


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



@dataclass
class   _a      :
    a_    :				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.'
            )
        }		,		)
    a_    :				Optional[str]             =    field(
        default=_lowerCAmelCase		,		metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_lowerCAmelCase )}		,		)
    a_    :				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'
            )
        }		,		)
    a_    :				Optional[str]             =    field(
        default=_lowerCAmelCase		,		metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
    a_    :				Optional[str]             =    field(
        default=_lowerCAmelCase		,		metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
    a_    :				Optional[str]             =    field(
        default=_lowerCAmelCase		,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}		,		)
    a_    :				bool             =    field(
        default=_lowerCAmelCase		,		metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}		,		)
    a_    :				str             =    field(
        default='main'		,		metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}		,		)
    a_    :				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							:					List[str]				):







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







          if self.train_file is not None:
                lowerCamelCase__           =		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:
                lowerCamelCase__           =		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						(				_a:     Any		,   _a:     Tuple							)->		Optional[Any]:
      '''simple docstring'''


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

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



def   snake_case						(				)->		List[Any]:
      '''simple docstring'''


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

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

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

      model.resize_token_embeddings(len(_a							)							)

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

      lowerCamelCase__           =		'max_length' if data_args.pad_to_max_length else False

      def tokenize_function(_a:     Optional[int]							):
            # Remove empty lines
            lowerCamelCase__           =		[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							)

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

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

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

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

            lowerCamelCase__           =		trainer.evaluate()

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

            lowerCamelCase__           =		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   snake_case						(				_a:     Optional[int]							)->		Optional[Any]:
      '''simple docstring'''


      main()


if __name__ == "__main__":
   main()







"""simple docstring"""


import argparse
import json

from tqdm import tqdm



def   snake_case						(				)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		argparse.ArgumentParser()

      # Required parameters
      parser.add_argument(
          '--src_path'		,   type=_a		,   default='biencoder-nq-dev.json'		,   help='Path to raw DPR training data'		,   )
      parser.add_argument(
          '--evaluation_set'		,   type=_a		,   help='where to store parsed evaluation_set file'		,   )
      parser.add_argument(
          '--gold_data_path'		,   type=_a		,   help='where to store parsed gold_data_path file'		,   )
      lowerCamelCase__           =		parser.parse_args()

      with open(args.src_path		,   'r'							) as src_file, open(args.evaluation_set		,   'w'							) as eval_file, open(
          args.gold_data_path		,   'w'							) as gold_file:
            lowerCamelCase__           =		json.load(_a							)
            for dpr_record in tqdm(_a							):
                  lowerCamelCase__           =		dpr_record['question']
                  lowerCamelCase__           =		[context['title'] for context in dpr_record['positive_ctxs']]
                  eval_file.write(question + '\n'							)
                  gold_file.write('\t'.join(_a							) + '\n'							)


if __name__ == "__main__":
   main()



