





'''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 	_lowerCAmelCase      (  )					->					Union[str, Any]:
					__A						:			Optional[int]								=	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=0.01      )
					parser.add_argument('--output_dir'			,							type=__snake_case			,							default='./results'      )
					return parser.parse_args()


lowercase__   :				Tuple      =   load('''accuracy''')






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					Any:
					__A						:			List[Any]								=	eval_pred
					__A						:			Dict								=	np.argmax(__snake_case			,							axis=1      )
					return metric.compute(predictions=__snake_case			,							references=__snake_case      )


class 			SCREAMING_SNAKE_CASE   (a__							):


					def __init__(						self						,     _UpperCAmelCase):

										'''simple docstring'''






										super().__init__()
										__A						:			int								=	trainer


					def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     **_UpperCAmelCase):

										'''simple docstring'''






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






def 	_lowerCAmelCase      (  )					->					str:
					__A						:			List[Any]								=	get_args()
					set_seed(args.seed      )

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

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

					if args.freeze:
										for param in model.roberta.parameters():
															__A						:			Optional[Any]								=	False

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

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

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

					__A						:			str								=	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'			,							)

					__A						:			Tuple								=	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()






'''simple docstring'''




from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch


@require_torch
@require_tf
class 			SCREAMING_SNAKE_CASE   (a__							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	SMALL_MODEL_IDENTIFIER
          __A						:			Any								=	'pt'
          __A						:			str								=	'tf'


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Any								=	AutoModel.from_pretrained(self.test_model)
          model_pt.save_pretrained(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	TFAutoModel.from_pretrained(self.test_model						,     from_pt=_UpperCAmelCase)
          model_tf.save_pretrained(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	'mock_framework'

          # Framework provided - return whatever the user provides
          __A						:			Union[str, Any]								=	FeaturesManager.determine_framework(self.test_model						,     _UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)

          # Local checkpoint and framework provided - return provided framework
          # PyTorch checkpoint
          with TemporaryDirectory() as local_pt_ckpt:
               self._setup_pt_ckpt(_UpperCAmelCase)
               __A						:			List[str]								=	FeaturesManager.determine_framework(_UpperCAmelCase						,     _UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)

          # TensorFlow checkpoint
          with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_UpperCAmelCase)
               __A						:			Tuple								=	FeaturesManager.determine_framework(_UpperCAmelCase						,     _UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          with TemporaryDirectory() as local_pt_ckpt:
               self._setup_pt_ckpt(_UpperCAmelCase)
               __A						:			Optional[int]								=	FeaturesManager.determine_framework(_UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     self.framework_pt)

          # TensorFlow checkpoint
          with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_UpperCAmelCase)
               __A						:			List[str]								=	FeaturesManager.determine_framework(_UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     self.framework_tf)

          # Invalid local checkpoint
          with TemporaryDirectory() as local_invalid_ckpt:
               with self.assertRaises(_UpperCAmelCase):
                    __A						:			Tuple								=	FeaturesManager.determine_framework(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Tuple								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'						,     _UpperCAmelCase):
               __A						:			Union[str, Any]								=	FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase						,     self.framework_pt)

          # PyTorch not in environment -> use TensorFlow
          __A						:			List[str]								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_torch_available'						,     _UpperCAmelCase):
               __A						:			List[Any]								=	FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase						,     self.framework_tf)

          # Both in environment -> use PyTorch
          __A						:			Any								=	MagicMock(return_value=_UpperCAmelCase)
          __A						:			Dict								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'						,     _UpperCAmelCase), patch(
              'transformers.onnx.features.is_torch_available'						,     _UpperCAmelCase):
               __A						:			int								=	FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase						,     self.framework_pt)

          # Both not in environment -> raise error
          __A						:			List[str]								=	MagicMock(return_value=_UpperCAmelCase)
          __A						:			Tuple								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'						,     _UpperCAmelCase), patch(
              'transformers.onnx.features.is_torch_available'						,     _UpperCAmelCase):
               with self.assertRaises(_UpperCAmelCase):
                    __A						:			int								=	FeaturesManager.determine_framework(self.test_model)