



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


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

@dataclass
class     _A       :
       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 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''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=__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 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           if self.train_file is not None:
               __a						       =				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:
               __a						       =				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	(			_UpperCAmelCase   , _UpperCAmelCase				):
    with open(_UpperCAmelCase   , '''r'''   , encoding='''utf-8'''				) as f:
        __a						       =				[json.loads(_UpperCAmelCase				) for line in f.read().splitlines() if (len(_UpperCAmelCase				) > 0 and not line.isspace())]
    assert len(_UpperCAmelCase				) == len(_UpperCAmelCase				)

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





def      __snake_case	(			):
    # 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.

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

    # Detecting last checkpoint.
    __a						       =				None
    if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
        __a						       =				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'''   , _UpperCAmelCase				)

    # 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.
        __a						       =				load_dataset(data_args.dataset_name   , data_args.dataset_config_name				)
        if "validation" not in datasets.keys():
            __a						       =				load_dataset(
                data_args.dataset_name   , data_args.dataset_config_name   , split=f'train[:{data_args.validation_split_percentage}%]'   , )
            __a						       =				load_dataset(
                data_args.dataset_name   , data_args.dataset_config_name   , split=f'train[{data_args.validation_split_percentage}%:]'   , )
    else:
        __a						       =				{}
        if data_args.train_file is not None:
            __a						       =				data_args.train_file
        if data_args.validation_file is not None:
            __a						       =				data_args.validation_file
        __a						       =				data_args.train_file.split('''.'''				)[-1]
        if extension == "txt":
            __a						       =				'''text'''
        __a						       =				load_dataset(_UpperCAmelCase   , data_files=_UpperCAmelCase				)
    # 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.
    __a						       =				{
        '''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:
        __a						       =				AutoConfig.from_pretrained(model_args.config_name   , **_UpperCAmelCase				)
    elif model_args.model_name_or_path:
        __a						       =				AutoConfig.from_pretrained(model_args.model_name_or_path   , **_UpperCAmelCase				)
    else:
        __a						       =				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}'				)

    __a						       =				{
        '''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:
        __a						       =				AutoTokenizer.from_pretrained(model_args.tokenizer_name   , **_UpperCAmelCase				)
    elif model_args.model_name_or_path:
        __a						       =				AutoTokenizer.from_pretrained(model_args.model_name_or_path   , **_UpperCAmelCase				)
    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:
        __a						       =				AutoModelForMaskedLM.from_pretrained(
            model_args.model_name_or_path   , from_tf=bool('''.ckpt''' in model_args.model_name_or_path				)   , config=_UpperCAmelCase   , 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'''				)
        __a						       =				AutoModelForMaskedLM.from_config(_UpperCAmelCase				)

    model.resize_token_embeddings(len(_UpperCAmelCase				)				)

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

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

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

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

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

    # Initialize our Trainer
    __a						       =				Trainer(
        model=_UpperCAmelCase   , args=_UpperCAmelCase   , 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=_UpperCAmelCase   , data_collator=_UpperCAmelCase   , )

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

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

        __a						       =				trainer.evaluate()

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

        __a						       =				os.path.join(training_args.output_dir   , '''eval_results_mlm_wwm.txt'''				)
        if trainer.is_world_process_zero():
            with open(_UpperCAmelCase   , '''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	(			_UpperCAmelCase				):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
 main()










import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union

@dataclass
class     _A       :
       UpperCamelCase__  :    Optional[Union[str, Path]]											=						None
       UpperCamelCase__  :    bool											=						False
       UpperCamelCase__  :    bool											=						False
       UpperCamelCase__  :    bool											=						False
       UpperCamelCase__  :    Optional[Dict]											=						None
       UpperCamelCase__  :    Optional[str]											=						None
       UpperCamelCase__  :    bool											=						False
       UpperCamelCase__  :    bool											=						False
       UpperCamelCase__  :    bool											=						False
       UpperCamelCase__  :    bool											=						True
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    int											=						1
       UpperCamelCase__  :    Optional[Union[str, bool]]											=						None
       UpperCamelCase__  :    bool											=						False
       UpperCamelCase__  :    Optional[Dict]											=						None
       UpperCamelCase__  :    Optional[str]											=						None



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           return self.__class__(**{k: copy.deepcopy(__SCREAMING_SNAKE_CASE) for k, v in self.__dict__.items()})





