

import random



class   lowercase_   :



      @staticmethod
      def 						UpperCamelCase_		(      A__				:    str		)   ->					Optional[Any]:
             _snake_case						=							[ord(_UpperCamelCase		) for i in text]
             _snake_case						=							[]
             _snake_case						=							[]
             for i in plain:
                    _snake_case						=							random.randint(1      , 300		)
                    _snake_case						=							(i + k) * k
                    cipher.append(_UpperCamelCase		)
                    key.append(_UpperCamelCase		)
             return cipher, key







      @staticmethod
      def 						UpperCamelCase_		(      A__				:    list[int]      , A__				:    list[int]		)   ->					Optional[int]:
             _snake_case						=							[]
             for i in range(len(_UpperCamelCase		)		):
                    _snake_case						=							int((cipher[i] - (key[i]) ** 2) / key[i]		)
                    plain.append(chr(_UpperCamelCase		)		)
             return "".join(_UpperCamelCase		)


if __name__ == "__main__":
 __A      		=     Onepad().encrypt('''Hello''')
 print(c, k)
 print(Onepad().decrypt(c, k))




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__A      		=     logging.getLogger(__name__)



@dataclass
class   lowercase_   :
      UpperCamelCase_      :		str										=    field(
          metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}					)
      UpperCamelCase_      :		Optional[str]										=    field(
          default=__lowercase       ,							metadata={"help": "Pretrained config name or path if not the same as model_name"}					)
      UpperCamelCase_      :		Optional[str]										=    field(
          default=__lowercase       ,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}					)
      UpperCamelCase_      :		Optional[str]										=    field(
          default=__lowercase       ,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,							)
      UpperCamelCase_      :		bool										=    field(default=__lowercase       ,							metadata={"help": "Whether tp freeze the encoder."}					)
      UpperCamelCase_      :		bool										=    field(default=__lowercase       ,							metadata={"help": "Whether  to freeze the embeddings."}					)



@dataclass
class   lowercase_   :
      UpperCamelCase_      :		str										=    field(
          metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}					)
      UpperCamelCase_      :		Optional[str]										=    field(
          default="summarization"       ,							metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}       ,							)
      UpperCamelCase_      :		Optional[int]										=    field(
          default=1_0_2_4       ,							metadata={
              "help": (
                  "The maximum total input sequence length after tokenization. Sequences longer "
                  "than this will be truncated, sequences shorter will be padded."
              )
          }       ,							)
      UpperCamelCase_      :		Optional[int]										=    field(
          default=1_2_8       ,							metadata={
              "help": (
                  "The maximum total sequence length for target text after tokenization. Sequences longer "
                  "than this will be truncated, sequences shorter will be padded."
              )
          }       ,							)
      UpperCamelCase_      :		Optional[int]										=    field(
          default=1_4_2       ,							metadata={
              "help": (
                  "The maximum total sequence length for validation target text after tokenization. Sequences longer "
                  "than this will be truncated, sequences shorter will be padded. "
                  "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
                  "during ``evaluate`` and ``predict``."
              )
          }       ,							)
      UpperCamelCase_      :		Optional[int]										=    field(
          default=1_4_2       ,							metadata={
              "help": (
                  "The maximum total sequence length for test target text after tokenization. Sequences longer "
                  "than this will be truncated, sequences shorter will be padded."
              )
          }       ,							)
      UpperCamelCase_      :		Optional[int]										=    field(default=-1       ,							metadata={"help": "# training examples. -1 means use all."}					)
      UpperCamelCase_      :		Optional[int]										=    field(default=-1       ,							metadata={"help": "# validation examples. -1 means use all."}					)
      UpperCamelCase_      :		Optional[int]										=    field(default=-1       ,							metadata={"help": "# test examples. -1 means use all."}					)
      UpperCamelCase_      :		Optional[str]										=    field(default=__lowercase       ,							metadata={"help": "Source language id for translation."}					)
      UpperCamelCase_      :		Optional[str]										=    field(default=__lowercase       ,							metadata={"help": "Target language id for translation."}					)
      UpperCamelCase_      :		Optional[int]										=    field(default=__lowercase       ,							metadata={"help": "# num_beams to use for evaluation."}					)
      UpperCamelCase_      :		bool										=    field(
          default=__lowercase       ,							metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}       ,							)



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     List[Any]:



  """simple docstring"""






  logger.info(F"""***** {split} metrics *****"""					)
  for key in sorted(metrics.keys()					):
    logger.info(F"""  {key} = {metrics[key]}"""					)
  save_json(_UpperCamelCase			,	os.path.join(_UpperCamelCase			,	F"""{split}_results.json"""					)					)



def 				snake_case_()							->     List[Any]:



  """simple docstring"""






  _snake_case						=							HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)					)

  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.
    _snake_case,					_snake_case,					_snake_case						=							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
  else:
    _snake_case,					_snake_case,					_snake_case						=							parser.parse_args_into_dataclasses()

  check_output_dir(_UpperCamelCase					)

  # Setup logging
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''			,	datefmt='''%m/%d/%Y %H:%M:%S'''			,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN			,	)
  logger.warning(
      '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''			,	training_args.local_rank			,	training_args.device			,	training_args.n_gpu			,	bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED					)			,	training_args.fpaa			,	)
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
  # 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()
  logger.info('''Training/evaluation parameters %s'''			,	_UpperCamelCase					)

  # Set seed
  set_seed(training_args.seed					)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  _snake_case						=							AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path			,	cache_dir=model_args.cache_dir			,	)

  _snake_case						=							('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
  for p in extra_model_params:
    if getattr(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					):
      assert hasattr(_UpperCamelCase			,	_UpperCamelCase					), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
      setattr(_UpperCamelCase			,	_UpperCamelCase			,	getattr(_UpperCamelCase			,	_UpperCamelCase					)					)

