

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


__lowercase     : Any       =						logging.getLogger(__name__)
__lowercase     : Any       =						list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__lowercase     : Dict       =						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class 						_A     :





  '''simple docstring'''



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





  '''simple docstring'''



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

      '''simple docstring'''
      if self.train_file is not None:
          snake_case							:		Tuple        =		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       lowercase					(	__A :					Union[str, Any] ,    __A :					List[Any]		)				->			str:





    '''simple docstring'''




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

    snake_case							:		Optional[Any]        =		{c: dataset[c] for c in dataset.column_names}
    snake_case							:		Dict        =		refs
    return Dataset.from_dict(__A		)







def       lowercase					(	)				->			str:





    '''simple docstring'''




    snake_case							:		List[str]        =		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      ,		snake_case      ,		snake_case							:		Any        =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
    else:
        snake_case      ,		snake_case      ,		snake_case							:		str        =		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							:		Tuple        =		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							:		Dict        =		load_dataset(data_args.dataset_name ,    data_args.dataset_config_name		)
        if "validation" not in datasets.keys():
            snake_case							:		Union[str, Any]        =		load_dataset(
                data_args.dataset_name ,    data_args.dataset_config_name ,    split=f"""train[:{data_args.validation_split_percentage}%]""" ,    )
            snake_case							:		List[str]        =		load_dataset(
                data_args.dataset_name ,    data_args.dataset_config_name ,    split=f"""train[{data_args.validation_split_percentage}%:]""" ,    )
    else:
        snake_case							:		int        =		{}
        if data_args.train_file is not None:
            snake_case							:		Optional[int]        =		data_args.train_file
        if data_args.validation_file is not None:
            snake_case							:		Dict        =		data_args.validation_file
        snake_case							:		str        =		data_args.train_file.split("""."""		)[-1]
        if extension == "txt":
            snake_case							:		str        =		"""text"""
        snake_case							:		List[str]        =		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							:		Optional[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:
        snake_case							:		Tuple        =		AutoConfig.from_pretrained(model_args.config_name ,    **__A		)
    elif model_args.model_name_or_path:
        snake_case							:		Tuple        =		AutoConfig.from_pretrained(model_args.model_name_or_path ,    **__A		)
    else:
        snake_case							:		Union[str, Any]        =		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							:		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							:		Optional[Any]        =		AutoTokenizer.from_pretrained(model_args.tokenizer_name ,    **__A		)
    elif model_args.model_name_or_path:
        snake_case							:		Union[str, 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							:		Optional[Any]        =		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							:		Any        =		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							:		str        =		datasets["""train"""].column_names
    else:
        snake_case							:		Dict        =		datasets["""validation"""].column_names
    snake_case							:		List[str]        =		"""text""" if """text""" in column_names else column_names[0]

    snake_case							:		int        =		"""max_length""" if data_args.pad_to_max_length else False

    def tokenize_function(__A :					Tuple		):
        # Remove empty lines
        snake_case							:		int        =		[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							:		Tuple        =		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							:		List[str]        =		add_chinese_references(tokenized_datasets["""train"""] ,    data_args.train_ref_file		)
    if data_args.validation_ref_file is not None:
        snake_case							:		Optional[int]        =		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							:		int        =		data_args.train_ref_file or data_args.validation_ref_file
    if has_ref:
        snake_case							:		List[Any]        =		False

    # Data collator
    # This one will take care of randomly masking the tokens.
    snake_case							:		Optional[int]        =		DataCollatorForWholeWordMask(tokenizer=__A ,    mlm_probability=data_args.mlm_probability		)

    # Initialize our Trainer
    snake_case							:		Optional[Any]        =		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							:		Any        =		last_checkpoint
        elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path		):
            snake_case							:		Optional[Any]        =		model_args.model_name_or_path
        else:
            snake_case							:		Any        =		None
        snake_case							:		Dict        =		trainer.train(resume_from_checkpoint=__A		)
        trainer.save_model()  # Saves the tokenizer too for easy upload

        snake_case							:		Optional[Any]        =		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							:		List[str]        =		{}
    if training_args.do_eval:
        logger.info("""*** Evaluate ***"""		)

        snake_case							:		Union[str, Any]        =		trainer.evaluate()

        snake_case							:		List[Any]        =		math.exp(eval_output["""eval_loss"""]		)
        snake_case							:		Any        =		perplexity

        snake_case							:		Any        =		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       lowercase					(	__A :					int		)				->			Optional[Any]:





