





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       __UpperCamelCase					(      )						->   Any:
    '''simple docstring'''





    lowerCAmelCase_       :						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       __UpperCamelCase					(      lowercase__						:							Dict		)						->   Union[str, Any]:
    '''simple docstring'''





    lowerCAmelCase_       :						int							= eval_pred
    lowerCAmelCase_       :						Optional[int]							= np.argmax(_snake_case		,     axis=1		)
    return metric.compute(predictions=_snake_case		,     references=_snake_case		)

class 		__a				(						__UpperCamelCase   ):



       def __init__(  self						: Union[str, Any]       ,      UpperCAmelCase						: List[str]      ):
           super().__init__()
           lowerCAmelCase_       :						List[str]							= trainer



       def 			A				(  self						: Tuple       ,      UpperCAmelCase						: List[Any]       ,      UpperCAmelCase						: Optional[Any]       ,      UpperCAmelCase						: Optional[Any]       ,      **UpperCAmelCase						: Dict      ):
           if control.should_evaluate:
               lowerCAmelCase_       :						int							= deepcopy(UpperCamelCase_      )
               self._trainer.evaluate(eval_dataset=self._trainer.train_dataset       ,      metric_key_prefix="""train"""      )
               return control_copy






def       __UpperCamelCase					(      )						->   Optional[Any]:
    '''simple docstring'''





    lowerCAmelCase_       :						int							= get_args()
    set_seed(args.seed		)

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

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

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

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

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

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

    lowerCAmelCase_       :						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"""		,     )

    lowerCAmelCase_       :						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()




"""simple docstring"""



UpperCamelCase__          =	{
    'meter': 'm',
    'kilometer': 'km',
    'megametre': 'Mm',
    'gigametre': 'Gm',
    'terametre': 'Tm',
    'petametre': 'Pm',
    'exametre': 'Em',
    'zettametre': 'Zm',
    'yottametre': 'Ym',
}
# Exponent of the factor(meter)
UpperCamelCase__          =	{
    'm': 0,
    'km': 3,
    'Mm': 6,
    'Gm': 9,
    'Tm': 12,
    'Pm': 15,
    'Em': 18,
    'Zm': 21,
    'Ym': 24,
}
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      int     							=					from_type.lower().strip('s' )
    UpperCAmelCase__	:      List[Any]     							=					to_type.lower().strip('s' )

    UpperCAmelCase__	:      Optional[int]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )

    if from_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      Optional[int]     							=					(
            F'''Invalid \'from_type\' value: {from_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    if to_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      int     							=					(
            F'''Invalid \'to_type\' value: {to_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    UpperCAmelCase__	:      str     							=					METRIC_CONVERSION[from_sanitized]
    UpperCAmelCase__	:      Optional[int]     							=					METRIC_CONVERSION[to_sanitized]
    UpperCAmelCase__	:      int     							=					1

    if from_exponent > to_exponent:
        UpperCAmelCase__	:      List[str]     							=					from_exponent - to_exponent
    else:
        UpperCAmelCase__	:      int     							=					-(to_exponent - from_exponent)

    return value * pow(10    ,_snake_case )


if __name__ == "__main__":
    from doctest import testmod

    testmod()

