'''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


snake_case__					:      Tuple				      =  logging.getLogger(__name__)
snake_case__					:      Any				      =  list(MODEL_FOR_MASKED_LM_MAPPING.keys())
snake_case__					:      int				      =  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  lowerCamelCase_		:Optional[str]						   =		field(
      default=lowerCamelCase_						,     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=lowerCamelCase_						,     metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(lowerCamelCase_	)}						,     )
  lowerCamelCase_		:Optional[str]						   =		field(
      default=lowerCamelCase_						,     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=lowerCamelCase_						,     metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}	)
  lowerCamelCase_		:Optional[str]						   =		field(
      default=lowerCamelCase_						,     metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}	)
  lowerCamelCase_		:Optional[str]						   =		field(
      default=lowerCamelCase_						,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}						,     )
  lowerCamelCase_		:bool						   =		field(
      default=lowerCamelCase_						,     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=lowerCamelCase_						,     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 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  lowerCamelCase_		:Optional[str]						   =		field(
      default=lowerCamelCase_						,     metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}	)
  lowerCamelCase_		:Optional[str]						   =		field(
      default=lowerCamelCase_						,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}	)
  lowerCamelCase_		:Optional[str]						   =		field(default=lowerCamelCase_						,     metadata={'''help''': '''The input training data file (a text file).'''}	)
  lowerCamelCase_		:Optional[str]						   =		field(
      default=lowerCamelCase_						,     metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}						,     )
  lowerCamelCase_		:Optional[str]						   =		field(
      default=lowerCamelCase_						,     metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}						,     )
  lowerCamelCase_		:Optional[str]						   =		field(
      default=lowerCamelCase_						,     metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}						,     )
  lowerCamelCase_		:bool						   =		field(
      default=lowerCamelCase_						,     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=lowerCamelCase_						,     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=lowerCamelCase_						,     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=lowerCamelCase_						,     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_							:       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:
            UpperCAmelCase_							:       int       =				self.validation_file.split('.'						)[-1]
            assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."







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



     """simple docstring"""





     with open(lowerCamelCase_     ,					'r'     ,					encoding='utf-8'       ) as f:
          UpperCAmelCase_							:       List[Any]       =				[json.loads(lowerCamelCase_       ) for line in f.read().splitlines() if (len(lowerCamelCase_       ) > 0 and not line.isspace())]
     assert len(lowerCamelCase_       ) == len(lowerCamelCase_       )

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


def  _lowerCamelCase   (				):



     """simple docstring"""





     UpperCAmelCase_							:       Tuple       =				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_							:       Any       =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
     else:
          UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_							:       Any       =				parser.parse_args_into_dataclasses()

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

     UpperCAmelCase_							:       Optional[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_							:       int       =				AutoTokenizer.from_pretrained(model_args.tokenizer_name     ,					**lowerCamelCase_       )
     elif model_args.model_name_or_path:
          UpperCAmelCase_							:       Any       =				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_							:       List[Any]       =				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_							:       int       =				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_							:       List[str]       =				datasets['train'].column_names
     else:
          UpperCAmelCase_							:       Any       =				datasets['validation'].column_names
     UpperCAmelCase_							:       List[str]       =				'text' if 'text' in column_names else column_names[0]

     UpperCAmelCase_							:       int       =				'max_length' if data_args.pad_to_max_length else False

     def tokenize_function(lowerCamelCase_ :							List[str]       ):
          # Remove empty lines
          UpperCAmelCase_							:       Dict       =				[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_							:       Optional[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_							:       Union[str, Any]       =				add_chinese_references(tokenized_datasets['train']     ,					data_args.train_ref_file       )
     if data_args.validation_ref_file is not None:
          UpperCAmelCase_							:       Union[str, 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_							:       Any       =				data_args.train_ref_file or data_args.validation_ref_file
     if has_ref:
          UpperCAmelCase_							:       List[str]       =				False

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

     # Initialize our Trainer
     UpperCAmelCase_							:       Union[str, Any]       =				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_							:       Any       =				last_checkpoint
          elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path       ):
               UpperCAmelCase_							:       str       =				model_args.model_name_or_path
          else:
               UpperCAmelCase_							:       str       =				None
          UpperCAmelCase_							:       List[Any]       =				trainer.train(resume_from_checkpoint=lowerCamelCase_       )
          trainer.save_model()  # Saves the tokenizer too for easy upload

          UpperCAmelCase_							:       Optional[int]       =				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_							:       List[Any]       =				{}
     if training_args.do_eval:
          logger.info('*** Evaluate ***'       )

          UpperCAmelCase_							:       Dict       =				trainer.evaluate()

          UpperCAmelCase_							:       Optional[int]       =				math.exp(eval_output['eval_loss']       )
          UpperCAmelCase_							:       Optional[Any]       =				perplexity

          UpperCAmelCase_							:       List[Any]       =				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  _lowerCamelCase   (				lowerCamelCase_ :							int       ):



     """simple docstring"""





     main()


if __name__ == "__main__":
 main()







'''simple docstring'''


from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_	):







  '''simple docstring'''






  @register_to_config
  def __init__(			self				,		snake_case_ = 7_6_8				,		):




       '''simple docstring'''

       super().__init__()

       UpperCAmelCase_							:       int       =				nn.Parameter(torch.zeros(1				,		snake_case_						)						)
       UpperCAmelCase_							:       str       =				nn.Parameter(torch.ones(1				,		snake_case_						)						)


  def _UpperCamelCase					(			self				,		snake_case_ = None				,		snake_case_ = None				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				nn.Parameter(self.mean.to(snake_case_						).to(snake_case_						)						)
       UpperCAmelCase_							:       Tuple       =				nn.Parameter(self.std.to(snake_case_						).to(snake_case_						)						)
       return self


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				(embeds - self.mean) * 1.0 / self.std
       return embeds






  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				(embeds * self.std) + self.mean
       return embeds






