

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





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




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






        if self.train_file is not None:
              _lowercase      			=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:
              _lowercase      			=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 (     A__      :  Tuple ,  A__      :  str	)       -> Dict:





      """simple docstring"""



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

      _lowercase      			={c: dataset[c] for c in dataset.column_names}
      _lowercase      			=refs
      return Dataset.from_dict(A__	)
def 	a (     )       -> Tuple:





      """simple docstring"""



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

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

      _lowercase      			={
          '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:
            _lowercase      			=AutoTokenizer.from_pretrained(model_args.tokenizer_name ,  **A__	)
      elif model_args.model_name_or_path:
            _lowercase      			=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:
            _lowercase      			=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'	)
            _lowercase      			=AutoModelForMaskedLM.from_config(A__	)

      model.resize_token_embeddings(len(A__	)	)

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

      _lowercase      			='max_length' if data_args.pad_to_max_length else False

      def tokenize_function(A__      :  Dict	):
            # Remove empty lines
            _lowercase      			=[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	)

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

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

      # Initialize our Trainer
      _lowercase      			=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:
                  _lowercase      			=last_checkpoint
            elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path	):
                  _lowercase      			=model_args.model_name_or_path
            else:
                  _lowercase      			=None
            _lowercase      			=trainer.train(resume_from_checkpoint=A__	)
            trainer.save_model()  # Saves the tokenizer too for easy upload

            _lowercase      			=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
      _lowercase      			={}
      if training_args.do_eval:
            logger.info('*** Evaluate ***'	)

            _lowercase      			=trainer.evaluate()

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

            _lowercase      			=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 	a (     A__      :  Optional[int]	)       -> Tuple:





      """simple docstring"""



      main()


if __name__ == "__main__":
  main()





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase_   							=  HfArgumentParser(InitializationArguments)
lowercase_   							=  parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase_   							=  AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase_   							=  {
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowercase_   							=  AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase_   							=  AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)


