


def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     return abs(_lowercase ) if a == 0 else greatest_common_divisor(b % a					,  _lowercase )






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     while y:  # --> when y=0 then loop will terminate and return x as final GCD.
          __UpperCamelCase,    __UpperCamelCase =			y, x % y
     return abs(_lowercase )






def 		_A		(		)					-> Optional[int]:


     """simple docstring"""


     try:
          __UpperCamelCase =			input('Enter two integers separated by comma (,): ' ).split(',' )
          __UpperCamelCase =			int(nums[0] )
          __UpperCamelCase =			int(nums[1] )
          print(
              f'''greatest_common_divisor({num_a}, {num_a}) = '''
              f'''{greatest_common_divisor(_lowercase					,  _lowercase )}''' )
          print(f'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(_lowercase					,  _lowercase )}''' )
     except (IndexError, UnboundLocalError, ValueError):
          print('Wrong input' )


if __name__ == "__main__":
    main()





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   ()	-> Union[str, Any]:
	A__				:       int	  =						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_			:					Any    			=     load('accuracy')

def 		UpperCamelCase   (lowercase_:					Dict   )	-> Any:
	A__  ,  A__				:       int	  =						eval_pred
	A__				:       List[str]	  =						np.argmax(lowercase_       , axis=1   )
	return metric.compute(predictions=lowercase_       , references=lowercase_   )



class        _a		(__magic_name__							):


					'''simple docstring'''






					def __init__(					self       ,			A__     ):
						super().__init__()
						A__				:       Any	  =						trainer






					def     __A     (					self       ,			A__       ,			A__       ,			A__       ,			**A__     ):
						if control.should_evaluate:
							A__				:       Dict	  =						deepcopy(A__     )
							self._trainer.evaluate(eval_dataset=self._trainer.train_dataset       ,			metric_key_prefix="""train"""     )
							return control_copy



def 		UpperCamelCase   ()	-> Optional[int]:
	A__				:       Optional[Any]	  =						get_args()
	set_seed(args.seed   )

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

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

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

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

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

	A__				:       int	  =						train_test_validation.map(
	    lowercase_       , batched=lowercase_       , remove_columns=train_test_validation["""train"""].column_names       , )
	A__				:       List[str]	  =						DataCollatorWithPadding(tokenizer=lowercase_   )

	A__				:       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"""       , )

	A__				:       int	  =						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()



