





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__()				->      Union[str, Any]:







	'''simple docstring'''







	lowerCamelCase__								=		argparse.ArgumentParser()
	parser.add_argument('''--model_ckpt'''       ,type=__snake_case       ,default='''microsoft/unixcoder-base-nine'''							)
	parser.add_argument('''--num_epochs'''       ,type=__snake_case       ,default=5							)
	parser.add_argument('''--batch_size'''       ,type=__snake_case       ,default=6							)
	parser.add_argument('''--gradient_accumulation_steps'''       ,type=__snake_case       ,default=1							)
	parser.add_argument('''--freeze'''       ,type=__snake_case       ,default=__snake_case							)
	parser.add_argument('''--learning_rate'''       ,type=__snake_case       ,default=5E-4							)
	parser.add_argument('''--seed'''       ,type=__snake_case       ,default=0							)
	parser.add_argument('''--lr_scheduler_type'''       ,type=__snake_case       ,default='''cosine'''							)
	parser.add_argument('''--num_warmup_steps'''       ,type=__snake_case       ,default=10							)
	parser.add_argument('''--weight_decay'''       ,type=__snake_case       ,default=0.0_1							)
	parser.add_argument('''--output_dir'''       ,type=__snake_case       ,default='''./results'''							)
	return parser.parse_args()


_a 						=      load("accuracy")





def lowerCAmelCase__(__snake_case							)				->      str:







	'''simple docstring'''







	lowerCamelCase__				,	lowerCamelCase__								=		eval_pred
	lowerCamelCase__								=		np.argmax(__snake_case       ,axis=1							)
	return metric.compute(predictions=__snake_case       ,references=__snake_case							)






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				super().__init__()
				lowerCamelCase__								=		trainer





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







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





def lowerCAmelCase__()				->      List[str]:







	'''simple docstring'''







	lowerCamelCase__								=		get_args()
	set_seed(args.seed							)

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

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

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

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

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

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

	lowerCamelCase__								=		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'''       ,)

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

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


if __name__ == "__main__":
			main()










import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows





def lowerCAmelCase__(__snake_case							)				->      int:  # picklable for multiprocessing







	'''simple docstring'''







	return i + 1





@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def lowerCAmelCase__()				->      Any:







	'''simple docstring'''







	with parallel_backend('''spark'''							):
		assert ParallelBackendConfig.backend_name == "spark"

	lowerCamelCase__								=		[1, 2, 3]
	with pytest.raises(__snake_case							):
		with parallel_backend('''unsupported backend'''							):
			map_nested(__snake_case       ,__snake_case       ,num_proc=2							)

	with pytest.raises(__snake_case							):
		with parallel_backend('''unsupported backend'''							):
			map_nested(__snake_case       ,__snake_case       ,num_proc=-1							)





@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('''num_proc'''       ,[2, -1]							)
def lowerCAmelCase__(__snake_case							)				->      Tuple:







	'''simple docstring'''







	lowerCamelCase__								=		[1, 2]
	lowerCamelCase__								=		{'''a''': 1, '''b''': 2}
	lowerCamelCase__								=		{'''a''': [1, 2], '''b''': [3, 4]}
	lowerCamelCase__								=		{'''a''': {'''1''': 1}, '''b''': 2}
	lowerCamelCase__								=		{'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
	lowerCamelCase__								=		[2, 3]
	lowerCamelCase__								=		{'''a''': 2, '''b''': 3}
	lowerCamelCase__								=		{'''a''': [2, 3], '''b''': [4, 5]}
	lowerCamelCase__								=		{'''a''': {'''1''': 2}, '''b''': 3}
	lowerCamelCase__								=		{'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}

	with parallel_backend('''spark'''							):
		assert map_nested(__snake_case       ,__snake_case       ,num_proc=__snake_case							) == expected_map_nested_sa
		assert map_nested(__snake_case       ,__snake_case       ,num_proc=__snake_case							) == expected_map_nested_sa
		assert map_nested(__snake_case       ,__snake_case       ,num_proc=__snake_case							) == expected_map_nested_sa
		assert map_nested(__snake_case       ,__snake_case       ,num_proc=__snake_case							) == expected_map_nested_sa
		assert map_nested(__snake_case       ,__snake_case       ,num_proc=__snake_case							) == expected_map_nested_sa



