConfigClass(**checkpoint["""model_args"""]      )
						A__						:  Any		  =  ModelClass(config=UpperCAmelCase__      )
						A__						:  Optional[Any]		  =  GenerationConfigClass()

						A__						:  str		  =  model_generation_config
						A__						:  Optional[int]		  =  checkpoint["""model"""]
						# fixup checkpoint
						A__						:  Optional[int]		  =  """_orig_mod."""
						for k, v in list(state_dict.items()      ):
												if k.startswith(UpperCAmelCase__      ):
																		# replace part of the key with corresponding layer name in HF implementation
																		A__						:  str		  =  k[len(UpperCAmelCase__      ) :]
																		for old_layer_name in new_layer_name_dict:
																								A__						:  Optional[Any]		  =  new_k.replace(UpperCAmelCase__,						new_layer_name_dict[old_layer_name]      )

																		A__						:  List[str]		  =  state_dict.pop(UpperCAmelCase__      )

						A__						:  List[str]		  =  set(state_dict.keys()      ) - set(model.state_dict().keys()      )
						A__						:  List[Any]		  =  {k for k in extra_keys if not k.endswith(""".attn.bias"""      )}
						A__						:  List[str]		  =  set(model.state_dict().keys()      ) - set(state_dict.keys()      )
						A__						:  int		  =  {k for k in missing_keys if not k.endswith(""".attn.bias"""      )}
						if len(UpperCAmelCase__      ) != 0:
												raise ValueError(f'extra keys found: {extra_keys}'      )
						if len(UpperCAmelCase__      ) != 0:
												raise ValueError(f'missing keys: {missing_keys}'      )
						model.load_state_dict(UpperCAmelCase__,						strict=UpperCAmelCase__      )
						A__						:  List[Any]		  =  model.num_parameters(exclude_embeddings=UpperCAmelCase__      )
						A__						:  List[str]		  =  checkpoint["""best_val_loss"""].item()
						logger.info(f'model loaded: {round(n_params/1e6,						1      )}M params, {round(UpperCAmelCase__,						3      )} loss'      )
						model.eval()
						model.to(UpperCAmelCase__      )
						del checkpoint, state_dict

						return model




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					Any=False,						UpperCAmelCase__       :					List[str]="text"      )		->Optional[Any]:
						if model_type not in ("text", "coarse", "fine"):
												raise NotImplementedError()

						A__						:  Union[str, Any]		  =  """cpu"""  # do conversion on cpu

						A__						:  Optional[int]		  =  _get_ckpt_path(UpperCAmelCase__,						use_small=UpperCAmelCase__      )
						A__						:  str		  =  _load_model(UpperCAmelCase__,						UpperCAmelCase__,						model_type=UpperCAmelCase__,						use_small=UpperCAmelCase__      )

						# load bark initial model
						A__						:  Optional[int]		  =  _bark_load_model(UpperCAmelCase__,						"""cpu""",						model_type=UpperCAmelCase__,						use_small=UpperCAmelCase__      )

						if model_type == "text":
												A__						:  Union[str, Any]		  =  bark_model["""model"""]

						if model.num_parameters(exclude_embeddings=UpperCAmelCase__      ) != bark_model.get_num_params():
												raise ValueError("""initial and new models don't have the same number of parameters"""      )

						# check if same output as the bark model
						A__						:  Any		  =  5
						A__						:  int		  =  1_0

						if model_type in ["text", "coarse"]:
												A__						:  int		  =  torch.randint(2_5_6,						(batch_size, sequence_length),						dtype=torch.int      )
												A__						:  Dict		  =  bark_model(UpperCAmelCase__      )[0]

												A__						:  List[Any]		  =  model(UpperCAmelCase__      )

												# take last logits
												A__						:  Any		  =  output_new_model_total.logits[:, [-1], :]

						else:
												A__						:  int		  =  3
												A__						:  int		  =  8
												A__						:  Any		  =  torch.randint(2_5_6,						(batch_size, sequence_length, n_codes_total),						dtype=torch.int      )

												A__						:  List[str]		  =  model(UpperCAmelCase__,						UpperCAmelCase__      )
												A__						:  Optional[int]		  =  bark_model(UpperCAmelCase__,						UpperCAmelCase__      )

												A__						:  Optional[Any]		  =  output_new_model_total.logits

						# output difference should come from the difference of self-attention implementation design
						if output_new_model.shape != output_old_model.shape:
												raise ValueError("""initial and new outputs don't have the same shape"""      )
						if (output_new_model - output_old_model).abs().max().item() > 1e-3:
												raise ValueError("""initial and new outputs are not equal"""      )

						Path(UpperCAmelCase__      ).mkdir(exist_ok=UpperCAmelCase__      )
						model.save_pretrained(UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					Dict,						)		->List[str]:
						A__						:  List[Any]		  =  os.path.join(UpperCAmelCase__,						UpperCAmelCase__      )

