'''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 __lowerCAmelCase			(							)     ->   Optional[int]:
				__lowerCamelCase          =  argparse.ArgumentParser()
				parser.add_argument('''--model_ckpt'''    ,  type=UpperCamelCase__    ,  default='''microsoft/unixcoder-base-nine'''    )
				parser.add_argument('''--num_epochs'''    ,  type=UpperCamelCase__    ,  default=5    )
				parser.add_argument('''--batch_size'''    ,  type=UpperCamelCase__    ,  default=6    )
				parser.add_argument('''--gradient_accumulation_steps'''    ,  type=UpperCamelCase__    ,  default=1    )
				parser.add_argument('''--freeze'''    ,  type=UpperCamelCase__    ,  default=UpperCamelCase__    )
				parser.add_argument('''--learning_rate'''    ,  type=UpperCamelCase__    ,  default=5E-4    )
				parser.add_argument('''--seed'''    ,  type=UpperCamelCase__    ,  default=0    )
				parser.add_argument('''--lr_scheduler_type'''    ,  type=UpperCamelCase__    ,  default='''cosine'''    )
				parser.add_argument('''--num_warmup_steps'''    ,  type=UpperCamelCase__    ,  default=10    )
				parser.add_argument('''--weight_decay'''    ,  type=UpperCamelCase__    ,  default=0.0_1    )
				parser.add_argument('''--output_dir'''    ,  type=UpperCamelCase__    ,  default='''./results'''    )
				return parser.parse_args()


__UpperCAmelCase												=load("accuracy")






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   Dict:
				__lowerCamelCase     ,  __lowerCamelCase          =  eval_pred
				__lowerCamelCase          =  np.argmax(UpperCamelCase__    ,  axis=1    )
				return metric.compute(predictions=UpperCamelCase__    ,  references=UpperCamelCase__    )




class    a__				(       UpperCAmelCase__			):






					def __init__(			self		:		Optional[int]  ,    a		:		List[str]   ):





									"""simple docstring"""



									super().__init__()
									__lowerCamelCase          =  trainer






					def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    a		:		List[str]  ,    a		:		Union[str, Any]  ,    a		:		Optional[int]  ,    **a		:		List[Any]   ):





									"""simple docstring"""



									if control.should_evaluate:
													__lowerCamelCase          =  deepcopy(__UpperCAmelCase   )
													self._trainer.evaluate(eval_dataset=self._trainer.train_dataset  ,    metric_key_prefix='''train'''   )
													return control_copy






def __lowerCAmelCase			(							)     ->   Tuple:
				__lowerCamelCase          =  get_args()
				set_seed(args.seed    )

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

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

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

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

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

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

				__lowerCamelCase          =  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=0.0_1    ,  metric_for_best_model='''accuracy'''    ,  run_name='''complexity-java'''    ,  report_to='''wandb'''    ,  )

				__lowerCamelCase          =  Trainer(
				    model=UpperCamelCase__    ,  args=UpperCamelCase__    ,  train_dataset=tokenized_datasets['''train''']    ,  eval_dataset=tokenized_datasets['''valid''']    ,  tokenizer=UpperCamelCase__    ,  data_collator=UpperCamelCase__    ,  compute_metrics=UpperCamelCase__    ,  )

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


if __name__ == "__main__":
		main()

'''simple docstring'''




def __lowerCAmelCase			(							UpperCamelCase__    )     ->   str:
    __lowerCamelCase          =  []
    __lowerCamelCase          =  set({'''(''', '''[''', '''{'''}    )
    __lowerCamelCase          =  set({''')''', ''']''', '''}'''}    )
    __lowerCamelCase          =  {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}

    for i in range(len(UpperCamelCase__    )    ):
        if s[i] in open_brackets:
            stack.append(s[i]    )

        elif s[i] in closed_brackets and (
            len(UpperCamelCase__    ) == 0 or (len(UpperCamelCase__    ) > 0 and open_to_closed[stack.pop()] != s[i])
        ):
            return False

    return len(UpperCamelCase__    ) == 0






def __lowerCAmelCase			(							)     ->   Union[str, Any]:
    __lowerCamelCase          =  input('''Enter sequence of brackets: '''    )
    if is_balanced(UpperCamelCase__    ):
        print(UpperCamelCase__    ,  '''is balanced'''    )
    else:
        print(UpperCamelCase__    ,  '''is not balanced'''    )


if __name__ == "__main__":
  main()
