





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__						:      List[Any]	=    logging.getLogger(__name__)
lowercase__						:      Dict	=    list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowercase__						:      Dict	=    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class     __lowerCAmelCase					:






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





       def snake_case__						(							self       :		Optional[Any]					)   ->					str:

            '''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					:






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

            '''simple docstring'''




            if self.train_file is not None:
                 _UpperCamelCase       =	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       =	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__    (    lowercase :					Union[str, Any], lowercase :					Union[str, Any]				)		->		int:





     """simple docstring"""

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

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







def  a__    (    )		->		Optional[Any]:





     """simple docstring"""

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

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

     model.resize_token_embeddings(len(lowercase				)				)

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

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

     def tokenize_function(lowercase :					Tuple				):
          # Remove empty lines
          _UpperCamelCase       =	[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				)

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

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

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

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

          _UpperCamelCase       =	trainer.evaluate()

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

          _UpperCamelCase       =	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  a__    (    lowercase :					Optional[int]				)		->		List[Any]:





     """simple docstring"""

     main()


if __name__ == "__main__":
   main()










'''simple docstring'''

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase__						:      Any	=    logging.getLogger(__name__)







def  a__    (    lowercase :					Optional[Any], lowercase :					Tuple				)		->		Any:





     """simple docstring"""

     return (preds == labels).mean()
@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					str						    =   field(
           metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,						)

@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					str						    =   field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()			)}			)
       _snake_case     :					str						    =   field(metadata={'help': 'Should contain the data files for the task.'}			)
       _snake_case     :					int						    =   field(
           default=1_2_8						,						metadata={
               'help': (
                   'The maximum total input sequence length after tokenization. Sequences longer '
                   'than this will be truncated, sequences shorter will be padded.'
               )
           }						,						)
       _snake_case     :					bool						    =   field(
           default=__magic_name__						,						metadata={'help': 'Overwrite the cached training and evaluation sets'}			)







def  a__    (    )		->		Optional[Any]:





     """simple docstring"""

     _UpperCamelCase       =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
     _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir				)
         and os.listdir(training_args.output_dir				)
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
              ''' --overwrite_output_dir to overcome.'''				)

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, )
     logger.warning(
         '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''', training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.local_rank != -1				), 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
     set_seed(training_args.seed				)

     try:
          _UpperCamelCase       =	processors[data_args.task_name]()
          _UpperCamelCase       =	processor.get_labels()
          _UpperCamelCase       =	len(lowercase				)
     except KeyError:
          raise ValueError('''Task not found: %s''' % (data_args.task_name)				)

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     _UpperCamelCase       =	AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=lowercase, finetuning_task=data_args.task_name, cache_dir=model_args.cache_dir, )
     _UpperCamelCase       =	AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, )
     _UpperCamelCase       =	AutoModelForMultipleChoice.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, )

     # Get datasets
     _UpperCamelCase       =	(
         MultipleChoiceDataset(
             data_dir=data_args.data_dir, tokenizer=lowercase, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.train, )
         if training_args.do_train
         else None
     )
     _UpperCamelCase       =	(
         MultipleChoiceDataset(
             data_dir=data_args.data_dir, tokenizer=lowercase, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.dev, )
         if training_args.do_eval
         else None
     )

     def compute_metrics(lowercase :					EvalPrediction				) -> Dict:
          _UpperCamelCase       =	np.argmax(p.predictions, axis=1				)
          return {"acc": simple_accuracy(lowercase, p.label_ids				)}

     # Data collator
     _UpperCamelCase       =	DataCollatorWithPadding(lowercase, pad_to_multiple_of=8				) if training_args.fpaa else None

     # Initialize our Trainer
     _UpperCamelCase       =	Trainer(
         model=lowercase, args=lowercase, train_dataset=lowercase, eval_dataset=lowercase, compute_metrics=lowercase, data_collator=lowercase, )

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_master():
               tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
     _UpperCamelCase       =	{}
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''				)

          _UpperCamelCase       =	trainer.evaluate()

          _UpperCamelCase       =	os.path.join(training_args.output_dir, '''eval_results.txt'''				)
          if trainer.is_world_master():
               with open(lowercase, '''w'''				) as writer:
                    logger.info('''***** Eval results *****'''				)
                    for key, value in result.items():
                         logger.info('''  %s = %s''', lowercase, lowercase				)
                         writer.write('''%s = %s\n''' % (key, value)				)

                    results.update(lowercase				)

     return results







def  a__    (    lowercase :					Tuple				)		->		List[Any]:





     """simple docstring"""

     main()


if __name__ == "__main__":
   main()



