
"""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        _SCREAMING_SNAKE_CASE  :






    '''simple docstring'''



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






    '''simple docstring'''



    SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
        default=UpperCAmelCase      ,				metadata={"help": "The name of the dataset to use (via the datasets library)."}							)
    SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
        default=UpperCAmelCase      ,				metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}							)
    SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(default=UpperCAmelCase      ,				metadata={"help": "The input training data file (a text file)."}							)
    SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
        default=UpperCAmelCase      ,				metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}      ,				)
    SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
        default=UpperCAmelCase      ,				metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}      ,				)
    SCREAMING_SNAKE_CASE_:							Optional[str]							=					field(
        default=UpperCAmelCase      ,				metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}      ,				)
    SCREAMING_SNAKE_CASE_:							bool							=					field(
        default=UpperCAmelCase      ,				metadata={"help": "Overwrite the cached training and evaluation sets"}							)
    SCREAMING_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"
        }      ,				)
    SCREAMING_SNAKE_CASE_:							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."
            )
        }      ,				)
    SCREAMING_SNAKE_CASE_:							Optional[int]							=					field(
        default=UpperCAmelCase      ,				metadata={"help": "The number of processes to use for the preprocessing."}      ,				)
    SCREAMING_SNAKE_CASE_:							float							=					field(
        default=0.1_5      ,				metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}							)
    SCREAMING_SNAKE_CASE_:							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 				__lowerCamelCase	(					self	:			List[Any]  )		->  Any:







           """simple docstring"""




           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  (    SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					List[Any]						):






       """simple docstring"""



       with open(SCREAMING_SNAKE_CASE     ,  'r'     ,  encoding='utf-8'						) as f:
              _lowerCAmelCase  	=			[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						)

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






def 	__snake_case  (    ):






       """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'     ,  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.
              _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(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.
       _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     ,  **SCREAMING_SNAKE_CASE						)
       elif model_args.model_name_or_path:
              _lowerCAmelCase  	=			AutoConfig.from_pretrained(model_args.model_name_or_path     ,  **SCREAMING_SNAKE_CASE						)
       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     ,  **SCREAMING_SNAKE_CASE						)
       elif model_args.model_name_or_path:
              _lowerCAmelCase  	=			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:
              _lowerCAmelCase  	=			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'						)
              _lowerCAmelCase  	=			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:
              _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(SCREAMING_SNAKE_CASE:					str						):
              # Remove empty lines
              _lowerCAmelCase  	=			[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						)

       _lowerCAmelCase  	=			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:
              _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=SCREAMING_SNAKE_CASE     ,  mlm_probability=data_args.mlm_probability						)

       # Initialize our Trainer
       _lowerCAmelCase  	=			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:
                     _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=SCREAMING_SNAKE_CASE						)
              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(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
       _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(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 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






       """simple docstring"""



       main()


if __name__ == "__main__":
 main()








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[int(SCREAMING_SNAKE_CASE						) for i in ip_va_address.split('.'						) if i.isdigit()]
							return len(SCREAMING_SNAKE_CASE						) == 4 and all(0 <= int(SCREAMING_SNAKE_CASE						) <= 254 for octet in octets						)


if __name__ == "__main__":
	_snake_case          =						input().strip()
	_snake_case          =						'''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
	print(f'{ip} is a {valid_or_invalid} IP v4 address.')






