



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       (			)    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= argparse.ArgumentParser()
							parser.add_argument('--model_ckpt'							,       type=a__							,       default='microsoft/unixcoder-base-nine'       )
							parser.add_argument('--num_epochs'							,       type=a__							,       default=5       )
							parser.add_argument('--batch_size'							,       type=a__							,       default=6       )
							parser.add_argument('--gradient_accumulation_steps'							,       type=a__							,       default=1       )
							parser.add_argument('--freeze'							,       type=a__							,       default=a__       )
							parser.add_argument('--learning_rate'							,       type=a__							,       default=5E-4       )
							parser.add_argument('--seed'							,       type=a__							,       default=0       )
							parser.add_argument('--lr_scheduler_type'							,       type=a__							,       default='cosine'       )
							parser.add_argument('--num_warmup_steps'							,       type=a__							,       default=10       )
							parser.add_argument('--weight_decay'							,       type=a__							,       default=0.01       )
							parser.add_argument('--output_dir'							,       type=a__							,       default='./results'       )
							return parser.parse_args()


lowerCAmelCase__		:					Optional[Any]							=load('''accuracy''')






def      __lowercase       (			a__       )    ->			int:
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= eval_pred
							__SCREAMING_SNAKE_CASE									= np.argmax(a__							,       axis=1       )
							return metric.compute(predictions=a__							,       references=a__       )

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							_A		):







													'''simple docstring'''

													super().__init__()
													__SCREAMING_SNAKE_CASE									= trainer






						def 	_A     (   self				,							_A				,							_A				,							_A				,							**_A		):







													'''simple docstring'''

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







def      __lowercase       (			)    ->			Tuple:
							__SCREAMING_SNAKE_CASE									= get_args()
							set_seed(args.seed       )

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

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

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

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

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

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

							__SCREAMING_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'							,       )

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

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


if __name__ == "__main__":
			main()











def      __lowercase       (			a__ = 10_00       )    ->			int:
							__SCREAMING_SNAKE_CASE									= -1
							__SCREAMING_SNAKE_CASE									= 0
							for a in range(1							,       n // 3       ):
														# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
														__SCREAMING_SNAKE_CASE									= (n * n - 2 * a * n) // (2 * n - 2 * a)
														__SCREAMING_SNAKE_CASE									= n - a - b
														if c * c == (a * a + b * b):
																					__SCREAMING_SNAKE_CASE									= a * b * c
																					if candidate >= product:
																												__SCREAMING_SNAKE_CASE									= candidate
							return product


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






