


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        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		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=0.01      )
						parser.add_argument('''--output_dir'''    ,       type=lowercase__    ,       default='''./results'''      )
						return parser.parse_args()


__lowerCAmelCase	: List[Any]  				=load('accuracy')


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		eval_pred
						__SCREAMING_SNAKE_CASE						:     int					=		np.argmax(lowercase__    ,       axis=1      )
						return metric.compute(predictions=lowercase__    ,       references=lowercase__      )
class        _lowercase      (			A__       ):






				'''simple docstring'''



				def __init__(       self			:Any			,			lowerCAmelCase__			:Union[str, Any]	)		->			None:
										super().__init__()
										__SCREAMING_SNAKE_CASE						:     Dict					=		trainer



				def 		__magic_name__(       self			:Dict			,			lowerCAmelCase__			:Optional[int]			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:List[str]			,			**lowerCAmelCase__			:int	)		->			Optional[int]:
										if control.should_evaluate:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		deepcopy(lowerCAmelCase__	)
																self._trainer.evaluate(eval_dataset=self._trainer.train_dataset			,			metric_key_prefix='''train'''	)
																return control_copy




def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		get_args()
						set_seed(args.seed      )

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

						print('''Loading tokenizer and model'''      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		AutoTokenizer.from_pretrained(args.model_ckpt      )
						__SCREAMING_SNAKE_CASE						:     Dict					=		tokenizer.eos_token
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		AutoModelForSequenceClassification.from_pretrained(args.model_ckpt    ,       num_labels=7      )
						__SCREAMING_SNAKE_CASE						:     str					=		model.config.eos_token_id

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

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

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

						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		train_test_validation.map(
						    lowercase__    ,       batched=lowercase__    ,       remove_columns=train_test_validation['''train'''].column_names    ,       )
						__SCREAMING_SNAKE_CASE						:     Dict					=		DataCollatorWithPadding(tokenizer=lowercase__      )

						__SCREAMING_SNAKE_CASE						:     Any					=		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						:     Any					=		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()




from __future__ import annotations


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		0.00
						__SCREAMING_SNAKE_CASE						:     List[str]					=		0
						for resistor in resistors:
												if resistor <= 0:
																		__SCREAMING_SNAKE_CASE						:     Any					=		F'''Resistor at index {index} has a negative or zero value!'''
																		raise ValueError(lowercase__      )
												first_sum += 1 / float(lowercase__      )
												index += 1
						return 1 / first_sum


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		0.00
						__SCREAMING_SNAKE_CASE						:     int					=		0
						for resistor in resistors:
												sum_r += resistor
												if resistor < 0:
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		F'''Resistor at index {index} has a negative value!'''
																		raise ValueError(lowercase__      )
												index += 1
						return sum_r


if __name__ == "__main__":
						import doctest

						doctest.testmod()
