





'''simple docstring'''






import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


__lowerCamelCase						: Optional[int]		=   logging.getLogger(__name__)
__lowerCamelCase						: Optional[int]		=   list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__lowerCamelCase						: List[str]		=   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class 		UpperCAmelCase   :


  """simple docstring"""







  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={
          """help""": (
              """The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch."""
          )
      }   ,     )
  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_lowerCAmelCase)}   ,     )
  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={
          """help""": (
              """Override some existing default config settings when a model is trained from scratch. Example: """
              """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
          )
      }   ,     )
  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}   ,     )
  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}   ,     )
  lowerCAmelCase_					=				field(
      default="""main"""   ,     metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}   ,     )
  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={
          """help""": (
              """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
              """with private models)."""
          )
      }   ,     )


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Any:







    if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
      raise ValueError(
          '''--config_overrides can\'t be used in combination with --config_name or --model_name_or_path'''  )







@dataclass
class 		UpperCAmelCase   :


  """simple docstring"""







  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={"""help""": """The name of the dataset to use (via the datasets library)."""})
  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
  lowerCAmelCase_					=				field(default=_lowerCAmelCase   ,     metadata={"""help""": """The input training data file (a text file)."""})
  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}   ,     )
  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}   ,     )
  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}   ,     )
  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
  lowerCAmelCase_					=				field(
      default=5   ,     metadata={
          """help""": """The percentage of the train set used as validation set in case there\'s no validation split"""
      }   ,     )
  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated. Default to the max input length of the model."""
          )
      }   ,     )
  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={"""help""": """The number of processes to use for the preprocessing."""}   ,     )
  lowerCAmelCase_					=				field(
      default=0.15   ,     metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""})
  lowerCAmelCase_					=				field(
      default=_lowerCAmelCase   ,     metadata={
          """help""": (
              """Whether to pad all samples to `max_seq_length`. """
              """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
          )
      }   ,     )


  def   UpperCamelCase__      (				self    :					str  )     ->						int:







    if self.train_file is not None:
      _UpperCamelCase       			=self.train_file.split('''.'''  )[-1]
      assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
    if self.validation_file is not None:
      _UpperCamelCase       			=self.validation_file.split('''.'''  )[-1]
      assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."




def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  with open(__SCREAMING_SNAKE_CASE  ,      '''r'''  ,      encoding='''utf-8'''   ) as f:
    _UpperCamelCase       			=[json.loads(__SCREAMING_SNAKE_CASE   ) for line in f.read().splitlines() if (len(__SCREAMING_SNAKE_CASE   ) > 0 and not line.isspace())]
  assert len(__SCREAMING_SNAKE_CASE   ) == len(__SCREAMING_SNAKE_CASE   )

  _UpperCamelCase       			={c: dataset[c] for c in dataset.column_names}
  _UpperCamelCase       			=refs
  return Dataset.from_dict(__SCREAMING_SNAKE_CASE   )


def 					_a   ():


  """simple docstring"""

  _UpperCamelCase       			=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
  if len(sys.argv   ) == 2 and sys.argv[1].endswith('''.json'''   ):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
  else:
    _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=parser.parse_args_into_dataclasses()

  # Detecting last checkpoint.
  _UpperCamelCase       			=None
  if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
    _UpperCamelCase       			=get_last_checkpoint(training_args.output_dir   )
    if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
      raise ValueError(
          f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
          '''Use --overwrite_output_dir to overcome.'''   )
    elif last_checkpoint is not None:
      logger.info(
          f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
          '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''   )

    # Setup logging
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''  ,      datefmt='''%m/%d/%Y %H:%M:%S'''  ,      handlers=[logging.StreamHandler(sys.stdout   )]  ,      )
  logger.setLevel(logging.INFO if is_main_process(training_args.local_rank   ) else logging.WARN   )

  # Log on each process the small summary:
  logger.warning(
      f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
      + f'''distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}'''   )
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank   ):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info('''Training/evaluation parameters %s'''  ,      __SCREAMING_SNAKE_CASE   )

