






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 						a    (		):






				'''simple docstring'''
				lowercase__     				=    argparse.ArgumentParser()
				parser.add_argument('''--model_ckpt'''	,     type=lowerCamelCase_	,     default='''microsoft/unixcoder-base-nine'''       )
				parser.add_argument('''--num_epochs'''	,     type=lowerCamelCase_	,     default=5       )
				parser.add_argument('''--batch_size'''	,     type=lowerCamelCase_	,     default=6       )
				parser.add_argument('''--gradient_accumulation_steps'''	,     type=lowerCamelCase_	,     default=1       )
				parser.add_argument('''--freeze'''	,     type=lowerCamelCase_	,     default=lowerCamelCase_       )
				parser.add_argument('''--learning_rate'''	,     type=lowerCamelCase_	,     default=5e-4       )
				parser.add_argument('''--seed'''	,     type=lowerCamelCase_	,     default=0       )
				parser.add_argument('''--lr_scheduler_type'''	,     type=lowerCamelCase_	,     default='''cosine'''       )
				parser.add_argument('''--num_warmup_steps'''	,     type=lowerCamelCase_	,     default=10       )
				parser.add_argument('''--weight_decay'''	,     type=lowerCamelCase_	,     default=0.01       )
				parser.add_argument('''--output_dir'''	,     type=lowerCamelCase_	,     default='''./results'''       )
				return parser.parse_args()


A__			:    List[str]         =				load('accuracy')






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__				,		lowercase__     				=    eval_pred
				lowercase__     				=    np.argmax(lowerCamelCase_	,     axis=1       )
				return metric.compute(predictions=lowerCamelCase_	,     references=lowerCamelCase_       )
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				def __init__(  self		:					Optional[Any],						lowerCamelCase		:					str						):

								'''simple docstring'''
								super().__init__()
								lowercase__     				=    trainer




				def        lowercase__     (  self		:					List[Any],						lowerCamelCase		:					Any,						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Optional[Any],						**lowerCamelCase		:					List[str]						):

								'''simple docstring'''
								if control.should_evaluate:
												lowercase__     				=    deepcopy(lowerCamelCase						)
												self._trainer.evaluate(eval_dataset=self._trainer.train_dataset,						metric_key_prefix='''train'''						)
												return control_copy






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    get_args()
				set_seed(args.seed       )

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

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

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

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

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

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

				lowercase__     				=    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'''	,     )

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

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


if __name__ == "__main__":
							main()












class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								# we need a list not a string, so do something to change the type
								lowercase__     				=    arr.split(''','''						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    [int(self.array[0]						)] * len(self.array						)
								lowercase__     				=    [int(self.array[0]						)] * len(self.array						)
								for i in range(1,						len(self.array						)						):
												lowercase__     				=    max(
												    int(self.array[i]						) + sum_value[i - 1],						int(self.array[i]						)						)
												lowercase__     				=    max(sum_value[i],						rear[i - 1]						)
								return rear[len(self.array						) - 1]


if __name__ == "__main__":
							A__			:    Dict         =				input('please input some numbers:')
							A__			:    Union[str, Any]         =				SubArray(whole_array)
							A__			:    int         =				array.solve_sub_array()
							print(('the results is:', re))




