





"""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


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  _SCREAMING_SNAKE_CASE:
       SCREAMING_SNAKE_CASE_       :				Optional[str]     						=  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_       :				Optional[str]     						=  field(
           default=A   ,     metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(A  )}   ,     )
       SCREAMING_SNAKE_CASE_       :				Optional[str]     						=  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_       :				Optional[str]     						=  field(
           default=A   ,     metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
       SCREAMING_SNAKE_CASE_       :				Optional[str]     						=  field(
           default=A   ,     metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}  )
       SCREAMING_SNAKE_CASE_       :				Optional[str]     						=  field(
           default=A   ,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,     )
       SCREAMING_SNAKE_CASE_       :				bool     						=  field(
           default=A   ,     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=A   ,     metadata={
               '''help''': (
                   '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                   '''with private models).'''
               )
           }   ,     )




       def    _UpperCamelCase					(						self							)      ->				List[Any]:





             """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:
       SCREAMING_SNAKE_CASE_       :				Optional[str]     						=  field(
           default=A   ,     metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}  )
       SCREAMING_SNAKE_CASE_       :				Optional[str]     						=  field(
           default=A   ,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}  )
       SCREAMING_SNAKE_CASE_       :				Optional[str]     						=  field(default=A   ,     metadata={'''help''': '''The input training data file (a text file).'''}  )
       SCREAMING_SNAKE_CASE_       :				Optional[str]     						=  field(
           default=A   ,     metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}   ,     )
       SCREAMING_SNAKE_CASE_       :				Optional[str]     						=  field(
           default=A   ,     metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}   ,     )
       SCREAMING_SNAKE_CASE_       :				Optional[str]     						=  field(
           default=A   ,     metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}   ,     )
       SCREAMING_SNAKE_CASE_       :				bool     						=  field(
           default=A   ,     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=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_       :				Optional[int]     						=  field(
           default=A   ,     metadata={'''help''': '''The number of processes to use for the preprocessing.'''}   ,     )
       SCREAMING_SNAKE_CASE_       :				float     						=  field(
           default=0.15   ,     metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}  )
       SCREAMING_SNAKE_CASE_       :				bool     						=  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    _UpperCamelCase					(						self							)      ->				Union[str, Any]:





             """simple docstring"""






             if self.train_file is not None:
                   __SCREAMING_SNAKE_CASE       :List[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:
                   __SCREAMING_SNAKE_CASE       :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 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		Dict							)      ->				str:
      with open(a_			,       '''r'''			,       encoding='''utf-8'''							) as f:
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  [json.loads(a_							) for line in f.read().splitlines() if (len(a_							) > 0 and not line.isspace())]
      assert len(a_							) == len(a_							)

      __SCREAMING_SNAKE_CASE       :int		    =  {c: dataset[c] for c in dataset.column_names}
      __SCREAMING_SNAKE_CASE       :Dict		    =  refs
      return Dataset.from_dict(a_							)



def 					__lowerCamelCase       (							)      ->				List[Any]:
      # 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.

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

      # Detecting last checkpoint.
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  None
      if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
            __SCREAMING_SNAKE_CASE       :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'''			,       a_							)

      # 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.
            __SCREAMING_SNAKE_CASE       :Tuple		    =  load_dataset(data_args.dataset_name			,       data_args.dataset_config_name							)
            if "validation" not in datasets.keys():
                  __SCREAMING_SNAKE_CASE       :List[Any]		    =  load_dataset(
                      data_args.dataset_name			,       data_args.dataset_config_name			,       split=f'''train[:{data_args.validation_split_percentage}%]'''			,       )
                  __SCREAMING_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:
            __SCREAMING_SNAKE_CASE       :Any		    =  {}
            if data_args.train_file is not None:
                  __SCREAMING_SNAKE_CASE       :str		    =  data_args.train_file
            if data_args.validation_file is not None:
                  __SCREAMING_SNAKE_CASE       :List[Any]		    =  data_args.validation_file
            __SCREAMING_SNAKE_CASE       :List[str]		    =  data_args.train_file.split('''.'''							)[-1]
            if extension == "txt":
                  __SCREAMING_SNAKE_CASE       :str		    =  '''text'''
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  load_dataset(a_			,       data_files=a_							)
      # 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.
      __SCREAMING_SNAKE_CASE       :List[str]		    =  {
          '''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:
            __SCREAMING_SNAKE_CASE       :Any		    =  AutoConfig.from_pretrained(model_args.config_name			,       **a_							)
      elif model_args.model_name_or_path:
            __SCREAMING_SNAKE_CASE       :Dict		    =  AutoConfig.from_pretrained(model_args.model_name_or_path			,       **a_							)
      else:
            __SCREAMING_SNAKE_CASE       :Tuple		    =  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}'''							)

