





"""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					(       )       ->List[Any]:



    '''simple docstring'''







    __A   :						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()


a_			=      load("""accuracy""")







def 		__lowercase					(       snake_case_  :  Union[str, Any]						)       ->Any:



    '''simple docstring'''







    __A			,	__A   :						str									=					eval_pred
    __A   :						List[str]									=					np.argmax(snake_case_    ,axis=1						)
    return metric.compute(predictions=snake_case_    ,references=snake_case_						)






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     super().__init__()
     __A   :						Dict									=					trainer







 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase  ):



     '''simple docstring'''




     if control.should_evaluate:
         __A   :						str									=					deepcopy(__lowerCamelCase  )
         self._trainer.evaluate(eval_dataset=self._trainer.train_dataset ,						metric_key_prefix='''train'''  )
         return control_copy







def 		__lowercase					(       )       ->int:



    '''simple docstring'''







    __A   :						List[Any]									=					get_args()
    set_seed(args.seed						)

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

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

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

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

    def tokenize(snake_case_  :  Optional[Any]						):
        __A   :						Union[str, Any]									=					tokenizer(example['''src''']    ,truncation=snake_case_    ,max_length=1024						)
        __A   :						List[Any]									=					labels.straint(example['''complexity''']						)
        return {
            "input_ids": inputs["input_ids"],
            "attention_mask": inputs["attention_mask"],
            "label": label,
        }

    __A   :						Optional[int]									=					train_test_validation.map(
        snake_case_    ,batched=snake_case_    ,remove_columns=train_test_validation['''train'''].column_names    ,)
    __A   :						str									=					DataCollatorWithPadding(tokenizer=snake_case_						)

    __A   :						str									=					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'''    ,)

    __A   :						int									=					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()











"""simple docstring"""



def 		__lowercase					(       )       ->Tuple:



    '''simple docstring'''







    __A   :						str									=					[]
    __A   :						List[Any]									=					1

    while len(snake_case_						) < 1e6:
        constant.append(str(snake_case_						)						)
        i += 1

    __A   :						Any									=					''''''.join(snake_case_						)

    return (
        int(constant[0]						)
        * int(constant[9]						)
        * int(constant[99]						)
        * int(constant[999]						)
        * int(constant[9999]						)
        * int(constant[99999]						)
        * int(constant[999999]						)
    )


if __name__ == "__main__":
       print(solution())