  # Set seed before initializing model.
  set_seed(training_args.seed   )

  # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
  # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
  # (the dataset will be downloaded automatically from the datasets Hub).
  #
  # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
  # 'text' is found. You can easily tweak this behavior (see below).
  #
  # In distributed training, the load_dataset function guarantee that only one local process can concurrently
  # download the dataset.
  if data_args.dataset_name is not None:
    # Downloading and loading a dataset from the hub.
    _UpperCamelCase       			=load_dataset(data_args.dataset_name  ,      data_args.dataset_config_name   )
    if "validation" not in datasets.keys():
      _UpperCamelCase       			=load_dataset(
          data_args.dataset_name  ,      data_args.dataset_config_name  ,      split=f'''train[:{data_args.validation_split_percentage}%]'''  ,      )
      _UpperCamelCase       			=load_dataset(
          data_args.dataset_name  ,      data_args.dataset_config_name  ,      split=f'''train[{data_args.validation_split_percentage}%:]'''  ,      )
  else:
    _UpperCamelCase       			={}
    if data_args.train_file is not None:
      _UpperCamelCase       			=data_args.train_file
    if data_args.validation_file is not None:
      _UpperCamelCase       			=data_args.validation_file
    _UpperCamelCase       			=data_args.train_file.split('''.'''   )[-1]
    if extension == "txt":
      _UpperCamelCase       			='''text'''
    _UpperCamelCase       			=load_dataset(__SCREAMING_SNAKE_CASE  ,      data_files=__SCREAMING_SNAKE_CASE   )
  # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
  # https://huggingface.co/docs/datasets/loading_datasets.html.

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  _UpperCamelCase       			={
      '''cache_dir''': model_args.cache_dir,
      '''revision''': model_args.model_revision,
      '''use_auth_token''': True if model_args.use_auth_token else None,
  }
  if model_args.config_name:
    _UpperCamelCase       			=AutoConfig.from_pretrained(model_args.config_name  ,      **__SCREAMING_SNAKE_CASE   )
  elif model_args.model_name_or_path:
    _UpperCamelCase       			=AutoConfig.from_pretrained(model_args.model_name_or_path  ,      **__SCREAMING_SNAKE_CASE   )
  else:
    _UpperCamelCase       			=CONFIG_MAPPING[model_args.model_type]()
    logger.warning('''You are instantiating a new config instance from scratch.'''   )
    if model_args.config_overrides is not None:
      logger.info(f'''Overriding config: {model_args.config_overrides}'''   )
      config.update_from_string(model_args.config_overrides   )
      logger.info(f'''New config: {config}'''   )

  _UpperCamelCase       			={
      '''cache_dir''': model_args.cache_dir,
      '''use_fast''': model_args.use_fast_tokenizer,
      '''revision''': model_args.model_revision,
      '''use_auth_token''': True if model_args.use_auth_token else None,
  }
  if model_args.tokenizer_name:
    _UpperCamelCase       			=AutoTokenizer.from_pretrained(model_args.tokenizer_name  ,      **__SCREAMING_SNAKE_CASE   )
  elif model_args.model_name_or_path:
    _UpperCamelCase       			=AutoTokenizer.from_pretrained(model_args.model_name_or_path  ,      **__SCREAMING_SNAKE_CASE   )
  else:
    raise ValueError(
        '''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
        '''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''   )

  if model_args.model_name_or_path:
    _UpperCamelCase       			=AutoModelForMaskedLM.from_pretrained(
        model_args.model_name_or_path  ,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path   )  ,      config=__SCREAMING_SNAKE_CASE  ,      cache_dir=model_args.cache_dir  ,      revision=model_args.model_revision  ,      use_auth_token=True if model_args.use_auth_token else None  ,      )
  else:
    logger.info('''Training new model from scratch'''   )
    _UpperCamelCase       			=AutoModelForMaskedLM.from_config(__SCREAMING_SNAKE_CASE   )

  model.resize_token_embeddings(len(__SCREAMING_SNAKE_CASE   )   )

  # Preprocessing the datasets.
  # First we tokenize all the texts.
  if training_args.do_train:
    _UpperCamelCase       			=datasets['''train'''].column_names
  else:
    _UpperCamelCase       			=datasets['''validation'''].column_names
  _UpperCamelCase       			='''text''' if '''text''' in column_names else column_names[0]

