
"""simple docstring"""



def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Optional[int]     							=					''
    for i in table:
        res += inp[i - 1]
    return res
def 					lowerCamelCase   (     _snake_case ):
    return data[1:] + data[0]
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      str     							=					''
    for i in range(len(_snake_case ) ):
        if a[i] == b[i]:
            res += "0"
        else:
            res += "1"
    return res
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      str     							=					int('0b' + data[0] + data[-1]    ,2 )
    UpperCAmelCase__	:      Optional[Any]     							=					int('0b' + data[1:3]    ,2 )
    return bin(s[row][col] )[2:]
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      Any     							=					message[:4]
    UpperCAmelCase__	:      Optional[Any]     							=					message[4:]
    UpperCAmelCase__	:      Dict     							=					apply_table(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					xor(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Dict     							=					apply_sbox(_snake_case    ,temp[:4] )  # noqa: E741
    UpperCAmelCase__	:      Tuple     							=					apply_sbox(_snake_case    ,temp[4:] )
    UpperCAmelCase__	:      List[str]     							=					'0' * (2 - len(_snake_case )) + l  # noqa: E741
    UpperCAmelCase__	:      Optional[Any]     							=					'0' * (2 - len(_snake_case )) + r
    UpperCAmelCase__	:      Optional[int]     							=					apply_table(l + r    ,_snake_case )
    UpperCAmelCase__	:      Any     							=					xor(_snake_case    ,_snake_case )
    return temp + right


if __name__ == "__main__":
    UpperCamelCase__          =	input('Enter 10 bit key: ')
    UpperCamelCase__          =	input('Enter 8 bit message: ')

    UpperCamelCase__          =	[6, 3, 7, 4, 8, 5, 10, 9]
    UpperCamelCase__          =	[3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
    UpperCamelCase__          =	[2, 4, 3, 1]
    UpperCamelCase__          =	[2, 6, 3, 1, 4, 8, 5, 7]
    UpperCamelCase__          =	[4, 1, 3, 5, 7, 2, 8, 6]
    UpperCamelCase__          =	[4, 1, 2, 3, 2, 3, 4, 1]
    UpperCamelCase__          =	[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
    UpperCamelCase__          =	[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

    # key generation
    UpperCamelCase__          =	apply_table(key, paa_table)
    UpperCamelCase__          =	temp[:5]
    UpperCamelCase__          =	temp[5:]
    UpperCamelCase__          =	left_shift(left)
    UpperCamelCase__          =	left_shift(right)
    UpperCamelCase__          =	apply_table(left + right, pa_table)
    UpperCamelCase__          =	left_shift(left)
    UpperCamelCase__          =	left_shift(right)
    UpperCamelCase__          =	left_shift(left)
    UpperCamelCase__          =	left_shift(right)
    UpperCamelCase__          =	apply_table(left + right, pa_table)

    # encryption
    UpperCamelCase__          =	apply_table(message, IP)
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	temp[4:] + temp[:4]
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	apply_table(temp, IP_inv)
    print('Cipher text is:', CT)

    # decryption
    UpperCamelCase__          =	apply_table(CT, IP)
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	temp[4:] + temp[:4]
    UpperCamelCase__          =	function(expansion, sa, sa, keya, temp)
    UpperCamelCase__          =	apply_table(temp, IP_inv)
    print('Plain text after decypting is:', PT)



"""simple docstring"""



import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)
def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      List[Any]     							=					argparse.ArgumentParser()
    parser.add_argument('--model_ckpt'    ,type=_snake_case    ,default='microsoft/unixcoder-base-nine' )
    parser.add_argument('--num_epochs'    ,type=_snake_case    ,default=5 )
    parser.add_argument('--batch_size'    ,type=_snake_case    ,default=6 )
    parser.add_argument('--gradient_accumulation_steps'    ,type=_snake_case    ,default=1 )
    parser.add_argument('--freeze'    ,type=_snake_case    ,default=_snake_case )
    parser.add_argument('--learning_rate'    ,type=_snake_case    ,default=5e-4 )
    parser.add_argument('--seed'    ,type=_snake_case    ,default=0 )
    parser.add_argument('--lr_scheduler_type'    ,type=_snake_case    ,default='cosine' )
    parser.add_argument('--num_warmup_steps'    ,type=_snake_case    ,default=10 )
    parser.add_argument('--weight_decay'    ,type=_snake_case    ,default=0.01 )
    parser.add_argument('--output_dir'    ,type=_snake_case    ,default='./results' )
    return parser.parse_args()


