





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


__magic_name__   :						Union[str, Any]    =			logging.getLogger(__name__)
__magic_name__   :						Optional[Any]    =			list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__magic_name__   :						Optional[int]    =			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     UpperCAmelCase__       :						Optional[str]      =   field(
         default=UpperCAmelCase__					,    metadata={
             '''help''': (
                 '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
             )
         }					,    )
     UpperCAmelCase__       :						Optional[str]      =   field(
         default=UpperCAmelCase__					,    metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCAmelCase__			)}					,    )
     UpperCAmelCase__       :						Optional[str]      =   field(
         default=UpperCAmelCase__					,    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'''
             )
         }					,    )
     UpperCAmelCase__       :						Optional[str]      =   field(
         default=UpperCAmelCase__					,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
     UpperCAmelCase__       :						Optional[str]      =   field(
         default=UpperCAmelCase__					,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}			)
     UpperCAmelCase__       :						Optional[str]      =   field(
         default=UpperCAmelCase__					,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}					,    )
     UpperCAmelCase__       :						bool      =   field(
         default=UpperCAmelCase__					,    metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}					,    )
     UpperCAmelCase__       :						str      =   field(
         default='''main'''					,    metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}					,    )
     UpperCAmelCase__       :						bool      =   field(
         default=UpperCAmelCase__					,    metadata={
             '''help''': (
                 '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                 '''with private models).'''
             )
         }					,    )

     def 	UpperCamelCase(					self	):




       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       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     UpperCAmelCase__       :						Optional[str]      =   field(
         default=UpperCAmelCase__					,    metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}			)
     UpperCAmelCase__       :						Optional[str]      =   field(
         default=UpperCAmelCase__					,    metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
     UpperCAmelCase__       :						Optional[str]      =   field(default=UpperCAmelCase__					,    metadata={'''help''': '''The input training data file (a text file).'''}			)
     UpperCAmelCase__       :						Optional[str]      =   field(
         default=UpperCAmelCase__					,    metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}					,    )
     UpperCAmelCase__       :						Optional[str]      =   field(
         default=UpperCAmelCase__					,    metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}					,    )
     UpperCAmelCase__       :						Optional[str]      =   field(
         default=UpperCAmelCase__					,    metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}					,    )
     UpperCAmelCase__       :						bool      =   field(
         default=UpperCAmelCase__					,    metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}			)
     UpperCAmelCase__       :						Optional[int]      =   field(
         default=5					,    metadata={
             '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
         }					,    )
     UpperCAmelCase__       :						Optional[int]      =   field(
         default=UpperCAmelCase__					,    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.'''
             )
         }					,    )
     UpperCAmelCase__       :						Optional[int]      =   field(
         default=UpperCAmelCase__					,    metadata={'''help''': '''The number of processes to use for the preprocessing.'''}					,    )
     UpperCAmelCase__       :						float      =   field(
         default=0.15					,    metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}			)
     UpperCAmelCase__       :						bool      =   field(
         default=UpperCAmelCase__					,    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	):




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







def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  with open(SCREAMING_SNAKE_CASE__					,	"r"					,	encoding="utf-8"       ) as f:
    _snake_case     		=				[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__       )

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




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				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.
    _snake_case     		=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
  else:
    _snake_case     		=				parser.parse_args_into_dataclasses()

  # Detecting last checkpoint.
  _snake_case     		=				None
  if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
    _snake_case     		=				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.
    _snake_case     		=				load_dataset(data_args.dataset_name					,	data_args.dataset_config_name       )
    if "validation" not in datasets.keys():
      _snake_case     		=				load_dataset(
          data_args.dataset_name					,	data_args.dataset_config_name					,	split=f'''train[:{data_args.validation_split_percentage}%]'''					,	)
      _snake_case     		=				load_dataset(
          data_args.dataset_name					,	data_args.dataset_config_name					,	split=f'''train[{data_args.validation_split_percentage}%:]'''					,	)
  else:
    _snake_case     		=				{}
    if data_args.train_file is not None:
      _snake_case     		=				data_args.train_file
    if data_args.validation_file is not None:
      _snake_case     		=				data_args.validation_file
    _snake_case     		=				data_args.train_file.split("."       )[-1]
    if extension == "txt":
      _snake_case     		=				"""text"""
    _snake_case     		=				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.
  _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,
  }
  if model_args.config_name:
    _snake_case     		=				AutoConfig.from_pretrained(model_args.config_name					,	**SCREAMING_SNAKE_CASE__       )
  elif model_args.model_name_or_path:
    _snake_case     		=				AutoConfig.from_pretrained(model_args.model_name_or_path					,	**SCREAMING_SNAKE_CASE__       )
  else:
    _snake_case     		=				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}'''       )

  _snake_case     		=				{
      """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:
    _snake_case     		=				AutoTokenizer.from_pretrained(model_args.tokenizer_name					,	**SCREAMING_SNAKE_CASE__       )
  elif model_args.model_name_or_path:
    _snake_case     		=				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:
    _snake_case     		=				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"       )
    _snake_case     		=				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:
    _snake_case     		=				datasets["""train"""].column_names
  else:
    _snake_case     		=				datasets["""validation"""].column_names
  _snake_case     		=				"""text""" if """text""" in column_names else column_names[0]

  _snake_case     		=				"""max_length""" if data_args.pad_to_max_length else False

  def tokenize_function(SCREAMING_SNAKE_CASE__       ):
    # Remove empty lines
    _snake_case     		=				[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       )

  _snake_case     		=				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:
    _snake_case     		=				add_chinese_references(tokenized_datasets["train"]					,	data_args.train_ref_file       )
  if data_args.validation_ref_file is not None:
    _snake_case     		=				add_chinese_references(
        tokenized_datasets["validation"]					,	data_args.validation_ref_file       )
  # If we have ref files, need to avoid it removed by trainer
  _snake_case     		=				data_args.train_ref_file or data_args.validation_ref_file
  if has_ref:
    _snake_case     		=				False

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

  # Initialize our Trainer
  _snake_case     		=				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:
      _snake_case     		=				last_checkpoint
    elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path       ):
      _snake_case     		=				model_args.model_name_or_path
    else:
      _snake_case     		=				None
    _snake_case     		=				trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE__       )
    trainer.save_model()  # Saves the tokenizer too for easy upload

    _snake_case     		=				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
  _snake_case     		=				{}
  if training_args.do_eval:
    logger.info("*** Evaluate ***"       )

    _snake_case     		=				trainer.evaluate()

    _snake_case     		=				math.exp(eval_output["eval_loss"]       )
    _snake_case     		=				perplexity

    _snake_case     		=				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 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  main()


if __name__ == "__main__":
 main()









'''simple docstring'''



import functools




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				len(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				len(SCREAMING_SNAKE_CASE__       )

  @functools.cache
  def min_distance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ) -> int:
    # if first word index is overflow - delete all from the second word
    if indexa >= len_worda:
      return len_worda - indexa
    # if second word index is overflow - delete all from the first word
    if indexa >= len_worda:
      return len_worda - indexa
    _snake_case     		=				int(worda[indexa] != worda[indexa]       )  # current letters not identical
    return min(
        1 + min_distance(indexa + 1					,	SCREAMING_SNAKE_CASE__       )					,	1 + min_distance(SCREAMING_SNAKE_CASE__					,	indexa + 1       )					,	diff + min_distance(indexa + 1					,	indexa + 1       )					,	)

  return min_distance(0					,	0       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


