


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


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







@dataclass
class      lowerCAmelCase__       :




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


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            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__       :




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "The name of the dataset to use (via the datasets library)."})
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "The configuration name of the dataset to use (via the datasets library)."})
     __SCREAMING_SNAKE_CASE  			= field(default=a     ,    metadata={"help": "The input training data file (a text file)."})
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    metadata={"help": "Overwrite the cached training and evaluation sets"})
     __SCREAMING_SNAKE_CASE  			= 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  			= field(
         default=a     ,    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  			= field(
         default=a     ,    metadata={"help": "The number of processes to use for the preprocessing."}     ,    )
     __SCREAMING_SNAKE_CASE  			= field(
         default=0.15     ,    metadata={"help": "Ratio of tokens to mask for masked language modeling loss"})
     __SCREAMING_SNAKE_CASE  			= field(
         default=a     ,    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)			->				Union[str, Any]:
            if self.train_file is not None:
                   _A      :       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:
                   _A      :       int				    = 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				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  Dict						):
       with open(UpperCamelCase__				,							"r"				,							encoding="utf-8"						) as f:
              _A      :       Union[str, Any]				    = [json.loads(UpperCamelCase__						) for line in f.read().splitlines() if (len(UpperCamelCase__						) > 0 and not line.isspace())]
       assert len(UpperCamelCase__						) == len(UpperCamelCase__						)

       _A      :       Dict				    = {c: dataset[c] for c in dataset.column_names}
       _A      :       str				    = refs
       return Dataset.from_dict(UpperCamelCase__						)
def 							_UpperCAmelCase				():
       # 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.

       _A      :       Union[str, Any]				    = 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.
              _A    ,			_A    ,			_A      :       str				    = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
       else:
              _A    ,			_A    ,			_A      :       Optional[int]				    = parser.parse_args_into_dataclasses()

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

       _A      :       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:
              _A      :       List[Any]				    = AutoTokenizer.from_pretrained(model_args.tokenizer_name				,							**UpperCamelCase__						)
       elif model_args.model_name_or_path:
              _A      :       Any				    = 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:
              _A      :       Dict				    = 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"						)
              _A      :       Tuple				    = AutoModelForMaskedLM.from_config(UpperCamelCase__						)

       model.resize_token_embeddings(len(UpperCamelCase__						)						)

       # Preprocessing the datasets.
       # First we tokenize all the texts.
       if training_args.do_train:
              _A      :       str				    = datasets["train"].column_names
       else:
              _A      :       Tuple				    = datasets["validation"].column_names
       _A      :       Union[str, Any]				    = "text" if "text" in column_names else column_names[0]

       _A      :       List[Any]				    = "max_length" if data_args.pad_to_max_length else False

       def tokenize_function(UpperCamelCase__     :  Dict						):
              # Remove empty lines
              _A      :       Union[str, Any]				    = [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						)

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

       # Data collator
       # This one will take care of randomly masking the tokens.
       _A      :       Union[str, Any]				    = DataCollatorForWholeWordMask(tokenizer=UpperCamelCase__				,							mlm_probability=data_args.mlm_probability						)

       # Initialize our Trainer
       _A      :       Dict				    = 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:
                     _A      :       Dict				    = last_checkpoint
              elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path						):
                     _A      :       Tuple				    = model_args.model_name_or_path
              else:
                     _A      :       Tuple				    = None
              _A      :       str				    = trainer.train(resume_from_checkpoint=UpperCamelCase__						)
              trainer.save_model()  # Saves the tokenizer too for easy upload

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

              _A      :       List[Any]				    = trainer.evaluate()

              _A      :       Optional[Any]				    = math.exp(eval_output["eval_loss"]						)
              _A      :       Dict				    = perplexity

              _A      :       Dict				    = 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 							_UpperCAmelCase				(UpperCamelCase__     :  Tuple						):
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
    main()








def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     while b:
          UpperCamelCase__				,     UpperCamelCase__			:   int       				=  b, a % b
     return a







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     return a if b == 0 else euclidean_gcd_recursive(__lowerCAmelCase					,    a % b			)







def 		SCREAMING_SNAKE_CASE							(					) ->	str:
     print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3					,    5			)}'			)
     print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5					,    3			)}'			)
     print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1					,    3			)}'			)
     print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3					,    6			)}'			)
     print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6					,    3			)}'			)

     print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3					,    5			)}'			)
     print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5					,    3			)}'			)
     print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1					,    3			)}'			)
     print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3					,    6			)}'			)
     print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6					,    3			)}'			)


if __name__ == "__main__":
 main()