      __SCREAMING_SNAKE_CASE       :List[str]		    =  {
          '''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:
            __SCREAMING_SNAKE_CASE       :Dict		    =  AutoTokenizer.from_pretrained(model_args.tokenizer_name			,       **a_							)
      elif model_args.model_name_or_path:
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  AutoTokenizer.from_pretrained(model_args.model_name_or_path			,       **a_							)
      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:
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  AutoModelForMaskedLM.from_pretrained(
                model_args.model_name_or_path			,       from_tf=bool('''.ckpt''' in model_args.model_name_or_path							)			,       config=a_			,       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'''							)
            __SCREAMING_SNAKE_CASE       :int		    =  AutoModelForMaskedLM.from_config(a_							)

      model.resize_token_embeddings(len(a_							)							)

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

      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  '''max_length''' if data_args.pad_to_max_length else False

      def tokenize_function(a_					:		List[str]							):
            # Remove empty lines
            __SCREAMING_SNAKE_CASE       :Tuple		    =  [line for line in examples['''text'''] if len(a_							) > 0 and not line.isspace()]
            return tokenizer(examples['''text''']			,       padding=a_			,       truncation=a_			,       max_length=data_args.max_seq_length							)

      __SCREAMING_SNAKE_CASE       :str		    =  datasets.map(
          a_			,       batched=a_			,       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:
            __SCREAMING_SNAKE_CASE       :Tuple		    =  add_chinese_references(tokenized_datasets['''train''']			,       data_args.train_ref_file							)
      if data_args.validation_ref_file is not None:
            __SCREAMING_SNAKE_CASE       :Dict		    =  add_chinese_references(
                tokenized_datasets['''validation''']			,       data_args.validation_ref_file							)
      # If we have ref files, need to avoid it removed by trainer
      __SCREAMING_SNAKE_CASE       :Tuple		    =  data_args.train_ref_file or data_args.validation_ref_file
      if has_ref:
            __SCREAMING_SNAKE_CASE       :str		    =  False

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

      # Initialize our Trainer
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  Trainer(
          model=a_			,       args=a_			,       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=a_			,       data_collator=a_			,       )

      # Training
      if training_args.do_train:
            if last_checkpoint is not None:
                  __SCREAMING_SNAKE_CASE       :str		    =  last_checkpoint
            elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path							):
                  __SCREAMING_SNAKE_CASE       :int		    =  model_args.model_name_or_path
            else:
                  __SCREAMING_SNAKE_CASE       :Any		    =  None
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  trainer.train(resume_from_checkpoint=a_							)
            trainer.save_model()  # Saves the tokenizer too for easy upload

            __SCREAMING_SNAKE_CASE       :List[Any]		    =  os.path.join(training_args.output_dir			,       '''train_results.txt'''							)
            if trainer.is_world_process_zero():
                  with open(a_			,       '''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
      __SCREAMING_SNAKE_CASE       :Any		    =  {}
      if training_args.do_eval:
            logger.info('''*** Evaluate ***'''							)

            __SCREAMING_SNAKE_CASE       :str		    =  trainer.evaluate()

            __SCREAMING_SNAKE_CASE       :Any		    =  math.exp(eval_output['''eval_loss''']							)
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  perplexity

            __SCREAMING_SNAKE_CASE       :List[Any]		    =  os.path.join(training_args.output_dir			,       '''eval_results_mlm_wwm.txt'''							)
            if trainer.is_world_process_zero():
                  with open(a_			,       '''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 					__lowerCamelCase       (							a_					:		Dict							)      ->				List[str]:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
  main()






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		str			,       a_					:		str							)      ->				str:
      __SCREAMING_SNAKE_CASE       :int		    =  len(a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  len(a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  (
          first_str_length if first_str_length > second_str_length else second_str_length
      )
      __SCREAMING_SNAKE_CASE       :list		    =  []
      for char_count in range(a_							):
            if char_count < first_str_length:
                  output_list.append(first_str[char_count]							)
            if char_count < second_str_length:
                  output_list.append(second_str[char_count]							)
      return "".join(a_							)


if __name__ == "__main__":
  print(alternative_string_arrange("AB", "XYZ"), end=" ")