  _UpperCamelCase       			='''max_length''' if data_args.pad_to_max_length else False

  def tokenize_function(__SCREAMING_SNAKE_CASE   ):
    # Remove empty lines
    _UpperCamelCase       			=[line for line in examples['''text'''] if len(__SCREAMING_SNAKE_CASE   ) > 0 and not line.isspace()]
    return tokenizer(examples['''text''']  ,      padding=__SCREAMING_SNAKE_CASE  ,      truncation=__SCREAMING_SNAKE_CASE  ,      max_length=data_args.max_seq_length   )

  _UpperCamelCase       			=datasets.map(
      __SCREAMING_SNAKE_CASE  ,      batched=__SCREAMING_SNAKE_CASE  ,      num_proc=data_args.preprocessing_num_workers  ,      remove_columns=[text_column_name]  ,      load_from_cache_file=not data_args.overwrite_cache  ,      )

  # Add the chinese references if provided
  if data_args.train_ref_file is not None:
    _UpperCamelCase       			=add_chinese_references(tokenized_datasets['''train''']  ,      data_args.train_ref_file   )
  if data_args.validation_ref_file is not None:
    _UpperCamelCase       			=add_chinese_references(
        tokenized_datasets['''validation''']  ,      data_args.validation_ref_file   )
  # If we have ref files, need to avoid it removed by trainer
  _UpperCamelCase       			=data_args.train_ref_file or data_args.validation_ref_file
  if has_ref:
    _UpperCamelCase       			=False

  # Data collator
  # This one will take care of randomly masking the tokens.
  _UpperCamelCase       			=DataCollatorForWholeWordMask(tokenizer=__SCREAMING_SNAKE_CASE  ,      mlm_probability=data_args.mlm_probability   )

  # Initialize our Trainer
  _UpperCamelCase       			=Trainer(
      model=__SCREAMING_SNAKE_CASE  ,      args=__SCREAMING_SNAKE_CASE  ,      train_dataset=tokenized_datasets['''train'''] if training_args.do_train else None  ,      eval_dataset=tokenized_datasets['''validation'''] if training_args.do_eval else None  ,      tokenizer=__SCREAMING_SNAKE_CASE  ,      data_collator=__SCREAMING_SNAKE_CASE  ,      )

  # Training
  if training_args.do_train:
    if last_checkpoint is not None:
      _UpperCamelCase       			=last_checkpoint
    elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path   ):
      _UpperCamelCase       			=model_args.model_name_or_path
    else:
      _UpperCamelCase       			=None
    _UpperCamelCase       			=trainer.train(resume_from_checkpoint=__SCREAMING_SNAKE_CASE   )
    trainer.save_model()  # Saves the tokenizer too for easy upload

    _UpperCamelCase       			=os.path.join(training_args.output_dir  ,      '''train_results.txt'''   )
    if trainer.is_world_process_zero():
      with open(__SCREAMING_SNAKE_CASE  ,      '''w'''   ) as writer:
        logger.info('''***** Train results *****'''   )
        for key, value in sorted(train_result.metrics.items()   ):
          logger.info(f'''  {key} = {value}'''   )
          writer.write(f'''{key} = {value}\n'''   )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
      trainer.state.save_to_json(os.path.join(training_args.output_dir  ,      '''trainer_state.json'''   )   )

    # Evaluation
  _UpperCamelCase       			={}
  if training_args.do_eval:
    logger.info('''*** Evaluate ***'''   )

    _UpperCamelCase       			=trainer.evaluate()

    _UpperCamelCase       			=math.exp(eval_output['''eval_loss''']   )
    _UpperCamelCase       			=perplexity

    _UpperCamelCase       			=os.path.join(training_args.output_dir  ,      '''eval_results_mlm_wwm.txt'''   )
    if trainer.is_world_process_zero():
      with open(__SCREAMING_SNAKE_CASE  ,      '''w'''   ) as writer:
        logger.info('''***** Eval results *****'''   )
        for key, value in sorted(results.items()   ):
          logger.info(f'''  {key} = {value}'''   )
          writer.write(f'''{key} = {value}\n'''   )

  return results


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  main()


if __name__ == "__main__":
       main()













