

'''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   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = 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					:			int       =	load('accuracy')







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = eval_pred
     __SCREAMING_SNAKE_CASE      = np.argmax(__snake_case ,       axis=1		)
     return metric.compute(predictions=__snake_case ,       references=__snake_case		)







class 		lowerCAmelCase__  ( __lowercase   ):



 """simple docstring"""



 def __init__(						self   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Any     )    ->					Optional[int]:


      """simple docstring"""




      super().__init__()
      __SCREAMING_SNAKE_CASE      = trainer


 def  UpperCAmelCase__						(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				**__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					Dict:


      """simple docstring"""




      if control.should_evaluate:
           __SCREAMING_SNAKE_CASE      = deepcopy(_A     )
           self._trainer.evaluate(eval_dataset=self._trainer.train_dataset   ,				metric_key_prefix="""train"""     )
           return control_copy







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = get_args()
     set_seed(args.seed		)

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

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

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

     __SCREAMING_SNAKE_CASE      = ClassLabel(num_classes=7 ,       names=list(set(train_test_validation["""train"""]["""complexity"""]		)		)		)

     def tokenize(a__		):
          __SCREAMING_SNAKE_CASE      = tokenizer(example["""src"""] ,       truncation=__snake_case ,       max_length=10_24		)
          __SCREAMING_SNAKE_CASE      = labels.straint(example["""complexity"""]		)
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

     __SCREAMING_SNAKE_CASE      = train_test_validation.map(
         __snake_case ,       batched=__snake_case ,       remove_columns=train_test_validation["""train"""].column_names ,       )
     __SCREAMING_SNAKE_CASE      = DataCollatorWithPadding(tokenizer=__snake_case		)

     __SCREAMING_SNAKE_CASE      = 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""" ,       )

     __SCREAMING_SNAKE_CASE      = 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()







def 					_snake_case	(  __snake_case = 100						):
					_UpperCamelCase			     =     (n * (n + 1) // 2) ** 2
					_UpperCamelCase			     =     n * (n + 1) * (2 * n + 1) // 6
					return sum_cubes - sum_squares


if __name__ == "__main__":
					print(f'{solution() = }')
