





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


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



@dataclass
class  A__       :
      _UpperCAmelCase     :Optional[str]						   =       field(
          default=__lowercase	,       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=__lowercase	,       metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__lowercase      )}	,       )
      _UpperCAmelCase     :Optional[str]						   =       field(
          default=__lowercase	,       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=__lowercase	,       metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
      _UpperCAmelCase     :Optional[str]						   =       field(
          default=__lowercase	,       metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
      _UpperCAmelCase     :Optional[str]						   =       field(
          default=__lowercase	,       metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,       )
      _UpperCAmelCase     :bool						   =       field(
          default=__lowercase	,       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=__lowercase	,       metadata={
              'help': (
                  'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                  'with private models).'
              )
          }	,       )





      def 	__UpperCamelCase(			self       ):
       '''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  A__       :
      _UpperCAmelCase     :Optional[str]						   =       field(
          default=__lowercase	,       metadata={'help': 'The name of the dataset to use (via the datasets library).'}      )
      _UpperCAmelCase     :Optional[str]						   =       field(
          default=__lowercase	,       metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}      )
      _UpperCAmelCase     :Optional[str]						   =       field(default=__lowercase	,       metadata={'help': 'The input training data file (a text file).'}      )
      _UpperCAmelCase     :Optional[str]						   =       field(
          default=__lowercase	,       metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}	,       )
      _UpperCAmelCase     :Optional[str]						   =       field(
          default=__lowercase	,       metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}	,       )
      _UpperCAmelCase     :Optional[str]						   =       field(
          default=__lowercase	,       metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}	,       )
      _UpperCAmelCase     :bool						   =       field(
          default=__lowercase	,       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=__lowercase	,       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=__lowercase	,       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=__lowercase	,       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       ):
       '''simple docstring'''




       if self.train_file is not None:
        UpperCamelCase     : Optional[Any]	     =       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     : Dict	     =       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_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Optional[Any]:
 with open(_lowerCAmelCase							,       "r"							,       encoding="utf-8"					) as f:
  UpperCamelCase     : int	     =       [json.loads(_lowerCAmelCase					) for line in f.read().splitlines() if (len(_lowerCAmelCase					) > 0 and not line.isspace())]
 assert len(_lowerCAmelCase					) == len(_lowerCAmelCase					)

 UpperCamelCase     : Tuple	     =       {c: dataset[c] for c in dataset.column_names}
 UpperCamelCase     : Any	     =       refs
 return Dataset.from_dict(_lowerCAmelCase					)



def 		A_    (					)						->			Any:
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 UpperCamelCase     : List[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.
  UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : Dict	     =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
 else:
  UpperCamelCase     ,   UpperCamelCase     ,   UpperCamelCase     : int	     =       parser.parse_args_into_dataclasses()

 # Detecting last checkpoint.
 UpperCamelCase     : List[Any]	     =       None
 if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
  UpperCamelCase     : List[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"							,       _lowerCAmelCase					)

 # 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     : List[str]	     =       load_dataset(data_args.dataset_name							,       data_args.dataset_config_name					)
  if "validation" not in datasets.keys():
   UpperCamelCase     : Optional[int]	     =       load_dataset(
       data_args.dataset_name							,       data_args.dataset_config_name							,       split=F"""train[:{data_args.validation_split_percentage}%]"""							,       )
   UpperCamelCase     : Tuple	     =       load_dataset(
       data_args.dataset_name							,       data_args.dataset_config_name							,       split=F"""train[{data_args.validation_split_percentage}%:]"""							,       )
 else:
  UpperCamelCase     : List[str]	     =       {}
  if data_args.train_file is not None:
   UpperCamelCase     : str	     =       data_args.train_file
  if data_args.validation_file is not None:
   UpperCamelCase     : int	     =       data_args.validation_file
  UpperCamelCase     : List[Any]	     =       data_args.train_file.split("."					)[-1]
  if extension == "txt":
   UpperCamelCase     : Optional[Any]	     =       "text"
  UpperCamelCase     : Tuple	     =       load_dataset(_lowerCAmelCase							,       data_files=_lowerCAmelCase					)
 # 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     : Any	     =       {
     "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     : Optional[Any]	     =       AutoConfig.from_pretrained(model_args.config_name							,       **_lowerCAmelCase					)
 elif model_args.model_name_or_path:
  UpperCamelCase     : Union[str, Any]	     =       AutoConfig.from_pretrained(model_args.model_name_or_path							,       **_lowerCAmelCase					)
 else:
  UpperCamelCase     : 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}"""					)