'''simple docstring'''






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

import regex as re

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


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation

__lowerCamelCase						: Optional[int]		=   logging.get_logger(__name__)


__lowerCamelCase						: int		=   {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__lowerCamelCase						: Any		=   {
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

__lowerCamelCase						: Dict		=   {'facebook/blenderbot-3B': 128}


@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 					_a   ():


  """simple docstring"""

  _UpperCamelCase       			=(
      list(range(ord('''!'''   )  ,      ord('''~'''   ) + 1   )   ) + list(range(ord('''¡'''   )  ,      ord('''¬'''   ) + 1   )   ) + list(range(ord('''®'''   )  ,      ord('''ÿ'''   ) + 1   )   )
  )
  _UpperCamelCase       			=bs[:]
  _UpperCamelCase       			=0
  for b in range(2**8   ):
    if b not in bs:
      bs.append(__SCREAMING_SNAKE_CASE   )
      cs.append(2**8 + n   )
      n += 1
  _UpperCamelCase       			=[chr(__SCREAMING_SNAKE_CASE   ) for n in cs]
  return dict(zip(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )   )


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=set()
  _UpperCamelCase       			=word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)   )
    _UpperCamelCase       			=char
  return pairs







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







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


  def __init__(				self    :					str							,	UpperCamelCase__    :					Tuple							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[Any]="replace"							,	UpperCamelCase__    :					Dict="<s>"							,	UpperCamelCase__    :					Optional[Any]="</s>"							,	UpperCamelCase__    :					Union[str, Any]="</s>"							,	UpperCamelCase__    :					Tuple="<s>"							,	UpperCamelCase__    :					int="<unk>"							,	UpperCamelCase__    :					int="<pad>"							,	UpperCamelCase__    :					Optional[int]="<mask>"							,	UpperCamelCase__    :					List[str]=False							,	**UpperCamelCase__    :					Tuple							,	)     ->						Optional[int]:
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else bos_token
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else eos_token
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else sep_token
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else cls_token
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else unk_token
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else pad_token

    # Mask token behave like a normal word, i.e. include the space before it
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else mask_token

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

    with open(UpperCamelCase__							,	encoding='''utf-8'''  ) as vocab_handle:
      _UpperCamelCase       			=json.load(UpperCamelCase__  )
    _UpperCamelCase       			={v: k for k, v in self.encoder.items()}
    _UpperCamelCase       			=errors  # how to handle errors in decoding
    _UpperCamelCase       			=bytes_to_unicode()
    _UpperCamelCase       			={v: k for k, v in self.byte_encoder.items()}
    with open(UpperCamelCase__							,	encoding='''utf-8'''  ) as merges_handle:
      _UpperCamelCase       			=merges_handle.read().split('''\n'''  )[1:-1]
    _UpperCamelCase       			=[tuple(merge.split()  ) for merge in bpe_merges]
    _UpperCamelCase       			=dict(zip(UpperCamelCase__							,	range(len(UpperCamelCase__  )  )  )  )
    _UpperCamelCase       			={}
    _UpperCamelCase       			=add_prefix_space

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


  @property
  # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
  def   UpperCamelCase__      (				self    :					Tuple  )     ->						List[Any]:
    return len(self.encoder  )


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Tuple:
    return dict(self.encoder							,	**self.added_tokens_encoder  )


  def   UpperCamelCase__      (				self    :					Any							,	UpperCamelCase__    :					List[str]  )     ->						Dict:
    if token in self.cache:
      return self.cache[token]
    _UpperCamelCase       			=tuple(UpperCamelCase__  )
    _UpperCamelCase       			=get_pairs(UpperCamelCase__  )

    if not pairs:
      return token

    while True:
      _UpperCamelCase       			=min(UpperCamelCase__							,	key=lambda UpperCamelCase__  : self.bpe_ranks.get(UpperCamelCase__							,	float('''inf'''  )  )  )
      if bigram not in self.bpe_ranks:
        break
      _UpperCamelCase			,     _UpperCamelCase       			=bigram
      _UpperCamelCase       			=[]
      _UpperCamelCase       			=0
      while i < len(UpperCamelCase__  ):
        try:
          _UpperCamelCase       			=word.index(UpperCamelCase__							,	UpperCamelCase__  )
        except ValueError:
          new_word.extend(word[i:]  )
          break
        else:
          new_word.extend(word[i:j]  )
          _UpperCamelCase       			=j