						A__						:  str		  =  BarkSemanticConfig.from_pretrained(os.path.join(UpperCAmelCase__,						"""config.json"""      )      )
						A__						:  int		  =  BarkCoarseConfig.from_pretrained(os.path.join(UpperCAmelCase__,						"""config.json"""      )      )
						A__						:  Optional[Any]		  =  BarkFineConfig.from_pretrained(os.path.join(UpperCAmelCase__,						"""config.json"""      )      )
						A__						:  Any		  =  EncodecConfig.from_pretrained("""facebook/encodec_24khz"""      )

						A__						:  int		  =  BarkSemanticModel.from_pretrained(UpperCAmelCase__      )
						A__						:  str		  =  BarkCoarseModel.from_pretrained(UpperCAmelCase__      )
						A__						:  Any		  =  BarkFineModel.from_pretrained(UpperCAmelCase__      )
						A__						:  Dict		  =  EncodecModel.from_pretrained("""facebook/encodec_24khz"""      )

						A__						:  Union[str, Any]		  =  BarkConfig.from_sub_model_configs(
						    UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )

						A__						:  Dict		  =  BarkGenerationConfig.from_sub_model_configs(
						    semantic.generation_config,						coarseAcoustic.generation_config,						fineAcoustic.generation_config      )

						A__						:  Optional[Any]		  =  BarkModel(UpperCAmelCase__      )

						A__						:  Tuple		  =  semantic
						A__						:  int		  =  coarseAcoustic
						A__						:  Dict		  =  fineAcoustic
						A__						:  Union[str, Any]		  =  codec

						A__						:  Optional[Any]		  =  bark_generation_config

						Path(UpperCAmelCase__      ).mkdir(exist_ok=UpperCAmelCase__      )
						bark.save_pretrained(UpperCAmelCase__,						repo_id=UpperCAmelCase__,						push_to_hub=UpperCAmelCase__      )


if __name__ == "__main__":
						A_    =							argparse.ArgumentParser()
						# Required parameters

						parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

						A_    =							parser.parse_args()

						load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)














"""simple docstring"""



import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


A_    =							logging.get_logger(__name__)


A_    =							{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

A_    =							{
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
}

A_    =							{
    '''allenai/longformer-base-4096''': 4096,
    '''allenai/longformer-large-4096''': 4096,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      _lowerCAmelCase		(							)		->List[str]:
						A__						:  Tuple		  =  (
						    list(range(ord("""!"""      ),						ord("""~"""      ) + 1      )      ) + list(range(ord("""¡"""      ),						ord("""¬"""      ) + 1      )      ) + list(range(ord("""®"""      ),						ord("""ÿ"""      ) + 1      )      )
						)
						A__						:  int		  =  bs[:]
						A__						:  Optional[Any]		  =  0
						for b in range(2**8      ):
												if b not in bs:
																		bs.append(UpperCAmelCase__      )
																		cs.append(2**8 + n      )
																		n += 1
						A__						:  Optional[Any]		  =  [chr(UpperCAmelCase__      ) for n in cs]
						return dict(zip(UpperCAmelCase__,						UpperCAmelCase__      )      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->Tuple:
						A__						:  Tuple		  =  set()
						A__						:  Tuple		  =  word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)      )
												A__						:  str		  =  char
						return pairs






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							VOCAB_FILES_NAMES
							snake_case_     =							PRETRAINED_VOCAB_FILES_MAP
							snake_case_     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_     =							['input_ids', 'attention_mask']


							def __init__(			self      :						List[str]			,    snake_case      :						List[str]			,    snake_case      :						str			,    snake_case      :						Dict="replace"			,    snake_case      :						Optional[int]="<s>"			,    snake_case      :						Any="</s>"			,    snake_case      :						List[Any]="</s>"			,    snake_case      :						str="<s>"			,    snake_case      :						Tuple="<unk>"			,    snake_case      :						List[str]="<pad>"			,    snake_case      :						Optional[Any]="<mask>"			,    snake_case      :						Tuple=False			,    **snake_case      :						Dict			,    ):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else bos_token
													A__						:  Optional[Any]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else eos_token
													A__						:  int		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else sep_token
													A__						:  str		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else cls_token
													A__						:  Optional[Any]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else unk_token
													A__						:  Optional[int]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else pad_token

