




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__    (		):







	'''simple docstring'''





	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()


lowercase__				:     Optional[Any] 		=						load("accuracy")


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_    ,      snake_case_		    =     eval_pred
	snake_case_		    =     np.argmax(_A     ,	axis=1							)
	return metric.compute(predictions=_A     ,	references=_A							)
class 		UpperCAmelCase   (				UpperCAmelCase__			):







						'''simple docstring'''





						def __init__(						self							:     Any       ,	__lowercase							:     Optional[int]						):






							"""simple docstring"""






							super().__init__()
							snake_case_		    =     trainer






						def        snake_case__						(						self							:     Dict       ,	__lowercase							:     Tuple       ,	__lowercase							:     Optional[Any]       ,	__lowercase							:     int       ,	**__lowercase							:     List[str]						):






							"""simple docstring"""






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







def 						lowerCamelCase__    (		):







	'''simple docstring'''





	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(_A							):
		snake_case_		    =     tokenizer(example["src"]     ,	truncation=_A     ,	max_length=1024							)
		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(
	    _A     ,	batched=_A     ,	remove_columns=train_test_validation["train"].column_names     ,	)
	snake_case_		    =     DataCollatorWithPadding(tokenizer=_A							)

	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=_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 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     []
	snake_case_		    =     1

	while len(_A							) < 1E6:
		constant.append(str(_A							)							)
		i += 1

	snake_case_		    =     "".join(_A							)

	return (
	    int(constant[0]							)
	    * int(constant[9]							)
	    * int(constant[99]							)
	    * int(constant[999]							)
	    * int(constant[9999]							)
	    * int(constant[99999]							)
	    * int(constant[999999]							)
	)


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