



'''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 		_snake_case		( )      ->						Union[str, Any]:







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


UpperCAmelCase     		=   load('accuracy')






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						List[str]:







	"""simple docstring"""
	lowerCAmelCase,       lowerCAmelCase   =     eval_pred
	lowerCAmelCase   =     np.argmax(_SCREAMING_SNAKE_CASE							,		axis=1							)
	return metric.compute(predictions=_SCREAMING_SNAKE_CASE							,		references=_SCREAMING_SNAKE_CASE							)

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						def __init__( self     ,	A_				)     ->     None:
							super().__init__()
							lowerCAmelCase   =     trainer




						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	**A_				)     ->     Dict:
							if control.should_evaluate:
								lowerCAmelCase   =     deepcopy(A_				)
								self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,	metric_key_prefix="""train"""				)
								return control_copy







def 		_snake_case		( )      ->						Tuple:







	"""simple docstring"""
	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(_SCREAMING_SNAKE_CASE					:						Optional[int]							):
		lowerCAmelCase   =     tokenizer(example["""src"""]							,		truncation=_SCREAMING_SNAKE_CASE							,		max_length=1_024							)
		lowerCAmelCase   =     labels.straint(example["""complexity"""]							)
		return {
		    "input_ids": inputs["input_ids"],
		    "attention_mask": inputs["attention_mask"],
		    "label": label,
		}

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

	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=0.01							,		metric_for_best_model="""accuracy"""							,		run_name="""complexity-java"""							,		report_to="""wandb"""							,		)

	lowerCAmelCase   =     Trainer(
	    model=_SCREAMING_SNAKE_CASE							,		args=_SCREAMING_SNAKE_CASE							,		train_dataset=tokenized_datasets["""train"""]							,		eval_dataset=tokenized_datasets["""valid"""]							,		tokenizer=_SCREAMING_SNAKE_CASE							,		data_collator=_SCREAMING_SNAKE_CASE							,		compute_metrics=_SCREAMING_SNAKE_CASE							,		)

	print("""Training..."""							)
	trainer.add_callback(CustomCallback(_SCREAMING_SNAKE_CASE							)							)
	trainer.train()


if __name__ == "__main__":
		main()




'''simple docstring'''

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						pass







class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						pass







class __snake_case:


						'''simple docstring'''



						def __init__( self				)     ->     int:
							lowerCAmelCase   =     [
							    [],
							    [],
							    [],
							]


						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     None:
							try:
								if len(self.queues[priority]				) >= 100:
									raise OverflowError("""Maximum queue size is 100"""				)
								self.queues[priority].append(A_				)
							except IndexError:
								raise ValueError("""Valid priorities are 0, 1, and 2"""				)


						def     __snake_case   ( self				)     ->     int:
							for queue in self.queues:
								if queue:
									return queue.pop(0				)
							raise UnderFlowError("""All queues are empty"""				)




						def __str__( self				)     ->     str:
							return "\n".join(f'Priority {i}: {q}' for i, q in enumerate(self.queues				)				)







class __snake_case:


						'''simple docstring'''



						def __init__( self				)     ->     Dict:
							lowerCAmelCase   =     []


						def     __snake_case   ( self     ,	A_				)     ->     None:
							if len(self.queue				) == 100:
								raise OverFlowError("""Maximum queue size is 100"""				)
							self.queue.append(A_				)


						def     __snake_case   ( self				)     ->     int:
							if not self.queue:
								raise UnderFlowError("""The queue is empty"""				)
							else:
								lowerCAmelCase   =     min(self.queue				)
								self.queue.remove(A_				)
								return data




						def __str__( self				)     ->     str:
							return str(self.queue				)







def 		_snake_case		( )      ->						Tuple:







	"""simple docstring"""
	lowerCAmelCase   =     FixedPriorityQueue()
	fpq.enqueue(0							,		10							)
	fpq.enqueue(1							,		70							)
	fpq.enqueue(0							,		100							)
	fpq.enqueue(2							,		1							)
	fpq.enqueue(2							,		5							)
	fpq.enqueue(1							,		7							)
	fpq.enqueue(2							,		4							)
	fpq.enqueue(1							,		64							)
	fpq.enqueue(0							,		128							)
	print(_SCREAMING_SNAKE_CASE							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(_SCREAMING_SNAKE_CASE							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)






def 		_snake_case		( )      ->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase   =     ElementPriorityQueue()
	epq.enqueue(10							)
	epq.enqueue(70							)
	epq.enqueue(100							)
	epq.enqueue(1							)
	epq.enqueue(5							)
	epq.enqueue(7							)
	epq.enqueue(4							)
	epq.enqueue(64							)
	epq.enqueue(128							)
	print(_SCREAMING_SNAKE_CASE							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(_SCREAMING_SNAKE_CASE							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)


if __name__ == "__main__":
				fixed_priority_queue()
				element_priority_queue()