





'''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


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


@dataclass
class 					a_     :

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


    def 			snake_case_(		self       )		->   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 					a_     :

    '''simple docstring'''
    UpperCamelCase     = field(
        default=snake_case_      ,    metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}       )
    UpperCamelCase     = field(
        default=snake_case_      ,    metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}       )
    UpperCamelCase     = field(default=snake_case_      ,    metadata={'''help''': '''The input training data file (a text file).'''}       )
    UpperCamelCase     = field(
        default=snake_case_      ,    metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}      ,    )
    UpperCamelCase     = field(
        default=snake_case_      ,    metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}      ,    )
    UpperCamelCase     = field(
        default=snake_case_      ,    metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}      ,    )
    UpperCamelCase     = field(
        default=snake_case_      ,    metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )
    UpperCamelCase     = field(
        default=5      ,    metadata={
            '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
        }      ,    )
    UpperCamelCase     = field(
        default=snake_case_      ,    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.'''
            )
        }      ,    )
    UpperCamelCase     = field(
        default=snake_case_      ,    metadata={'''help''': '''The number of processes to use for the preprocessing.'''}      ,    )
    UpperCamelCase     = field(
        default=0.15      ,    metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}       )
    UpperCamelCase     = field(
        default=snake_case_      ,    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:
            _SCREAMING_SNAKE_CASE									=			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									=			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						(     __lowerCamelCase :    Dict					,       __lowerCamelCase :    Optional[Any]    )						->Tuple:
    with open(__lowerCamelCase					,       """r"""					,       encoding="""utf-8"""    ) as f:
        _SCREAMING_SNAKE_CASE									=			[json.loads(__lowerCamelCase    ) for line in f.read().splitlines() if (len(__lowerCamelCase    ) > 0 and not line.isspace())]
    assert len(__lowerCamelCase    ) == len(__lowerCamelCase    )

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





def lowerCamelCase						(     )						->Union[str, 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									=			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									=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
    else:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			parser.parse_args_into_dataclasses()

    # Detecting last checkpoint.
    _SCREAMING_SNAKE_CASE									=			None
    if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
        _SCREAMING_SNAKE_CASE									=			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.
        _SCREAMING_SNAKE_CASE									=			load_dataset(data_args.dataset_name					,       data_args.dataset_config_name    )
        if "validation" not in datasets.keys():
            _SCREAMING_SNAKE_CASE									=			load_dataset(
                data_args.dataset_name					,       data_args.dataset_config_name					,       split=F'train[:{data_args.validation_split_percentage}%]'					,       )
            _SCREAMING_SNAKE_CASE									=			load_dataset(
                data_args.dataset_name					,       data_args.dataset_config_name					,       split=F'train[{data_args.validation_split_percentage}%:]'					,       )
    else:
        _SCREAMING_SNAKE_CASE									=			{}
        if data_args.train_file is not None:
            _SCREAMING_SNAKE_CASE									=			data_args.train_file
        if data_args.validation_file is not None:
            _SCREAMING_SNAKE_CASE									=			data_args.validation_file
        _SCREAMING_SNAKE_CASE									=			data_args.train_file.split("""."""    )[-1]
        if extension == "txt":
            _SCREAMING_SNAKE_CASE									=			"""text"""
        _SCREAMING_SNAKE_CASE									=			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.
    _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,
    }
    if model_args.config_name:
        _SCREAMING_SNAKE_CASE									=			AutoConfig.from_pretrained(model_args.config_name					,       **__lowerCamelCase    )
    elif model_args.model_name_or_path:
        _SCREAMING_SNAKE_CASE									=			AutoConfig.from_pretrained(model_args.model_name_or_path					,       **__lowerCamelCase    )
    else:
        _SCREAMING_SNAKE_CASE									=			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									=			{
        """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									=			AutoTokenizer.from_pretrained(model_args.tokenizer_name					,       **__lowerCamelCase    )
    elif model_args.model_name_or_path:
        _SCREAMING_SNAKE_CASE									=			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:
        _SCREAMING_SNAKE_CASE									=			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"""    )
        _SCREAMING_SNAKE_CASE									=			AutoModelForMaskedLM.from_config(__lowerCamelCase    )

    model.resize_token_embeddings(len(__lowerCamelCase    )    )

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

    _SCREAMING_SNAKE_CASE									=			"""max_length""" if data_args.pad_to_max_length else False

    def tokenize_function(__lowerCamelCase :    List[str]    ):
        # Remove empty lines
        _SCREAMING_SNAKE_CASE									=			[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    )

    _SCREAMING_SNAKE_CASE									=			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:
        _SCREAMING_SNAKE_CASE									=			add_chinese_references(tokenized_datasets["""train"""]					,       data_args.train_ref_file    )
    if data_args.validation_ref_file is not None:
        _SCREAMING_SNAKE_CASE									=			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									=			data_args.train_ref_file or data_args.validation_ref_file
    if has_ref:
        _SCREAMING_SNAKE_CASE									=			False

    # Data collator
    # This one will take care of randomly masking the tokens.
    _SCREAMING_SNAKE_CASE									=			DataCollatorForWholeWordMask(tokenizer=__lowerCamelCase					,       mlm_probability=data_args.mlm_probability    )

    # Initialize our Trainer
    _SCREAMING_SNAKE_CASE									=			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:
            _SCREAMING_SNAKE_CASE									=			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									=			model_args.model_name_or_path
        else:
            _SCREAMING_SNAKE_CASE									=			None
        _SCREAMING_SNAKE_CASE									=			trainer.train(resume_from_checkpoint=__lowerCamelCase    )
        trainer.save_model()  # Saves the tokenizer too for easy upload

        _SCREAMING_SNAKE_CASE									=			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
    _SCREAMING_SNAKE_CASE									=			{}
    if training_args.do_eval:
        logger.info("""*** Evaluate ***"""    )

        _SCREAMING_SNAKE_CASE									=			trainer.evaluate()

        _SCREAMING_SNAKE_CASE									=			math.exp(eval_output["""eval_loss"""]    )
        _SCREAMING_SNAKE_CASE									=			perplexity

        _SCREAMING_SNAKE_CASE									=			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 lowerCamelCase						(     __lowerCamelCase :    List[str]    )						->Union[str, Any]:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
      main()






import string

def 						UpperCamelCase					(lowercase_: str					)     ->      None:
	for key in range(len(string.ascii_uppercase					)					):
		A__							:    Dict        =		""""""
		for symbol in message:
			if symbol in string.ascii_uppercase:
				A__							:    Dict        =		string.ascii_uppercase.find(lowercase_					)
				A__							:    Optional[int]        =		num - key
				if num < 0:
					A__							:    Optional[int]        =		num + len(string.ascii_uppercase					)
				A__							:    Any        =		translated + string.ascii_uppercase[num]
			else:
				A__							:    Optional[Any]        =		translated + symbol
		print(f"""Decryption using Key #{key}: {translated}"""					)

def 						UpperCamelCase					()     ->      None:
	A__							:    Optional[Any]        =		input("""Encrypted message: """					)
	A__							:    Optional[Any]        =		message.upper()
	decrypt(lowercase_					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()



