
"""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 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      List[str]          =   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=0.0_1      )
		parser.add_argument('--output_dir',						type=A__,						default='./results'      )
		return parser.parse_args()


_A						=			load('accuracy')
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase     ,   lowerCamelCase					:      Tuple          =   eval_pred
		lowerCamelCase					:      List[str]          =   np.argmax(A__,						axis=1      )
		return metric.compute(predictions=A__,						references=A__      )






class 	_lowercase       (    __lowerCamelCase		):




	def __init__(			self		,			UpperCAmelCase_ )		->							int:
			super().__init__()
			lowerCamelCase					:      Tuple          =   trainer




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )		->							List[str]:
			if control.should_evaluate:
					lowerCamelCase					:      str          =   deepcopy(UpperCamelCase_ )
					self._trainer.evaluate(eval_dataset=self._trainer.train_dataset		,			metric_key_prefix='train' )
					return control_copy


def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      Union[str, Any]          =   get_args()
		set_seed(args.seed      )

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

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

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

		lowerCamelCase					:      List[str]          =   ClassLabel(num_classes=7,						names=list(set(train_test_validation['train']['complexity']      )      )      )

		def tokenize(a_      ):
				lowerCamelCase					:      str          =   tokenizer(example['src'],						truncation=A__,						max_length=1024      )
				lowerCamelCase					:      Optional[int]          =   labels.straint(example['complexity']      )
				return {
				    "input_ids": inputs["input_ids"],
				    "attention_mask": inputs["attention_mask"],
				    "label": label,
				}

		lowerCamelCase					:      Tuple          =   train_test_validation.map(
		    A__,						batched=A__,						remove_columns=train_test_validation['train'].column_names,						)
		lowerCamelCase					:      int          =   DataCollatorWithPadding(tokenizer=A__      )

		lowerCamelCase					:      Tuple          =   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					:      Tuple          =   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()








"""simple docstring"""

def 		UpperCAmelCase (		a_ = 10      ):





		'''simple docstring'''


		if not isinstance(a_,						a_      ) or n < 0:
				raise ValueError('Invalid input'      )
		lowerCamelCase					:      Union[str, Any]          =   10**n
		lowerCamelCase					:      int          =   2_8433 * (pow(2,						783_0457,						a_      )) + 1
		return str(number % modulus      )


if __name__ == "__main__":
					from doctest import testmod

					testmod()
					print(F"""{solution(1_0) = }""")






