



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


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







@dataclass
class 		snake_case						:



   '''simple docstring'''






   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,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=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(SCREAMING_SNAKE_CASE_  )}					,)
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_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_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}					,)
   snake_case_					: bool          =		field(
       default=SCREAMING_SNAKE_CASE_					,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=SCREAMING_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							:			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 		snake_case						:



   '''simple docstring'''






   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}  )
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}  )
   snake_case_					: Optional[str]          =		field(default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """The input training data file (a text file)."""}  )
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}					,)
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}					,)
   snake_case_					: Optional[str]          =		field(
       default=SCREAMING_SNAKE_CASE_					,metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}					,)
   snake_case_					: bool          =		field(
       default=SCREAMING_SNAKE_CASE_					,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=SCREAMING_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_					: Optional[int]          =		field(
       default=SCREAMING_SNAKE_CASE_					,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=SCREAMING_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							:			List[Any])				->				Any:

    """simple docstring"""







    if self.train_file is not None:
     _snake_case    :  Any					=					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:
     _snake_case    :  Optional[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       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	List[Any]				)  ->     Tuple:
 with open(SCREAMING_SNAKE_CASE__				,       """r"""				,       encoding="""utf-8"""				) as f:
  _snake_case    :  str					=					[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__				)

 _snake_case    :  Optional[Any]					=					{c: dataset[c] for c in dataset.column_names}
 _snake_case    :  List[Any]					=					refs
 return Dataset.from_dict(SCREAMING_SNAKE_CASE__				)



def       lowercase       (   )  ->     Dict:
 # 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.

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

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

 _snake_case    :  Optional[int]					=					{
     """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:
  _snake_case    :  List[str]					=					AutoTokenizer.from_pretrained(model_args.tokenizer_name				,       **SCREAMING_SNAKE_CASE__				)
 elif model_args.model_name_or_path:
  _snake_case    :  List[str]					=					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:
  _snake_case    :  Dict					=					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"""				)
  _snake_case    :  Optional[int]					=					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:
  _snake_case    :  int					=					datasets["""train"""].column_names
 else:
  _snake_case    :  Any					=					datasets["""validation"""].column_names
 _snake_case    :  Optional[Any]					=					"""text""" if """text""" in column_names else column_names[0]

 _snake_case    :  Optional[int]					=					"""max_length""" if data_args.pad_to_max_length else False

 def tokenize_function(SCREAMING_SNAKE_CASE__       :	int				):
  # Remove empty lines
  _snake_case    :  List[str]					=					[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				)

 _snake_case    :  int					=					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:
  _snake_case    :  Optional[Any]					=					add_chinese_references(tokenized_datasets["""train"""]				,       data_args.train_ref_file				)
 if data_args.validation_ref_file is not None:
  _snake_case    :  int					=					add_chinese_references(
      tokenized_datasets["""validation"""]				,       data_args.validation_ref_file				)
 # If we have ref files, need to avoid it removed by trainer
 _snake_case    :  Union[str, Any]					=					data_args.train_ref_file or data_args.validation_ref_file
 if has_ref:
  _snake_case    :  List[Any]					=					False

 # Data collator
 # This one will take care of randomly masking the tokens.
 _snake_case    :  Dict					=					DataCollatorForWholeWordMask(tokenizer=SCREAMING_SNAKE_CASE__				,       mlm_probability=data_args.mlm_probability				)

 # Initialize our Trainer
 _snake_case    :  Any					=					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:
   _snake_case    :  Tuple					=					last_checkpoint
  elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path				):
   _snake_case    :  Any					=					model_args.model_name_or_path
  else:
   _snake_case    :  Optional[Any]					=					None
  _snake_case    :  str					=					trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE__				)
  trainer.save_model()  # Saves the tokenizer too for easy upload

  _snake_case    :  Any					=					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
 _snake_case    :  Optional[Any]					=					{}
 if training_args.do_eval:
  logger.info("""*** Evaluate ***"""				)

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

  _snake_case    :  Dict					=					math.exp(eval_output["""eval_loss"""]				)
  _snake_case    :  Tuple					=					perplexity

  _snake_case    :  Tuple					=					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       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				)  ->     Optional[int]:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
  main()





def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int = 1_000				)  ->     int:
 _snake_case	,    _snake_case    :  str					=					1, 1
 _snake_case    :  List[Any]					=					2
 while True:
  _snake_case    :  Union[str, Any]					=					0
  _snake_case    :  int					=					fa + fa
  _snake_case	,    _snake_case    :  Union[str, Any]					=					fa, f
  index += 1
  for _ in str(SCREAMING_SNAKE_CASE__				):
   i += 1
  if i == n:
   break
 return index


if __name__ == "__main__":
  print(solution(int(str(input()).strip())))
