
"""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      lowercase__    (       )			->   Any:






    """simple docstring"""




    UpperCAmelCase          =       argparse.ArgumentParser()
    parser.add_argument('--model_ckpt'			,					type=lowerCAmelCase			,					default='microsoft/unixcoder-base-nine'			)
    parser.add_argument('--num_epochs'			,					type=lowerCAmelCase			,					default=5			)
    parser.add_argument('--batch_size'			,					type=lowerCAmelCase			,					default=6			)
    parser.add_argument('--gradient_accumulation_steps'			,					type=lowerCAmelCase			,					default=1			)
    parser.add_argument('--freeze'			,					type=lowerCAmelCase			,					default=lowerCAmelCase			)
    parser.add_argument('--learning_rate'			,					type=lowerCAmelCase			,					default=5E-4			)
    parser.add_argument('--seed'			,					type=lowerCAmelCase			,					default=0			)
    parser.add_argument('--lr_scheduler_type'			,					type=lowerCAmelCase			,					default='cosine'			)
    parser.add_argument('--num_warmup_steps'			,					type=lowerCAmelCase			,					default=10			)
    parser.add_argument('--weight_decay'			,					type=lowerCAmelCase			,					default=0.01			)
    parser.add_argument('--output_dir'			,					type=lowerCAmelCase			,					default='./results'			)
    return parser.parse_args()


SCREAMING_SNAKE_CASE_            =    load('''accuracy''')




def      lowercase__    (       lowerCAmelCase					:       Optional[Any]			)			->   int:






    """simple docstring"""




    UpperCAmelCase      ,				UpperCAmelCase          =       eval_pred
    UpperCAmelCase          =       np.argmax(lowerCAmelCase			,					axis=1			)
    return metric.compute(predictions=lowerCAmelCase			,					references=lowerCAmelCase			)
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):




      def __init__(				self ,							lowercase_				)    ->       None:
          super().__init__()
          UpperCAmelCase          =       trainer




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							**lowercase_				)    ->       List[Any]:
          if control.should_evaluate:
              UpperCAmelCase          =       deepcopy(lowercase_				)
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset ,							metric_key_prefix='train'				)
              return control_copy




def      lowercase__    (       )			->   List[Any]:






    """simple docstring"""




    UpperCAmelCase          =       get_args()
    set_seed(args.seed			)

    UpperCAmelCase          =       load_dataset('codeparrot/codecomplex'			,					split='train'			)
    UpperCAmelCase          =       dataset.train_test_split(test_size=0.2			)
    UpperCAmelCase          =       train_test['test'].train_test_split(test_size=0.5			)
    UpperCAmelCase          =       DatasetDict(
        {
            'train': train_test['train'],
            'test': test_validation['train'],
            'valid': test_validation['test'],
        }			)

    print('Loading tokenizer and model'			)
    UpperCAmelCase          =       AutoTokenizer.from_pretrained(args.model_ckpt			)
    UpperCAmelCase          =       tokenizer.eos_token
    UpperCAmelCase          =       AutoModelForSequenceClassification.from_pretrained(args.model_ckpt			,					num_labels=7			)
    UpperCAmelCase          =       model.config.eos_token_id

    if args.freeze:
        for param in model.roberta.parameters():
            UpperCAmelCase          =       False

    UpperCAmelCase          =       ClassLabel(num_classes=7			,					names=list(set(train_test_validation['train']['complexity']			)			)			)

    def tokenize(lowerCAmelCase					:       Union[str, Any]			):
        UpperCAmelCase          =       tokenizer(example['src']			,					truncation=lowerCAmelCase			,					max_length=1_024			)
        UpperCAmelCase          =       labels.straint(example['complexity']			)
        return {
            "input_ids": inputs["input_ids"],
            "attention_mask": inputs["attention_mask"],
            "label": label,
        }

    UpperCAmelCase          =       train_test_validation.map(
        lowerCAmelCase			,					batched=lowerCAmelCase			,					remove_columns=train_test_validation['train'].column_names			,					)
    UpperCAmelCase          =       DataCollatorWithPadding(tokenizer=lowerCAmelCase			)

    UpperCAmelCase          =       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          =       Trainer(
        model=lowerCAmelCase			,					args=lowerCAmelCase			,					train_dataset=tokenized_datasets['train']			,					eval_dataset=tokenized_datasets['valid']			,					tokenizer=lowerCAmelCase			,					data_collator=lowerCAmelCase			,					compute_metrics=lowerCAmelCase			,					)

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


if __name__ == "__main__":
   main()





"""simple docstring"""

def      lowercase__    (       lowerCAmelCase					:       str			)			->   bool:






    """simple docstring"""




    UpperCAmelCase          =       [int(lowerCAmelCase			) for i in ip_va_address.split('.'			) if i.isdigit()]
    return len(lowerCAmelCase			) == 4 and all(0 <= int(lowerCAmelCase			) <= 254 for octet in octets			)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_            =    input().strip()
   SCREAMING_SNAKE_CASE_            =    '''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
   print(F'{ip} is a {valid_or_invalid} IP v4 address.')



