
'''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_ :  Union[str, Any]     =					logging.getLogger(__name__)
snake_case_ :  Union[str, Any]     =					list(MODEL_FOR_MASKED_LM_MAPPING.keys())
snake_case_ :  Any     =					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class 						lowercase__	:






   '''simple docstring'''







   _snake_case		   =							field(
       default=snake_case_,					metadata={
           '''help''': (
               '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
           )
       },					)
   _snake_case		   =							field(
       default=snake_case_,					metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(snake_case_   )},					)
   _snake_case		   =							field(
       default=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'''
           )
       },					)
   _snake_case		   =							field(
       default=snake_case_,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}   )
   _snake_case		   =							field(
       default=snake_case_,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}   )
   _snake_case		   =							field(
       default=snake_case_,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},					)
   _snake_case		   =							field(
       default=snake_case_,					metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''},					)
   _snake_case		   =							field(
       default='''main''',					metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''},					)
   _snake_case		   =							field(
       default=snake_case_,					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 						lowercase__	:






   '''simple docstring'''







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

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

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



def  __snake_case     (    ):
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   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''',      _UpperCAmelCase)

   # 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(_UpperCAmelCase,      data_files=_UpperCAmelCase)
   # 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,      **_UpperCAmelCase)
   elif model_args.model_name_or_path:
      UpperCamelCase       =  AutoConfig.from_pretrained(model_args.model_name_or_path,      **_UpperCAmelCase)
   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,      **_UpperCAmelCase)
   elif model_args.model_name_or_path:
      UpperCamelCase       =  AutoTokenizer.from_pretrained(model_args.model_name_or_path,      **_UpperCAmelCase)
   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=_UpperCAmelCase,      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(_UpperCAmelCase)

   model.resize_token_embeddings(len(_UpperCAmelCase))

   # 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(_UpperCAmelCase					:       Any):
      # Remove empty lines
      UpperCamelCase       =  [line for line in examples['''text'''] if len(_UpperCAmelCase) > 0 and not line.isspace()]
      return tokenizer(examples['''text'''],      padding=_UpperCAmelCase,      truncation=_UpperCAmelCase,      max_length=data_args.max_seq_length)

   UpperCamelCase       =  datasets.map(
       _UpperCAmelCase,      batched=_UpperCAmelCase,      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=_UpperCAmelCase,      mlm_probability=data_args.mlm_probability)

   # Initialize our Trainer
   UpperCamelCase       =  Trainer(
       model=_UpperCAmelCase,      args=_UpperCAmelCase,      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=_UpperCAmelCase,      data_collator=_UpperCAmelCase,      )

   # 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=_UpperCAmelCase)
      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(_UpperCAmelCase,      '''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(_UpperCAmelCase,      '''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     (    _UpperCAmelCase					:       Any):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
   main()







'''simple docstring'''


def  __snake_case     (    _UpperCAmelCase					:       list[list[float]]):
   UpperCamelCase       =  []
   for data in source_data:
      for i, el in enumerate(_UpperCAmelCase):
         if len(_UpperCAmelCase) < i + 1:
            data_lists.append([])
         data_lists[i].append(float(_UpperCAmelCase))
   return data_lists



def  __snake_case     (    _UpperCAmelCase					:       list[list[float]],      _UpperCAmelCase					:       list[int]):
   UpperCamelCase       =  []
   for dlist, weight in zip(_UpperCAmelCase,      _UpperCAmelCase):
      UpperCamelCase       =  min(_UpperCAmelCase)
      UpperCamelCase       =  max(_UpperCAmelCase)

      UpperCamelCase       =  []
      # for weight 0 score is 1 - actual score
      if weight == 0:
         for item in dlist:
            try:
               score.append(1 - ((item - mind) / (maxd - mind)))
            except ZeroDivisionError:
               score.append(1)

      elif weight == 1:
         for item in dlist:
            try:
               score.append((item - mind) / (maxd - mind))
            except ZeroDivisionError:
               score.append(0)

        # weight not 0 or 1
      else:
         UpperCamelCase       =  f'Invalid weight of {weight:f} provided'
         raise ValueError(_UpperCAmelCase)

      score_lists.append(_UpperCAmelCase)

   return score_lists



def  __snake_case     (    _UpperCAmelCase					:       list[list[float]]):
   UpperCamelCase       =  [0 for i in range(len(score_lists[0]))]

   for slist in score_lists:
      for j, ele in enumerate(_UpperCAmelCase):
         UpperCamelCase       =  final_scores[j] + ele

   return final_scores



def  __snake_case     (    _UpperCAmelCase					:       list[list[float]],      _UpperCAmelCase					:       list[int]):
   UpperCamelCase       =  get_data(_UpperCAmelCase)
   UpperCamelCase       =  calculate_each_score(_UpperCAmelCase,      _UpperCAmelCase)
   UpperCamelCase       =  generate_final_scores(_UpperCAmelCase)

   # append scores to source data
   for i, ele in enumerate(_UpperCAmelCase):
      source_data[i].append(_UpperCAmelCase)

   return source_data





