
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


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



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


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


   '''simple docstring'''




   if self.train_file is not None:
     lowercase__  :   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:
     lowercase__  :   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 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Union[str, Any]:


  '''simple docstring'''






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

  lowercase__  :   Any    		=      {c: dataset[c] for c in dataset.column_names}
  lowercase__  :   List[str]    		=      refs
  return Dataset.from_dict(lowercase_   )

def 		UpperCamelCase	(      )		->	Any:


  '''simple docstring'''






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

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

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

  lowercase__  :   List[str]    		=      {
      """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__  :   Optional[Any]    		=      AutoTokenizer.from_pretrained(model_args.tokenizer_name , **lowercase_   )
  elif model_args.model_name_or_path:
    lowercase__  :   Any    		=      AutoTokenizer.from_pretrained(model_args.model_name_or_path , **lowercase_   )
  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__  :   List[str]    		=      AutoModelForMaskedLM.from_pretrained(
        model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path   ) , config=lowercase_ , 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__  :   Any    		=      AutoModelForMaskedLM.from_config(lowercase_   )

  model.resize_token_embeddings(len(lowercase_   )   )

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

  lowercase__  :   List[Any]    		=      """max_length""" if data_args.pad_to_max_length else False

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

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

  # Data collator
  # This one will take care of randomly masking the tokens.
  lowercase__  :   Tuple    		=      DataCollatorForWholeWordMask(tokenizer=lowercase_ , mlm_probability=data_args.mlm_probability   )

  # Initialize our Trainer
  lowercase__  :   int    		=      Trainer(
      model=lowercase_ , args=lowercase_ , 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=lowercase_ , data_collator=lowercase_ , )

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

    lowercase__  :   Union[str, Any]    		=      os.path.join(training_args.output_dir , """train_results.txt"""   )
    if trainer.is_world_process_zero():
      with open(lowercase_ , """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__  :   int    		=      {}
  if training_args.do_eval:
    logger.info("""*** Evaluate ***"""   )

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

    lowercase__  :   int    		=      math.exp(eval_output["""eval_loss"""]   )
    lowercase__  :   Union[str, Any]    		=      perplexity

    lowercase__  :   Any    		=      os.path.join(training_args.output_dir , """eval_results_mlm_wwm.txt"""   )
    if trainer.is_world_process_zero():
      with open(lowercase_ , """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 		UpperCamelCase	(      lowercase_   )		->	List[str]:


  '''simple docstring'''






  main()


if __name__ == "__main__":
     main()








from ..utils import DummyObject, requires_backends



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     int        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     Any        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     Union[str, Any]        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     List[Any]        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     Dict        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



class       _snake_case   (	metaclass=UpperCAmelCase_							):
 __lowerCAmelCase  :     List[str]        =			['torch', 'transformers', 'onnx']


 def __init__(					self					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(self					,  ["""torch""", """transformers""", """onnx"""])


 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])



 @classmethod
 def      lowercase__     (					cls					,  *SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   requires_backends(cls					,  ["""torch""", """transformers""", """onnx"""])






