



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				(       ):
						UpperCAmelCase					=			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=0.0_1						)
						parser.add_argument('--output_dir'		,     type=lowercase_		,     default='./results'						)
						return parser.parse_args()


snake_case_    					=	load("""accuracy""")

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase				,   UpperCAmelCase					=			eval_pred
						UpperCAmelCase					=			np.argmax(lowercase_		,     axis=1						)
						return metric.compute(predictions=lowercase_		,     references=lowercase_						)







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def __init__( self			:int       ,  lowercase_			:int )    ->      None:
												super().__init__()
												UpperCAmelCase					=			trainer






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:Dict       ,  lowercase_			:Tuple       ,  lowercase_			:Tuple       ,  **lowercase_			:Dict )    ->      List[str]:
												if control.should_evaluate:
																		UpperCAmelCase					=			deepcopy(lowercase_ )
																		self._trainer.evaluate(eval_dataset=self._trainer.train_dataset       ,  metric_key_prefix='train' )
																		return control_copy


def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			get_args()
						set_seed(args.seed						)

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

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

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

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

						def tokenize(lowercase_						):
												UpperCAmelCase					=			tokenizer(example['src']		,     truncation=lowercase_		,     max_length=1024						)
												UpperCAmelCase					=			labels.straint(example['complexity']						)
												return {
												    "input_ids": inputs["input_ids"],
												    "attention_mask": inputs["attention_mask"],
												    "label": label,
												}

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

						UpperCAmelCase					=			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'		,     )

						UpperCAmelCase					=			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()










"""simple docstring"""


def 			_lowerCAmelCase				(       ):
						for n in range(1		,     1000000						):
												yield n * (n + 1) // 2

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			1
						UpperCAmelCase					=			2
						while i * i <= n:
												UpperCAmelCase					=			0
												while n % i == 0:
																		n //= i
																		multiplicity += 1
												divisors_count *= multiplicity + 1
												i += 1
						if n > 1:
												divisors_count *= 2
						return divisors_count

def 			_lowerCAmelCase				(       ):
						return next(i for i in triangle_number_generator() if count_divisors(lowercase_						) > 500						)


if __name__ == "__main__":
				print(solution())





