





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





     """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        SCREAMING_SNAKE_CASE     :
   _UpperCamelCase					:	Optional[str]				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'The name of the dataset to use (via the datasets library).'}   )
   _UpperCamelCase					:	Optional[str]				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}   )
   _UpperCamelCase					:	Optional[str]				      = field(default=UpperCAmelCase    ,			metadata={'help': 'The input training data file (a text file).'}   )
   _UpperCamelCase					:	Optional[str]				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}    ,			)
   _UpperCamelCase					:	Optional[str]				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}    ,			)
   _UpperCamelCase					:	Optional[str]				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}    ,			)
   _UpperCamelCase					:	bool				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}   )
   _UpperCamelCase					:	Optional[int]				      = field(
       default=5    ,			metadata={
           'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
       }    ,			)
   _UpperCamelCase					:	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.'
           )
       }    ,			)
   _UpperCamelCase					:	Optional[int]				      = field(
       default=UpperCAmelCase    ,			metadata={'help': 'The number of processes to use for the preprocessing.'}    ,			)
   _UpperCamelCase					:	float				      = field(
       default=0.15    ,			metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}   )
   _UpperCamelCase					:	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  SCREAMING_SNAKE_CASE_							(					self    :       int )->     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     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     List[Any]:
  with open(_SCREAMING_SNAKE_CASE	,		'r'	,		encoding='utf-8'	) as f:
    lowercase__			    =						[json.loads(_SCREAMING_SNAKE_CASE	) for line in f.read().splitlines() if (len(_SCREAMING_SNAKE_CASE	) > 0 and not line.isspace())]
  assert len(_SCREAMING_SNAKE_CASE	) == len(_SCREAMING_SNAKE_CASE	)

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




def     __UpperCamelCase						()       ->     List[str]:
  # 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.

  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'	,		_SCREAMING_SNAKE_CASE	)

  # 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(_SCREAMING_SNAKE_CASE	,		data_files=_SCREAMING_SNAKE_CASE	)
  # 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	,		**_SCREAMING_SNAKE_CASE	)
  elif model_args.model_name_or_path:
    lowercase__			    =						AutoConfig.from_pretrained(model_args.model_name_or_path	,		**_SCREAMING_SNAKE_CASE	)
  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	,		**_SCREAMING_SNAKE_CASE	)
  elif model_args.model_name_or_path:
    lowercase__			    =						AutoTokenizer.from_pretrained(model_args.model_name_or_path	,		**_SCREAMING_SNAKE_CASE	)
  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=_SCREAMING_SNAKE_CASE	,		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(_SCREAMING_SNAKE_CASE	)

  model.resize_token_embeddings(len(_SCREAMING_SNAKE_CASE	)	)

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

  lowercase__			    =						datasets.map(
      _SCREAMING_SNAKE_CASE	,		batched=_SCREAMING_SNAKE_CASE	,		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=_SCREAMING_SNAKE_CASE	,		mlm_probability=data_args.mlm_probability	)

  # Initialize our Trainer
  lowercase__			    =						Trainer(
      model=_SCREAMING_SNAKE_CASE	,		args=_SCREAMING_SNAKE_CASE	,		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=_SCREAMING_SNAKE_CASE	,		data_collator=_SCREAMING_SNAKE_CASE	,		)

  # 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=_SCREAMING_SNAKE_CASE	)
    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(_SCREAMING_SNAKE_CASE	,		'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(_SCREAMING_SNAKE_CASE	,		'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						(_SCREAMING_SNAKE_CASE	)       ->     Optional[int]:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
      main()









from string import ascii_uppercase

lowercase_					     =	{str(ord(c) - 55): c for c in ascii_uppercase}




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     str:
  if isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
    raise TypeError('int() can\'t convert non-string with explicit base'	)
  if num < 0:
    raise ValueError('parameter must be positive int'	)
  if isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
    raise TypeError('\'str\' object cannot be interpreted as an integer'	)
  if isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
    raise TypeError('\'float\' object cannot be interpreted as an integer'	)
  if base in (0, 1):
    raise ValueError('base must be >= 2'	)
  if base > 36:
    raise ValueError('base must be <= 36'	)
  lowercase__			    =						''
  lowercase__			    =						0
  lowercase__			    =						0
  while div != 1:
    lowercase__    ,					lowercase__			    =						divmod(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)
    if base >= 11 and 9 < mod < 36:
      lowercase__			    =						ALPHABET_VALUES[str(_SCREAMING_SNAKE_CASE	)]
    else:
      lowercase__			    =						str(_SCREAMING_SNAKE_CASE	)
    new_value += actual_value
    lowercase__			    =						num // base
    lowercase__			    =						div
    if div == 0:
      return str(new_value[::-1]	)
    elif div == 1:
      new_value += str(_SCREAMING_SNAKE_CASE	)
      return str(new_value[::-1]	)

  return new_value[::-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      for base in range(2, 37):
            for num in range(1_000):
                  assert int(decimal_to_any(num, base), base) == num, (
                      num,
                      base,
                      decimal_to_any(num, base),
                      int(decimal_to_any(num, base), base),
                  )


