




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 					__SCREAMING_SNAKE_CASE   (  )       ->	Tuple:

    """simple docstring"""


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


a_   		=     load("accuracy")




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase		,      __UpperCamelCase  		=						eval_pred
    __UpperCamelCase  		=						np.argmax(lowercase_					,     axis=1      )
    return metric.compute(predictions=lowercase_					,     references=lowercase_      )

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: List[Any] ,       snake_case					: str				):
          super().__init__()
          __UpperCamelCase  		=						trainer







      def       snake_case	(     self					: Any ,       snake_case					: str ,       snake_case					: List[Any] ,       snake_case					: Optional[Any] ,       **snake_case					: Any				):
          if control.should_evaluate:
              __UpperCamelCase  		=						deepcopy(snake_case				)
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset ,       metric_key_prefix='''train'''				)
              return control_copy







def 					__SCREAMING_SNAKE_CASE   (  )       ->	Dict:

    """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(lowercase_      ):
        __UpperCamelCase  		=						tokenizer(example['''src''']					,     truncation=lowercase_					,     max_length=10_24      )
        __UpperCamelCase  		=						labels.straint(example['''complexity''']      )
        return {
            "input_ids": inputs["input_ids"],
            "attention_mask": inputs["attention_mask"],
            "label": label,
        }

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

    __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=lowercase_					,     args=lowercase_					,     train_dataset=tokenized_datasets['''train''']					,     eval_dataset=tokenized_datasets['''valid''']					,     tokenizer=lowercase_					,     data_collator=lowercase_					,     compute_metrics=lowercase_					,     )

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


if __name__ == "__main__":
 main()











def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_ = False      )       ->	str:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ):
        __UpperCamelCase  		=						F"Expected string as input, found {type(lowercase_      )}"
        raise ValueError(lowercase_      )
    if not isinstance(lowercase_					,     lowercase_      ):
        __UpperCamelCase  		=						F"Expected boolean as use_pascal parameter, found {type(lowercase_      )}"
        raise ValueError(lowercase_      )

    __UpperCamelCase  		=						input_str.split('''_'''      )

    __UpperCamelCase  		=						0 if use_pascal else 1

    __UpperCamelCase  		=						words[start_index:]

    __UpperCamelCase  		=						[word[0].upper() + word[1:] for word in words_to_capitalize]

    __UpperCamelCase  		=						'''''' if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]      )


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

 testmod()






