





'''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


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

@dataclass
class       lowerCAmelCase_						:
 __lowerCamelCase  :							Optional[Any]				     =    field(
     default=_UpperCAmelCase  ,metadata={
         "help": (
             "The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch."
         )
     }  ,)
 __lowerCamelCase  :							List[Any]				     =    field(
     default=_UpperCAmelCase  ,metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_UpperCAmelCase						)}  ,)
 __lowerCamelCase  :							List[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"
         )
     }  ,)
 __lowerCamelCase  :							Optional[int]				     =    field(
     default=_UpperCAmelCase  ,metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
 __lowerCamelCase  :							Any				     =    field(
     default=_UpperCAmelCase  ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
 __lowerCamelCase  :							List[Any]				     =    field(
     default=_UpperCAmelCase  ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,)
 __lowerCamelCase  :							Any				     =    field(
     default=_UpperCAmelCase  ,metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}  ,)
 __lowerCamelCase  :							Any				     =    field(
     default="main"  ,metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}  ,)
 __lowerCamelCase  :							Dict				     =    field(
     default=_UpperCAmelCase  ,metadata={
         "help": (
             "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
             "with private models)."
         )
     }  ,)




 def 							_snake_case      (		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       lowerCAmelCase_						:
 __lowerCamelCase  :							Any				     =    field(
     default=_UpperCAmelCase  ,metadata={"help": "The name of the dataset to use (via the datasets library)."}						)
 __lowerCamelCase  :							str				     =    field(
     default=_UpperCAmelCase  ,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}						)
 __lowerCamelCase  :							str				     =    field(default=_UpperCAmelCase  ,metadata={"help": "The input training data file (a text file)."}						)
 __lowerCamelCase  :							Tuple				     =    field(
     default=_UpperCAmelCase  ,metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}  ,)
 __lowerCamelCase  :							Any				     =    field(
     default=_UpperCAmelCase  ,metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}  ,)
 __lowerCamelCase  :							List[Any]				     =    field(
     default=_UpperCAmelCase  ,metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}  ,)
 __lowerCamelCase  :							Any				     =    field(
     default=_UpperCAmelCase  ,metadata={"help": "Overwrite the cached training and evaluation sets"}						)
 __lowerCamelCase  :							str				     =    field(
     default=5  ,metadata={
         "help": "The percentage of the train set used as validation set in case there\'s no validation split"
     }  ,)
 __lowerCamelCase  :							Optional[Any]				     =    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."
         )
     }  ,)
 __lowerCamelCase  :							Any				     =    field(
     default=_UpperCAmelCase  ,metadata={"help": "The number of processes to use for the preprocessing."}  ,)
 __lowerCamelCase  :							Dict				     =    field(
     default=0.1_5  ,metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}						)
 __lowerCamelCase  :							Tuple				     =    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 							_snake_case      (		self						)			->							Optional[int]:
     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 		__a(SCREAMING_SNAKE_CASE_     :    Optional[int]						,				SCREAMING_SNAKE_CASE_     :    Dict			):



    '''simple docstring'''





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

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


def 		__a():



    '''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			=					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
    else:
        _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"						,				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.
        _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(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.
    _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						,				**UpperCAmelCase__			)
    elif model_args.model_name_or_path:
        _lowerCAmelCase			=					AutoConfig.from_pretrained(model_args.model_name_or_path						,				**UpperCAmelCase__			)
    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						,				**UpperCAmelCase__			)
    elif model_args.model_name_or_path:
        _lowerCAmelCase			=					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:
        _lowerCAmelCase			=					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"			)
        _lowerCAmelCase			=					AutoModelForMaskedLM.from_config(UpperCAmelCase__			)

    model.resize_token_embeddings(len(UpperCAmelCase__			)			)

    # 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_     :    Tuple			):
        # Remove empty lines
        _lowerCAmelCase			=					[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			)

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

    # Initialize our Trainer
    _lowerCAmelCase			=					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:
            _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=UpperCAmelCase__			)
        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(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
    _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(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 		__a(SCREAMING_SNAKE_CASE_     :    List[str]			):



    '''simple docstring'''





    main()


if __name__ == "__main__":
       main()



'''simple docstring'''




import math




def 							lowerCamelCase	(      UpperCAmelCase__       :					int )			->      int:

							if not isinstance(UpperCAmelCase__      ,      UpperCAmelCase__ ):
														lowercase_					:			List[Any]										=       F'''Input value of [number={number}] must be an integer'''
														raise TypeError(UpperCAmelCase__ )

							if number < 1:
														lowercase_					:			Tuple										=       F'''Input value of [number={number}] must be > 0'''
														raise ValueError(UpperCAmelCase__ )
							elif number == 1:
														return 3
							elif number == 2:
														return 5
							else:
														lowercase_					:			Union[str, Any]										=       int(math.log(number // 3      ,      2 ) ) + 2

														lowercase_					:			Any										=       [3, 5]
														lowercase_					:			List[Any]										=       2
														lowercase_					:			Optional[Any]										=       3
														for block in range(1      ,      UpperCAmelCase__ ):
																					for _ in range(UpperCAmelCase__ ):
																												proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
																												proth_index += 1
																					increment *= 2

							return proth_list[number - 1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						for number in range(11):
												_lowercase						: Optional[Any]						=						0
												try:
																		_lowercase						: str						=						proth(number)
												except ValueError:
																		print(f"""ValueError: there is no {number}th Proth number""")
																		continue

												print(f"""The {number}th Proth number: {value}""")





