


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       (    )						->	Any:


						'''simple docstring'''






						__snake_case				=		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							:					Optional[int]     						=     load('accuracy')





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Any:


						'''simple docstring'''






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






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Tuple						)			->	None:




											'''simple docstring'''

											super().__init__()
											__snake_case				=		trainer






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    **SCREAMING_SNAKE_CASE	:		Any						)			->	str:




											'''simple docstring'''

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





def 	_lowerCAmelCase       (    )						->	Optional[int]:


						'''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(_lowerCAmelCase ):
												__snake_case				=		tokenizer(example["src"]      ,	truncation=_lowerCAmelCase      ,	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(
						    _lowerCAmelCase      ,	batched=_lowerCAmelCase      ,	remove_columns=train_test_validation["train"].column_names      ,	)
						__snake_case				=		DataCollatorWithPadding(tokenizer=_lowerCAmelCase )

						__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=_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()









def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						__snake_case				=		abs(_lowerCAmelCase )
						__snake_case				=		0
						while n > 0:
												res += n % 10
												n //= 10
						return res





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						__snake_case				=		abs(_lowerCAmelCase )
						return n if n < 10 else n % 10 + sum_of_digits(n // 10 )





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	int:


						'''simple docstring'''






						return sum(int(_lowerCAmelCase ) for c in str(abs(_lowerCAmelCase ) ) )





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						from collections.abc import Callable
						from timeit import timeit

						def benchmark_a_function(_lowerCAmelCase      ,	_lowerCAmelCase ) -> None:
												__snake_case				=		F'''{func.__name__}({value})'''
												__snake_case				=		timeit(F'''__main__.{call}'''      ,	setup="import __main__" )
												print(F'''{call:56} = {func(_lowerCAmelCase )} -- {timing:.4f} seconds''' )

						for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
												for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
																		benchmark_a_function(_lowerCAmelCase      ,	_lowerCAmelCase )
												print()


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()