    '''simple docstring'''




    main()


if __name__ == "__main__":
     main()









'''simple docstring'''







from ..utils import DummyObject, requires_backends







class 					_a (metaclass=a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Any          =					["""flax"""]







     def __init__(				self							,*__a							,**__a							)      ->				Tuple:
          requires_backends(self							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				Tuple:
          requires_backends(cls							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				Dict:
          requires_backends(cls							,["""flax"""]							)







class 					_a (metaclass=a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Optional[int]          =					["""flax"""]







     def __init__(				self							,*__a							,**__a							)      ->				List[Any]:
          requires_backends(self							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				List[str]:
          requires_backends(cls							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				str:
          requires_backends(cls							,["""flax"""]							)







class 					_a (metaclass=a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Any          =					["""flax"""]







     def __init__(				self							,*__a							,**__a							)      ->				Any:
          requires_backends(self							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				List[Any]:
          requires_backends(cls							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				Any:
          requires_backends(cls							,["""flax"""]							)







class 					_a (metaclass=a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     List[str]          =					["""flax"""]







     def __init__(				self							,*__a							,**__a							)      ->				Optional[Any]:
          requires_backends(self							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				Dict:
          requires_backends(cls							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				int:
          requires_backends(cls							,["""flax"""]							)







class 					_a (metaclass=a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     int          =					["""flax"""]







     def __init__(				self							,*__a							,**__a							)      ->				Optional[int]:
          requires_backends(self							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				Optional[Any]:
          requires_backends(cls							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				List[str]:
          requires_backends(cls							,["""flax"""]							)







class 					_a (metaclass=a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					["""flax"""]







     def __init__(				self							,*__a							,**__a							)      ->				Optional[Any]:
          requires_backends(self							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				List[Any]:
          requires_backends(cls							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				int:
          requires_backends(cls							,["""flax"""]							)







class 					_a (metaclass=a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Union[str, Any]          =					["""flax"""]







     def __init__(				self							,*__a							,**__a							)      ->				List[str]:
          requires_backends(self							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				Optional[Any]:
          requires_backends(cls							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				str:
          requires_backends(cls							,["""flax"""]							)







class 					_a (metaclass=a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Optional[Any]          =					["""flax"""]







     def __init__(				self							,*__a							,**__a							)      ->				str:
          requires_backends(self							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				str:
          requires_backends(cls							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				Union[str, Any]:
          requires_backends(cls							,["""flax"""]							)







class 					_a (metaclass=a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     List[Any]          =					["""flax"""]







     def __init__(				self							,*__a							,**__a							)      ->				Union[str, Any]:
          requires_backends(self							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				Any:
          requires_backends(cls							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				Optional[Any]:
          requires_backends(cls							,["""flax"""]							)







class 					_a (metaclass=a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Dict          =					["""flax"""]







     def __init__(				self							,*__a							,**__a							)      ->				Dict:
          requires_backends(self							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				List[str]:
          requires_backends(cls							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				Optional[Any]:
          requires_backends(cls							,["""flax"""]							)







class 					_a (metaclass=a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     List[str]          =					["""flax"""]







     def __init__(				self							,*__a							,**__a							)      ->				Tuple:
          requires_backends(self							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				Tuple:
          requires_backends(cls							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				Optional[int]:
          requires_backends(cls							,["""flax"""]							)







class 					_a (metaclass=a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Tuple          =					["""flax"""]







     def __init__(				self							,*__a							,**__a							)      ->				List[str]:
          requires_backends(self							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				Union[str, Any]:
          requires_backends(cls							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				Dict:
          requires_backends(cls							,["""flax"""]							)







class 					_a (metaclass=a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Tuple          =					["""flax"""]







     def __init__(				self							,*__a							,**__a							)      ->				Tuple:
          requires_backends(self							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				List[str]:
          requires_backends(cls							,["""flax"""]							)







     @classmethod
     def 			snake_case_      (				cls							,*__a							,**__a							)      ->				int:
          requires_backends(cls							,["""flax"""]							)



