


"""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   _a			( )      ->			int:
							snake_case_          =						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()


__SCREAMING_SNAKE_CASE							:      int						=      load('accuracy')







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Optional[int]:
							snake_case_    ,  snake_case_          =						eval_pred
							snake_case_          =						np.argmax(_SCREAMING_SNAKE_CASE			,	axis=1			)
							return metric.compute(predictions=_SCREAMING_SNAKE_CASE			,	references=_SCREAMING_SNAKE_CASE			)





class        __A       (snake_case__):





							'''simple docstring'''

							def __init__(			self							: Optional[Any]						,    UpperCAmelCase_							: Optional[int]    )   ->None:



														"""simple docstring"""



														super().__init__()
														snake_case_          =						trainer



							def 						lowerCAmelCase    (			self							: Optional[Any]						,    UpperCAmelCase_							: Union[str, Any]						,    UpperCAmelCase_							: Union[str, Any]						,    UpperCAmelCase_							: int						,    **UpperCAmelCase_							: int    )   ->Any:



														"""simple docstring"""



														if control.should_evaluate:
																					snake_case_          =						deepcopy(UpperCAmelCase_    )
																					self._trainer.evaluate(eval_dataset=self._trainer.train_dataset						,    metric_key_prefix="""train"""    )
																					return control_copy







def   _a			( )      ->			int:
							snake_case_          =						get_args()
							set_seed(args.seed			)

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

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

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

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

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

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

							snake_case_          =						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"""			,	)

							snake_case_          =						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"""
from collections import defaultdict
from math import gcd







def   _a			( _SCREAMING_SNAKE_CASE = 1_500_000			)      ->			int:
							snake_case_          =						defaultdict(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						2
							while 2 * euclid_m * (euclid_m + 1) <= limit:
														for euclid_n in range((euclid_m % 2) + 1			,	_SCREAMING_SNAKE_CASE			,	2			):
																					if gcd(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			) > 1:
																												continue
																					snake_case_          =						2 * euclid_m * (euclid_m + euclid_n)
																					for perimeter in range(_SCREAMING_SNAKE_CASE			,	limit + 1			,	_SCREAMING_SNAKE_CASE			):
																												frequencies[perimeter] += 1
														euclid_m += 1
							return sum(1 for frequency in frequencies.values() if frequency == 1			)


if __name__ == "__main__":
				print(f"""{solution() = }""")



