

"""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


_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       lowerCamelCase	:


  '''simple docstring'''







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



  def  _a      (self	):


        """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       lowerCamelCase	:


  '''simple docstring'''







  SCREAMING_SNAKE_CASE					      =  field(
      default=lowerCAmelCase__					,					metadata={'help': 'The name of the dataset to use (via the datasets library).'}   )
  SCREAMING_SNAKE_CASE					      =  field(
      default=lowerCAmelCase__					,					metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}   )
  SCREAMING_SNAKE_CASE					      =  field(default=lowerCAmelCase__					,					metadata={'help': 'The input training data file (a text file).'}   )
  SCREAMING_SNAKE_CASE					      =  field(
      default=lowerCAmelCase__					,					metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}					,					)
  SCREAMING_SNAKE_CASE					      =  field(
      default=lowerCAmelCase__					,					metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}					,					)
  SCREAMING_SNAKE_CASE					      =  field(
      default=lowerCAmelCase__					,					metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}					,					)
  SCREAMING_SNAKE_CASE					      =  field(
      default=lowerCAmelCase__					,					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=lowerCAmelCase__					,					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=lowerCAmelCase__					,					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=lowerCAmelCase__					,					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  _a      (self	):


        """simple docstring"""

        if self.train_file is not None:
              UpperCAmelCase__			:							Optional[int]							=    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:
              UpperCAmelCase__			:							Union[str, 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 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> Any:
      with open(lowerCAmelCase			,	"""r"""			,	encoding="""utf-8"""    ) as f:
            UpperCAmelCase__			:							Tuple							=    [json.loads(lowerCAmelCase    ) for line in f.read().splitlines() if (len(lowerCAmelCase    ) > 0 and not line.isspace())]
      assert len(lowerCAmelCase    ) == len(lowerCAmelCase    )

      UpperCAmelCase__			:							Optional[int]							=    {c: dataset[c] for c in dataset.column_names}
      UpperCAmelCase__			:							str							=    refs
      return Dataset.from_dict(lowerCAmelCase    )




def 					a__						(						)	-> Optional[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.

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

      # Detecting last checkpoint.
      UpperCAmelCase__			:							List[Any]							=    None
      if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
            UpperCAmelCase__			:							Any							=    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"""			,	lowerCAmelCase    )

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

      UpperCAmelCase__			:							Any							=    {
          """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:
            UpperCAmelCase__			:							List[Any]							=    AutoTokenizer.from_pretrained(model_args.tokenizer_name			,	**lowerCAmelCase    )
      elif model_args.model_name_or_path:
            UpperCAmelCase__			:							List[str]							=    AutoTokenizer.from_pretrained(model_args.model_name_or_path			,	**lowerCAmelCase    )
      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:
            UpperCAmelCase__			:							Tuple							=    AutoModelForMaskedLM.from_pretrained(
                model_args.model_name_or_path			,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path    )			,	config=lowerCAmelCase			,	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"""    )
            UpperCAmelCase__			:							int							=    AutoModelForMaskedLM.from_config(lowerCAmelCase    )

      model.resize_token_embeddings(len(lowerCAmelCase    )    )

      # Preprocessing the datasets.
      # First we tokenize all the texts.
      if training_args.do_train:
            UpperCAmelCase__			:							Any							=    datasets["""train"""].column_names
      else:
            UpperCAmelCase__			:							List[Any]							=    datasets["""validation"""].column_names
      UpperCAmelCase__			:							Any							=    """text""" if """text""" in column_names else column_names[0]

      UpperCAmelCase__			:							List[str]							=    """max_length""" if data_args.pad_to_max_length else False

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

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

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

      # Initialize our Trainer
      UpperCAmelCase__			:							str							=    Trainer(
          model=lowerCAmelCase			,	args=lowerCAmelCase			,	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=lowerCAmelCase			,	data_collator=lowerCAmelCase			,	)

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

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

            UpperCAmelCase__			:							Any							=    trainer.evaluate()

            UpperCAmelCase__			:							str							=    math.exp(eval_output["""eval_loss"""]    )
            UpperCAmelCase__			:							Tuple							=    perplexity

            UpperCAmelCase__			:							List[Any]							=    os.path.join(training_args.output_dir			,	"""eval_results_mlm_wwm.txt"""    )
            if trainer.is_world_process_zero():
                  with open(lowerCAmelCase			,	"""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__						(						lowerCAmelCase    )	-> Tuple:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
    main()




"""simple docstring"""
_A							=  """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""




def 					a__						(						)	-> None:
      UpperCAmelCase__			:							Optional[Any]							=    input("""Enter message: """    )
      UpperCAmelCase__			:							Optional[Any]							=    input("""Enter key [alphanumeric]: """    )
      UpperCAmelCase__			:							Optional[Any]							=    input("""Encrypt/Decrypt [e/d]: """    )

      if mode.lower().startswith("""e"""    ):
            UpperCAmelCase__			:							Optional[Any]							=    """encrypt"""
            UpperCAmelCase__			:							List[Any]							=    encrypt_message(lowerCAmelCase			,	lowerCAmelCase    )
      elif mode.lower().startswith("""d"""    ):
            UpperCAmelCase__			:							Optional[Any]							=    """decrypt"""
            UpperCAmelCase__			:							Dict							=    decrypt_message(lowerCAmelCase			,	lowerCAmelCase    )

      print(F"""\n{mode.title()}ed message:"""    )
      print(lowerCAmelCase    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      return translate_message(lowerCAmelCase			,	lowerCAmelCase			,	"""encrypt"""    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      return translate_message(lowerCAmelCase			,	lowerCAmelCase			,	"""decrypt"""    )




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> str:
      UpperCAmelCase__			:							Optional[Any]							=    []
      UpperCAmelCase__			:							int							=    0
      UpperCAmelCase__			:							str							=    key.upper()

      for symbol in message:
            UpperCAmelCase__			:							Optional[Any]							=    LETTERS.find(symbol.upper()    )
            if num != -1:
                  if mode == "encrypt":
                        num += LETTERS.find(key[key_index]    )
                  elif mode == "decrypt":
                        num -= LETTERS.find(key[key_index]    )

                  num %= len(lowerCAmelCase    )

                  if symbol.isupper():
                        translated.append(LETTERS[num]    )
                  elif symbol.islower():
                        translated.append(LETTERS[num].lower()    )

                  key_index += 1
                  if key_index == len(lowerCAmelCase    ):
                        UpperCAmelCase__			:							List[str]							=    0
            else:
                  translated.append(lowerCAmelCase    )
      return "".join(lowerCAmelCase    )


if __name__ == "__main__":
    main()

