"""simple docstring"""




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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_a				:		Union[str, Any]	   =       logging.getLogger(__name__)





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Optional[Any]     )	->				List[str]:
		return (preds == labels).mean()
@dataclass
class 						__A							:
					_UpperCamelCase							:      str		 =		field(
					    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}		)
					_UpperCamelCase							:      Optional[str]		 =		field(
					    default=lowerCamelCase_   ,     metadata={"help": "Pretrained config name or path if not the same as model_name"}		)
					_UpperCamelCase							:      Optional[str]		 =		field(
					    default=lowerCamelCase_   ,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}		)
					_UpperCamelCase							:      Optional[str]		 =		field(
					    default=lowerCamelCase_   ,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,     )







@dataclass
class 						__A							:
					_UpperCamelCase							:      str		 =		field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()		)}		)
					_UpperCamelCase							:      str		 =		field(metadata={"help": "Should contain the data files for the task."}		)
					_UpperCamelCase							:      int		 =		field(
					    default=128   ,     metadata={
					        "help": (
					            "The maximum total input sequence length after tokenization. Sequences longer "
					            "than this will be truncated, sequences shorter will be padded."
					        )
					    }   ,     )
					_UpperCamelCase							:      bool		 =		field(
					    default=lowerCamelCase_   ,     metadata={"help": "Overwrite the cached training and evaluation sets"}		)







def 							SCREAMING_SNAKE_CASE					(      )	->				Optional[Any]:
		_lowerCAmelCase						:	Any        =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
		_lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Dict        =					parser.parse_args_into_dataclasses()

		if (
		    os.path.exists(training_args.output_dir     )
		    and os.listdir(training_args.output_dir     )
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
				    """ --overwrite_output_dir to overcome."""     )

		# 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.local_rank != -1     )      ,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"""      ,SCREAMING_SNAKE_CASE__     )

		# Set seed
		set_seed(training_args.seed     )

		try:
				_lowerCAmelCase						:	List[Any]        =					processors[data_args.task_name]()
				_lowerCAmelCase						:	Tuple        =					processor.get_labels()
				_lowerCAmelCase						:	str        =					len(SCREAMING_SNAKE_CASE__     )
		except KeyError:
				raise ValueError("""Task not found: %s""" % (data_args.task_name)     )

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

		_lowerCAmelCase						:	str        =					AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,num_labels=SCREAMING_SNAKE_CASE__      ,finetuning_task=data_args.task_name      ,cache_dir=model_args.cache_dir      ,)
		_lowerCAmelCase						:	Any        =					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      ,)
		_lowerCAmelCase						:	Dict        =					AutoModelForMultipleChoice.from_pretrained(
		    model_args.model_name_or_path      ,from_tf=bool(""".ckpt""" in model_args.model_name_or_path     )      ,config=SCREAMING_SNAKE_CASE__      ,cache_dir=model_args.cache_dir      ,)

		# Get datasets
		_lowerCAmelCase						:	List[str]        =					(
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir      ,tokenizer=SCREAMING_SNAKE_CASE__      ,task=data_args.task_name      ,max_seq_length=data_args.max_seq_length      ,overwrite_cache=data_args.overwrite_cache      ,mode=Split.train      ,)
		    if training_args.do_train
		    else None
		)
		_lowerCAmelCase						:	Optional[Any]        =					(
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir      ,tokenizer=SCREAMING_SNAKE_CASE__      ,task=data_args.task_name      ,max_seq_length=data_args.max_seq_length      ,overwrite_cache=data_args.overwrite_cache      ,mode=Split.dev      ,)
		    if training_args.do_eval
		    else None
		)

		def compute_metrics(_lowerCamelCase						:	EvalPrediction     ) -> Dict:
				_lowerCAmelCase						:	str        =					np.argmax(p.predictions      ,axis=1     )
				return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__      ,p.label_ids     )}

		# Data collator
		_lowerCAmelCase						:	List[str]        =					DataCollatorWithPadding(SCREAMING_SNAKE_CASE__      ,pad_to_multiple_of=8     ) if training_args.fpaa else None

		# Initialize our Trainer
		_lowerCAmelCase						:	Tuple        =					Trainer(
		    model=SCREAMING_SNAKE_CASE__      ,args=SCREAMING_SNAKE_CASE__      ,train_dataset=SCREAMING_SNAKE_CASE__      ,eval_dataset=SCREAMING_SNAKE_CASE__      ,compute_metrics=SCREAMING_SNAKE_CASE__      ,data_collator=SCREAMING_SNAKE_CASE__      ,)

		# Training
		if training_args.do_train:
				trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path     ) else None     )
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_master():
						tokenizer.save_pretrained(training_args.output_dir     )

						 # Evaluation
		_lowerCAmelCase						:	Optional[int]        =					{}
		if training_args.do_eval:
				logger.info("""*** Evaluate ***"""     )

				_lowerCAmelCase						:	Dict        =					trainer.evaluate()

				_lowerCAmelCase						:	Optional[int]        =					os.path.join(training_args.output_dir      ,"""eval_results.txt"""     )
				if trainer.is_world_master():
						with open(SCREAMING_SNAKE_CASE__      ,"""w"""     ) as writer:
								logger.info("""***** Eval results *****"""     )
								for key, value in result.items():
										logger.info("""  %s = %s"""      ,SCREAMING_SNAKE_CASE__      ,SCREAMING_SNAKE_CASE__     )
										writer.write("""%s = %s\n""" % (key, value)     )

								results.update(SCREAMING_SNAKE_CASE__     )

		return results





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Tuple     )	->				Optional[int]:
		main()


if __name__ == "__main__":
					main()





"""simple docstring"""




import math





def 							SCREAMING_SNAKE_CASE					(      )	->				None:
  _lowerCAmelCase						:	Any        =					input("""Enter message: """     )
  _lowerCAmelCase						:	List[Any]        =					int(input(f"Enter key [2-{len(_lowerCamelCase     ) - 1}]: "     )     )
  _lowerCAmelCase						:	Optional[Any]        =					input("""Encryption/Decryption [e/d]: """     )

  if mode.lower().startswith("""e"""     ):
    _lowerCAmelCase						:	Tuple        =					encrypt_message(_lowerCamelCase      ,_lowerCamelCase     )
  elif mode.lower().startswith("""d"""     ):
    _lowerCAmelCase						:	Dict        =					decrypt_message(_lowerCamelCase      ,_lowerCamelCase     )

  # Append pipe symbol (vertical bar) to identify spaces at the end.
  print(f"Output:\n{text + '|'}"     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	Dict        =					[""""""] * key
  for col in range(_lowerCamelCase     ):
    _lowerCAmelCase						:	List[str]        =					col
    while pointer < len(_lowerCamelCase     ):
      cipher_text[col] += message[pointer]
      pointer += key
  return "".join(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	str        =					math.ceil(len(_lowerCamelCase     ) / key     )
  _lowerCAmelCase						:	Union[str, Any]        =					key
  _lowerCAmelCase						:	Any        =					(num_cols * num_rows) - len(_lowerCamelCase     )
  _lowerCAmelCase						:	Dict        =					[""""""] * num_cols
  _lowerCAmelCase						:	Optional[Any]        =					0
  _lowerCAmelCase						:	Dict        =					0

  for symbol in message:
    plain_text[col] += symbol
    col += 1

    if (
        (col == num_cols)
        or (col == num_cols - 1)
        and (row >= num_rows - num_shaded_boxes)
    ):
      _lowerCAmelCase						:	str        =					0
      row += 1

  return "".join(_lowerCamelCase     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