        if word[i] == first and i < len(UpperCamelCase__  ) - 1 and word[i + 1] == second:
          new_word.append(first + second  )
          i += 2
        else:
          new_word.append(word[i]  )
          i += 1
      _UpperCamelCase       			=tuple(UpperCamelCase__  )
      _UpperCamelCase       			=new_word
      if len(UpperCamelCase__  ) == 1:
        break
      else:
        _UpperCamelCase       			=get_pairs(UpperCamelCase__  )
    _UpperCamelCase       			=''' '''.join(UpperCamelCase__  )
    _UpperCamelCase       			=word
    return word


  def   UpperCamelCase__      (				self    :					Optional[Any]							,	UpperCamelCase__    :					List[Any]  )     ->						Optional[Any]:
    _UpperCamelCase       			=[]
    for token in re.findall(self.pat							,	UpperCamelCase__  ):
      _UpperCamelCase       			=''''''.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(UpperCamelCase__  ).split(''' '''  )  )
    return bpe_tokens


  def   UpperCamelCase__      (				self    :					int							,	UpperCamelCase__    :					Optional[int]  )     ->						int:
    return self.encoder.get(UpperCamelCase__							,	self.encoder.get(self.unk_token  )  )


  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					Optional[Any]  )     ->						List[str]:
    return self.decoder.get(UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Dict							,	UpperCamelCase__    :					int  )     ->						List[Any]:
    _UpperCamelCase       			=''''''.join(UpperCamelCase__  )
    _UpperCamelCase       			=bytearray([self.byte_decoder[c] for c in text]  ).decode('''utf-8'''							,	errors=self.errors  )
    return text


  def   UpperCamelCase__      (				self    :					Tuple							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[str] = None  )     ->						Tuple[str]:
    if not os.path.isdir(UpperCamelCase__  ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
      return
    _UpperCamelCase       			=os.path.join(
        UpperCamelCase__							,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )
    _UpperCamelCase       			=os.path.join(
        UpperCamelCase__							,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']  )

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

    _UpperCamelCase       			=0
    with open(UpperCamelCase__							,	'''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 UpperCamelCase__  : 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!'''  )
          _UpperCamelCase       			=token_index
        writer.write(''' '''.join(UpperCamelCase__  ) + '''\n'''  )
        index += 1

    return vocab_file, merge_file


  def   UpperCamelCase__      (				self    :					Optional[Any]							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None							,	UpperCamelCase__    :					bool = False  )     ->						List[int]:
    if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=UpperCamelCase__							,	token_ids_a=UpperCamelCase__							,	already_has_special_tokens=UpperCamelCase__  )

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


  def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						List[int]:
    _UpperCamelCase       			=[self.sep_token_id]
    _UpperCamelCase       			=[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    :					Optional[Any]							,	UpperCamelCase__    :					List[str]							,	UpperCamelCase__    :					str=False							,	**UpperCamelCase__    :					Union[str, Any]  )     ->						Optional[Any]:
    _UpperCamelCase       			=kwargs.pop('''add_prefix_space'''							,	self.add_prefix_space  )
    if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__  ) > 0 and not text[0].isspace()):
      _UpperCamelCase       			=''' ''' + text
    return (text, kwargs)


  def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						Any:
    return token_ids_a + [self.eos_token_id]




  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					"Conversation"  )     ->						List[int]:
    _UpperCamelCase       			=[]
    for is_user, text in conversation.iter_texts():
      if is_user:
        # We need to space prefix as it's being done within blenderbot
        inputs.append(''' ''' + text  )
      else:
        # Generated responses should contain them already.
        inputs.append(UpperCamelCase__  )

    _UpperCamelCase       			='''  '''.join(UpperCamelCase__  )
    _UpperCamelCase       			=self.encode(UpperCamelCase__  )
    if len(UpperCamelCase__  ) > self.model_max_length:
      _UpperCamelCase       			=input_ids[-self.model_max_length :]
      logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''  )
    return input_ids






