






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__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Tuple            =						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      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


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



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase   )	->							None:
							super().__init__()
							UpperCAmelCase_    :      Union[str, Any]            =						trainer



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		**_UpperCamelCase   )	->							str:
							if control.should_evaluate:
												UpperCAmelCase_    :      Any            =						deepcopy(_UpperCamelCase   )
												self._trainer.evaluate(eval_dataset=self._trainer.train_dataset				,		metric_key_prefix='train'   )
												return control_copy





def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Tuple            =						get_args()
					set_seed(args.seed  )

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

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

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

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

					def tokenize(__snake_case    :							str  ):
										UpperCAmelCase_    :      str            =						tokenizer(example['src']     ,       truncation=__snake_case     ,       max_length=1_024  )
										UpperCAmelCase_    :      Dict            =						labels.straint(example['complexity']  )
										return {
										    "input_ids": inputs["input_ids"],
										    "attention_mask": inputs["attention_mask"],
										    "label": label,
										}

					UpperCAmelCase_    :      List[Any]            =						train_test_validation.map(
					    __snake_case     ,       batched=__snake_case     ,       remove_columns=train_test_validation['train'].column_names     ,       )
					UpperCAmelCase_    :      Tuple            =						DataCollatorWithPadding(tokenizer=__snake_case  )

					UpperCAmelCase_    :      Optional[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'     ,       )

					UpperCAmelCase_    :      str            =						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      lowercase__      (     __snake_case    :							int     ,       __snake_case    :							int  ):


					'''simple docstring'''


					if a < 0 or b < 0:
										raise ValueError('the value of both inputs must be positive'  )

					UpperCAmelCase_    :      Tuple            =						str(bin(__snake_case  )  )[2:]  # remove the leading "0b"
					UpperCAmelCase_    :      Union[str, Any]            =						str(bin(__snake_case  )  )[2:]  # remove the leading "0b"

					UpperCAmelCase_    :      List[Any]            =						max(len(__snake_case  )     ,       len(__snake_case  )  )

					return "0b" + "".join(
					    str(int(char_a == '1' and char_b == '1'  )  )
					    for char_a, char_b in zip(a_binary.zfill(__snake_case  )     ,       b_binary.zfill(__snake_case  )  )  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