													# Mask token behave like a normal word, i.e. include the space before it
													A__						:  Union[str, Any]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else mask_token

													super().__init__(
													    errors=snake_case			,    bos_token=snake_case			,    eos_token=snake_case			,    unk_token=snake_case			,    sep_token=snake_case			,    cls_token=snake_case			,    pad_token=snake_case			,    mask_token=snake_case			,    add_prefix_space=snake_case			,    **snake_case			,    )

													with open(snake_case			,    encoding="""utf-8"""							) as vocab_handle:
																			A__						:  Tuple		  =  json.load(snake_case							)
													A__						:  Dict		  =  {v: k for k, v in self.encoder.items()}
													A__						:  Dict		  =  errors  # how to handle errors in decoding
													A__						:  Dict		  =  bytes_to_unicode()
													A__						:  Optional[int]		  =  {v: k for k, v in self.byte_encoder.items()}
													with open(snake_case			,    encoding="""utf-8"""							) as merges_handle:
																			A__						:  List[str]		  =  merges_handle.read().split("""\n"""							)[1:-1]
													A__						:  Dict		  =  [tuple(merge.split()							) for merge in bpe_merges]
													A__						:  Union[str, Any]		  =  dict(zip(snake_case			,    range(len(snake_case							)							)							)							)
													A__						:  Tuple		  =  {}
													A__						:  Optional[int]		  =  add_prefix_space

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													A__						:  Union[str, Any]		  =  re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)


							@property
							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													return len(self.encoder							)


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													return dict(self.encoder			,    **self.added_tokens_encoder							)


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						Tuple							):
													'''simple docstring'''





													if token in self.cache:
																			return self.cache[token]
													A__						:  str		  =  tuple(snake_case							)
													A__						:  Any		  =  get_pairs(snake_case							)

													if not pairs:
																			return token

													while True:
																			A__						:  int		  =  min(snake_case			,    key=lambda snake_case							: self.bpe_ranks.get(snake_case			,    float("""inf"""							)							)							)
																			if bigram not in self.bpe_ranks:
																									break
																			A__  ,  A__						:  int		  =  bigram
																			A__						:  List[str]		  =  []
																			A__						:  Dict		  =  0
																			while i < len(snake_case							):
																									try:
																															A__						:  int		  =  word.index(snake_case			,    snake_case							)
																									except ValueError:
																															new_word.extend(word[i:]							)
																															break
																									else:
																															new_word.extend(word[i:j]							)
																															A__						:  str		  =  j

																									if word[i] == first and i < len(snake_case							) - 1 and word[i + 1] == second:
																															new_word.append(first + second							)
																															i += 2
																									else:
																															new_word.append(word[i]							)
																															i += 1
																			A__						:  Any		  =  tuple(snake_case							)
																			A__						:  Optional[Any]		  =  new_word
																			if len(snake_case							) == 1:
																									break
																			else:
																									A__						:  int		  =  get_pairs(snake_case							)
													A__						:  Dict		  =  """ """.join(snake_case							)
													A__						:  int		  =  word
													return word


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						List[str]							):
													'''simple docstring'''





													A__						:  int		  =  []
													for token in re.findall(self.pat			,    snake_case							):
																			A__						:  Union[str, Any]		  =  """""".join(
																			    self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																			bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case							).split(""" """							)							)
													return bpe_tokens


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						Dict							):
													'''simple docstring'''





													return self.encoder.get(snake_case			,    self.encoder.get(self.unk_token							)							)


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						int							):
													'''simple docstring'''





													return self.decoder.get(snake_case							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  """""".join(snake_case							)
													A__						:  Optional[Any]		  =  bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""			,    errors=self.errors							)
													return text


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						str			,    snake_case      :						Optional[str] = None							):
													'''simple docstring'''





													if not os.path.isdir(snake_case							):
																			logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																			return
													A__						:  Any		  =  os.path.join(
													    snake_case			,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
													A__						:  int		  =  os.path.join(
													    snake_case			,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

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

													A__						:  Optional[int]		  =  0
													with open(snake_case			,    """w"""			,    encoding="""utf-8"""							) as writer:
																			writer.write("""#version: 0.2\n"""							)
																			for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,    key=lambda snake_case							: kv[1]							):
																									if index != token_index:
																															logger.warning(
																															    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																															    """ Please check that the tokenizer is not corrupted!"""							)
																															A__						:  Dict		  =  token_index
																									writer.write(""" """.join(snake_case							) + """\n"""							)
																									index += 1

													return vocab_file, merge_file


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None							):
													'''simple docstring'''





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


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None			,    snake_case      :						bool = False							):
													'''simple docstring'''





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

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


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None							):
													'''simple docstring'''





													A__						:  int		  =  [self.sep_token_id]
													A__						:  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 + sep + token_ids_a + sep							) * [0]


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						Optional[Any]			,    snake_case      :						List[str]=False			,    **snake_case      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  kwargs.pop("""add_prefix_space"""			,    self.add_prefix_space							)
													if (is_split_into_words or add_prefix_space) and (len(snake_case							) > 0 and not text[0].isspace()):
																			A__						:  str		  =  """ """ + text
													return (text, kwargs)