  _snake_case						=							AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,	cache_dir=model_args.cache_dir			,	)
  _snake_case						=							AutoModelForSeqaSeqLM.from_pretrained(
      model_args.model_name_or_path			,	from_tf='''.ckpt''' in model_args.model_name_or_path			,	config=_UpperCamelCase			,	cache_dir=model_args.cache_dir			,	)

  # use task specific params
  use_task_specific_params(_UpperCamelCase			,	data_args.task					)

  # set num_beams for evaluation
  if data_args.eval_beams is None:
    _snake_case						=							model.config.num_beams

  # set decoder_start_token_id for MBart
  if model.config.decoder_start_token_id is None and isinstance(_UpperCamelCase			,	(MBartTokenizer, MBartTokenizerFast)					):
    assert (
        data_args.tgt_lang is not None and data_args.src_lang is not None
    ), "mBart requires --tgt_lang and --src_lang"
    if isinstance(_UpperCamelCase			,	_UpperCamelCase					):
      _snake_case						=							tokenizer.lang_code_to_id[data_args.tgt_lang]
    else:
      _snake_case						=							tokenizer.convert_tokens_to_ids(data_args.tgt_lang					)

  if model_args.freeze_embeds:
    freeze_embeds(_UpperCamelCase					)
  if model_args.freeze_encoder:
    freeze_params(model.get_encoder()					)
    assert_all_frozen(model.get_encoder()					)

  _snake_case						=							SeqaSeqDataset

  # Get datasets
  _snake_case						=							(
      dataset_class(
          _UpperCamelCase			,	type_path='''train'''			,	data_dir=data_args.data_dir			,	n_obs=data_args.n_train			,	max_target_length=data_args.max_target_length			,	max_source_length=data_args.max_source_length			,	prefix=model.config.prefix or ''''''			,	)
      if training_args.do_train
      else None
  )
  _snake_case						=							(
      dataset_class(
          _UpperCamelCase			,	type_path='''val'''			,	data_dir=data_args.data_dir			,	n_obs=data_args.n_val			,	max_target_length=data_args.val_max_target_length			,	max_source_length=data_args.max_source_length			,	prefix=model.config.prefix or ''''''			,	)
      if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
      else None
  )
  _snake_case						=							(
      dataset_class(
          _UpperCamelCase			,	type_path='''test'''			,	data_dir=data_args.data_dir			,	n_obs=data_args.n_test			,	max_target_length=data_args.test_max_target_length			,	max_source_length=data_args.max_source_length			,	prefix=model.config.prefix or ''''''			,	)
      if training_args.do_predict
      else None
  )

  # Initialize our Trainer
  _snake_case						=							(
      build_compute_metrics_fn(data_args.task			,	_UpperCamelCase					) if training_args.predict_with_generate else None
  )
  _snake_case						=							SeqaSeqTrainer(
      model=_UpperCamelCase			,	args=_UpperCamelCase			,	data_args=_UpperCamelCase			,	train_dataset=_UpperCamelCase			,	eval_dataset=_UpperCamelCase			,	data_collator=SeqaSeqDataCollator(
          _UpperCamelCase			,	_UpperCamelCase			,	model.config.decoder_start_token_id			,	training_args.tpu_num_cores					)			,	compute_metrics=_UpperCamelCase			,	tokenizer=_UpperCamelCase			,	)

  _snake_case						=							{}
  # Training
  if training_args.do_train:
    logger.info('''*** Train ***'''					)

    _snake_case						=							trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
    _snake_case						=							train_result.metrics
    _snake_case						=							data_args.n_train

    trainer.save_model()  # this also saves the tokenizer

    if trainer.is_world_process_zero():
      handle_metrics('''train'''			,	_UpperCamelCase			,	training_args.output_dir					)
      all_metrics.update(_UpperCamelCase					)

      # 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'''					)					)

      # For convenience, we also re-save the tokenizer to the same directory,
      # so that you can share your model easily on huggingface.co/models =)
      tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
  if training_args.do_eval:
    logger.info('''*** Evaluate ***'''					)

    _snake_case						=							trainer.evaluate(metric_key_prefix='''val'''					)
    _snake_case						=							data_args.n_val
    _snake_case						=							round(metrics['''val_loss''']			,	4					)

    if trainer.is_world_process_zero():
      handle_metrics('''val'''			,	_UpperCamelCase			,	training_args.output_dir					)
      all_metrics.update(_UpperCamelCase					)

  if training_args.do_predict:
    logger.info('''*** Predict ***'''					)

    _snake_case						=							trainer.predict(test_dataset=_UpperCamelCase			,	metric_key_prefix='''test'''					)
    _snake_case						=							test_output.metrics
    _snake_case						=							data_args.n_test

    if trainer.is_world_process_zero():
      _snake_case						=							round(metrics['''test_loss''']			,	4					)
      handle_metrics('''test'''			,	_UpperCamelCase			,	training_args.output_dir					)
      all_metrics.update(_UpperCamelCase					)

      if training_args.predict_with_generate:
        _snake_case						=							tokenizer.batch_decode(
            test_output.predictions			,	skip_special_tokens=_UpperCamelCase			,	clean_up_tokenization_spaces=_UpperCamelCase					)
        _snake_case						=							lmap(str.strip			,	_UpperCamelCase					)
        write_txt_file(_UpperCamelCase			,	os.path.join(training_args.output_dir			,	'''test_generations.txt'''					)					)

  if trainer.is_world_process_zero():
    save_json(_UpperCamelCase			,	os.path.join(training_args.output_dir			,	'''all_results.json'''					)					)

  return all_metrics



def 				snake_case_(_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  main()


if __name__ == "__main__":
 main()