UpperCamelCase__          =	load('accuracy')
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					eval_pred
    UpperCAmelCase__	:      Optional[int]     							=					np.argmax(_snake_case    ,axis=1 )
    return metric.compute(predictions=_snake_case    ,references=_snake_case )





class 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_      ):
          super().__init__()
          UpperCAmelCase__	:      List[str]     							=					trainer



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ):
          if control.should_evaluate:
              UpperCAmelCase__	:      int     							=					deepcopy(UpperCamelCase_      )
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,	metric_key_prefix='train'      )
              return control_copy




def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      int     							=					get_args()
    set_seed(args.seed )

    UpperCAmelCase__	:      Optional[int]     							=					load_dataset('codeparrot/codecomplex'    ,split='train' )
    UpperCAmelCase__	:      Tuple     							=					dataset.train_test_split(test_size=0.2 )
    UpperCAmelCase__	:      List[Any]     							=					train_test['test'].train_test_split(test_size=0.5 )
    UpperCAmelCase__	:      Tuple     							=					DatasetDict(
        {
            'train': train_test['train'],
            'test': test_validation['train'],
            'valid': test_validation['test'],
        } )

    print('Loading tokenizer and model' )
    UpperCAmelCase__	:      str     							=					AutoTokenizer.from_pretrained(args.model_ckpt )
    UpperCAmelCase__	:      Optional[Any]     							=					tokenizer.eos_token
    UpperCAmelCase__	:      Optional[Any]     							=					AutoModelForSequenceClassification.from_pretrained(args.model_ckpt    ,num_labels=7 )
    UpperCAmelCase__	:      Dict     							=					model.config.eos_token_id

    if args.freeze:
        for param in model.roberta.parameters():
            UpperCAmelCase__	:      Optional[int]     							=					False

    UpperCAmelCase__	:      Optional[Any]     							=					ClassLabel(num_classes=7    ,names=list(set(train_test_validation['train']['complexity'] ) ) )

    def tokenize(_snake_case ):
        UpperCAmelCase__	:      Dict     							=					tokenizer(example['src']    ,truncation=_snake_case    ,max_length=1024 )
        UpperCAmelCase__	:      Any     							=					labels.straint(example['complexity'] )
        return {
            "input_ids": inputs["input_ids"],
            "attention_mask": inputs["attention_mask"],
            "label": label,
        }

    UpperCAmelCase__	:      List[Any]     							=					train_test_validation.map(
        _snake_case    ,batched=_snake_case    ,remove_columns=train_test_validation['train'].column_names    ,)
    UpperCAmelCase__	:      Tuple     							=					DataCollatorWithPadding(tokenizer=_snake_case )

    UpperCAmelCase__	:      Union[str, Any]     							=					TrainingArguments(
        output_dir=args.output_dir    ,learning_rate=args.learning_rate    ,lr_scheduler_type=args.lr_scheduler_type    ,evaluation_strategy='epoch'    ,save_strategy='epoch'    ,logging_strategy='epoch'    ,per_device_train_batch_size=args.batch_size    ,per_device_eval_batch_size=args.batch_size    ,num_train_epochs=args.num_epochs    ,gradient_accumulation_steps=args.gradient_accumulation_steps    ,weight_decay=0.01    ,metric_for_best_model='accuracy'    ,run_name='complexity-java'    ,report_to='wandb'    ,)

    UpperCAmelCase__	:      Dict     							=					Trainer(
        model=_snake_case    ,args=_snake_case    ,train_dataset=tokenized_datasets['train']    ,eval_dataset=tokenized_datasets['valid']    ,tokenizer=_snake_case    ,data_collator=_snake_case    ,compute_metrics=_snake_case    ,)

    print('Training...' )
    trainer.add_callback(CustomCallback(_snake_case ) )
    trainer.train()


if __name__ == "__main__":
    main()

