




"""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


A_      =    logging.getLogger(__name__)
A_      =    list(MODEL_FOR_MASKED_LM_MAPPING.keys())
A_      =    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class  lowercase:







 '''simple docstring'''





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


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   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  lowercase:







 '''simple docstring'''





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




   '''simple docstring'''



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







def  UpperCAmelCase__	(snake_case__							: List[str]		,       snake_case__							: int   ):
  """simple docstring"""

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

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


def  UpperCAmelCase__	():
  """simple docstring"""

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

  # Detecting last checkpoint.
  _snake_case							:		Optional[int]								=  None
  if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
    _snake_case							:		Union[str, Any]								=  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"""		,       __a   )

  # 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							:		List[str]								=  load_dataset(data_args.dataset_name		,       data_args.dataset_config_name   )
    if "validation" not in datasets.keys():
      _snake_case							:		Optional[int]								=  load_dataset(
          data_args.dataset_name		,       data_args.dataset_config_name		,       split=F"train[:{data_args.validation_split_percentage}%]"		,       )
      _snake_case							:		Any								=  load_dataset(
          data_args.dataset_name		,       data_args.dataset_config_name		,       split=F"train[{data_args.validation_split_percentage}%:]"		,       )
  else:
    _snake_case							:		Any								=  {}
    if data_args.train_file is not None:
      _snake_case							:		str								=  data_args.train_file
    if data_args.validation_file is not None:
      _snake_case							:		List[Any]								=  data_args.validation_file
    _snake_case							:		Dict								=  data_args.train_file.split("""."""   )[-1]
    if extension == "txt":
      _snake_case							:		Dict								=  'text'
    _snake_case							:		Optional[int]								=  load_dataset(__a		,       data_files=__a   )
  # 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							:		Tuple								=  {
      '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							:		str								=  AutoConfig.from_pretrained(model_args.config_name		,       **__a   )
  elif model_args.model_name_or_path:
    _snake_case							:		Dict								=  AutoConfig.from_pretrained(model_args.model_name_or_path		,       **__a   )
  else:
    _snake_case							:		List[str]								=  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							:		Optional[int]								=  {
      '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							:		List[str]								=  AutoTokenizer.from_pretrained(model_args.tokenizer_name		,       **__a   )
  elif model_args.model_name_or_path:
    _snake_case							:		List[Any]								=  AutoTokenizer.from_pretrained(model_args.model_name_or_path		,       **__a   )
  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							:		Dict								=  AutoModelForMaskedLM.from_pretrained(
        model_args.model_name_or_path		,       from_tf=bool(""".ckpt""" in model_args.model_name_or_path   )		,       config=__a		,       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							:		List[str]								=  AutoModelForMaskedLM.from_config(__a   )

  model.resize_token_embeddings(len(__a   )   )

  # Preprocessing the datasets.
  # First we tokenize all the texts.
  if training_args.do_train:
    _snake_case							:		Union[str, Any]								=  datasets['train'].column_names
  else:
    _snake_case							:		Tuple								=  datasets['validation'].column_names
  _snake_case							:		Union[str, Any]								=  'text' if 'text' in column_names else column_names[0]

  _snake_case							:		str								=  'max_length' if data_args.pad_to_max_length else False

  def tokenize_function(snake_case__							: Optional[Any]   ):
    # Remove empty lines
    _snake_case							:		str								=  [line for line in examples['text'] if len(__a   ) > 0 and not line.isspace()]
    return tokenizer(examples["""text"""]		,       padding=__a		,       truncation=__a		,       max_length=data_args.max_seq_length   )

  _snake_case							:		Union[str, Any]								=  datasets.map(
      __a		,       batched=__a		,       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							:		str								=  add_chinese_references(tokenized_datasets["""train"""]		,       data_args.train_ref_file   )
  if data_args.validation_ref_file is not None:
    _snake_case							:		Optional[Any]								=  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							:		str								=  data_args.train_ref_file or data_args.validation_ref_file
  if has_ref:
    _snake_case							:		Tuple								=  False

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

  # Initialize our Trainer
  _snake_case							:		List[str]								=  Trainer(
      model=__a		,       args=__a		,       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=__a		,       data_collator=__a		,       )

  # Training
  if training_args.do_train:
    if last_checkpoint is not None:
      _snake_case							:		Tuple								=  last_checkpoint
    elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path   ):
      _snake_case							:		Tuple								=  model_args.model_name_or_path
    else:
      _snake_case							:		str								=  None
    _snake_case							:		Optional[Any]								=  trainer.train(resume_from_checkpoint=__a   )
    trainer.save_model()  # Saves the tokenizer too for easy upload

    _snake_case							:		Dict								=  os.path.join(training_args.output_dir		,       """train_results.txt"""   )
    if trainer.is_world_process_zero():
      with open(__a		,       """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							:		Dict								=  {}
  if training_args.do_eval:
    logger.info("""*** Evaluate ***"""   )

    _snake_case							:		int								=  trainer.evaluate()

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

    _snake_case							:		int								=  os.path.join(training_args.output_dir		,       """eval_results_mlm_wwm.txt"""   )
    if trainer.is_world_process_zero():
      with open(__a		,       """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  UpperCAmelCase__	(snake_case__							: List[Any]   ):
  """simple docstring"""

  main()


if __name__ == "__main__":
     main()








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: Optional[Any]   ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  []
  _snake_case							:		Dict								=  set({"""(""", """[""", """{"""}   )
  _snake_case							:		Union[str, Any]								=  set({""")""", """]""", """}"""}   )
  _snake_case							:		Tuple								=  {"""{""": """}""", """[""": """]""", """(""": """)"""}

  for i in range(len(snake_case__   )   ):
    if s[i] in open_brackets:
      stack.append(s[i]   )

    elif s[i] in closed_brackets and (
        len(snake_case__   ) == 0 or (len(snake_case__   ) > 0 and open_to_closed[stack.pop()] != s[i])
    ):
      return False

  return len(snake_case__   ) == 0


def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		Any								=  input("""Enter sequence of brackets: """   )
  if is_balanced(snake_case__   ):
    print(snake_case__		,       """is balanced"""   )
  else:
    print(snake_case__		,       """is not balanced"""   )


if __name__ == "__main__":
     main()