 UpperCamelCase     : Union[str, Any]	     =       {
     "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     : Union[str, Any]	     =       AutoTokenizer.from_pretrained(model_args.tokenizer_name							,       **_lowerCAmelCase					)
 elif model_args.model_name_or_path:
  UpperCamelCase     : Tuple	     =       AutoTokenizer.from_pretrained(model_args.model_name_or_path							,       **_lowerCAmelCase					)
 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     : Dict	     =       AutoModelForMaskedLM.from_pretrained(
      model_args.model_name_or_path							,       from_tf=bool(".ckpt" in model_args.model_name_or_path					)							,       config=_lowerCAmelCase							,       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     : Optional[Any]	     =       AutoModelForMaskedLM.from_config(_lowerCAmelCase					)

 model.resize_token_embeddings(len(_lowerCAmelCase					)					)

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

 UpperCamelCase     : Optional[Any]	     =       "max_length" if data_args.pad_to_max_length else False

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

 UpperCamelCase     : Union[str, Any]	     =       datasets.map(
     _lowerCAmelCase							,       batched=_lowerCAmelCase							,       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     : Optional[int]	     =       add_chinese_references(tokenized_datasets["train"]							,       data_args.train_ref_file					)
 if data_args.validation_ref_file is not None:
  UpperCamelCase     : Any	     =       add_chinese_references(
      tokenized_datasets["validation"]							,       data_args.validation_ref_file					)
 # If we have ref files, need to avoid it removed by trainer
 UpperCamelCase     : int	     =       data_args.train_ref_file or data_args.validation_ref_file
 if has_ref:
  UpperCamelCase     : Union[str, Any]	     =       False

 # Data collator
 # This one will take care of randomly masking the tokens.
 UpperCamelCase     : List[Any]	     =       DataCollatorForWholeWordMask(tokenizer=_lowerCAmelCase							,       mlm_probability=data_args.mlm_probability					)

 # Initialize our Trainer
 UpperCamelCase     : Dict	     =       Trainer(
     model=_lowerCAmelCase							,       args=_lowerCAmelCase							,       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=_lowerCAmelCase							,       data_collator=_lowerCAmelCase							,       )

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

  UpperCamelCase     : List[str]	     =       os.path.join(training_args.output_dir							,       "train_results.txt"					)
  if trainer.is_world_process_zero():
   with open(_lowerCAmelCase							,       "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     : int	     =       {}
 if training_args.do_eval:
  logger.info("*** Evaluate ***"					)

  UpperCamelCase     : Optional[int]	     =       trainer.evaluate()

  UpperCamelCase     : int	     =       math.exp(eval_output["eval_loss"]					)
  UpperCamelCase     : List[str]	     =       perplexity

  UpperCamelCase     : str	     =       os.path.join(training_args.output_dir							,       "eval_results_mlm_wwm.txt"					)
  if trainer.is_world_process_zero():
   with open(_lowerCAmelCase							,       "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_    (					_lowerCAmelCase					)						->			Dict:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
    main()







class  A__       :  # Public class to implement a graph





      def __init__(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       row
       UpperCamelCase     : Any	     =       col
       UpperCamelCase     : Optional[Any]	     =       graph





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       return (
           0 <= i < self.ROW
           and 0 <= j < self.COL
           and not visited[i][j]
           and self.graph[i][j]
       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
       UpperCamelCase     : Dict	     =       [-1, 0, 1, -1, 1, -1, 0, 1]
       UpperCamelCase     : Any	     =       True  # Make those cells visited
       for k in range(8       ):
        if self.is_safe(i + row_nbr[k]	,							j + col_nbr[k]	,							A_       ):
         self.diffs(i + row_nbr[k]	,							j + col_nbr[k]	,							A_       )





      def 	__UpperCamelCase(			self       ):  # And finally, count all islands.
       '''simple docstring'''




       UpperCamelCase     : str	     =       [[False for j in range(self.COL       )] for i in range(self.ROW       )]
       UpperCamelCase     : int	     =       0
       for i in range(self.ROW       ):
        for j in range(self.COL       ):
         if visited[i][j] is False and self.graph[i][j] == 1:
          self.diffs(A_	,							A_	,							A_       )
          count += 1
       return count
