



'''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 				snake_case_  (      )   ->     Optional[int]:
							UpperCAmelCase				: Any											=	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=0.0_1						)
							parser.add_argument('''--output_dir'''     ,   type=_lowerCAmelCase     ,   default='''./results'''						)
							return parser.parse_args()


UpperCamelCase__:     Any										=    load("accuracy")


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

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  List[Any]	,    __snake_case    :  Optional[int]	)    ->	None:
								super().__init__()
								UpperCAmelCase				: Dict											=	trainer







	def   A			(		self    :  str	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  int	,    __snake_case    :  Any	,    **__snake_case    :  List[Any]	)    ->	Dict:
								if control.should_evaluate:
															UpperCAmelCase				: Optional[int]											=	deepcopy(__snake_case	)
															self._trainer.evaluate(eval_dataset=self._trainer.train_dataset	,    metric_key_prefix='''train'''	)
															return control_copy


def 				snake_case_  (      )   ->     List[Any]:
							UpperCAmelCase				: Tuple											=	get_args()
							set_seed(args.seed						)

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

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

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

							UpperCAmelCase				: Optional[Any]											=	ClassLabel(num_classes=7     ,   names=list(set(train_test_validation['''train''']['''complexity''']						)						)						)

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

							UpperCAmelCase				: List[str]											=	train_test_validation.map(
							    _lowerCAmelCase     ,   batched=_lowerCAmelCase     ,   remove_columns=train_test_validation['''train'''].column_names     ,   )
							UpperCAmelCase				: int											=	DataCollatorWithPadding(tokenizer=_lowerCAmelCase						)

							UpperCAmelCase				: 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'''     ,   )

							UpperCAmelCase				: int											=	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()










'''simple docstring'''






import math

import tensorflow as tf
from packaging import version


def 				snake_case_  (      _lowerCAmelCase						:		List[str]						)   ->     Dict:
							UpperCAmelCase				: Tuple											=	tf.convert_to_tensor(_lowerCAmelCase						)
							UpperCAmelCase				: Dict											=	0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0						)     ,   x.dtype						)						))

							return x * cdf


def 				snake_case_  (      _lowerCAmelCase						:		int						)   ->     List[Any]:
							UpperCAmelCase				: str											=	tf.convert_to_tensor(_lowerCAmelCase						)
							UpperCAmelCase				: Union[str, Any]											=	tf.cast(math.pi     ,   x.dtype						)
							UpperCAmelCase				: Tuple											=	tf.cast(0.0_4_4_7_1_5     ,   x.dtype						)
							UpperCAmelCase				: List[Any]											=	0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi						) * (x + coeff * tf.pow(_lowerCAmelCase     ,   3						))						))

							return x * cdf


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]						)   ->     Any:
							UpperCAmelCase				: List[Any]											=	tf.convert_to_tensor(_lowerCAmelCase						)

							return x * tf.tanh(tf.math.softplus(_lowerCAmelCase						)						)


def 				snake_case_  (      _lowerCAmelCase						:		str						)   ->     Tuple:
							UpperCAmelCase				: Union[str, Any]											=	tf.convert_to_tensor(_lowerCAmelCase						)
							UpperCAmelCase				: Union[str, Any]											=	tf.cast(0.0_4_4_7_1_5     ,   x.dtype						)
							UpperCAmelCase				: Tuple											=	tf.cast(0.7_9_7_8_8_4_5_6_0_8     ,   x.dtype						)

							return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)						))


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]						)   ->     Optional[int]:
							UpperCAmelCase				: str											=	tf.convert_to_tensor(_lowerCAmelCase						)
							UpperCAmelCase				: Optional[int]											=	tf.cast(1.7_0_2     ,   x.dtype						)
							return x * tf.math.sigmoid(coeff * x						)


def 				snake_case_  (      _lowerCAmelCase						:		Any						)   ->     Any:
							return tf.clip_by_value(_gelu(_lowerCAmelCase						)     ,   -10     ,   10						)


def 				snake_case_  (      _lowerCAmelCase						:		Dict     ,   _lowerCAmelCase						:		Optional[Any]=-1						)   ->     Union[str, Any]:
							UpperCAmelCase							,	UpperCAmelCase				: Optional[Any]											=	tf.split(_lowerCAmelCase     ,   2     ,   axis=_lowerCAmelCase						)
							return a * tf.math.sigmoid(_lowerCAmelCase						)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):


						def 				snake_case_  (      _lowerCAmelCase						:		Tuple						)   ->     Any:
													return tf.keras.activations.gelu(_lowerCAmelCase     ,   approximate=_lowerCAmelCase						)

						UpperCamelCase__:     List[Any]										=    tf.keras.activations.gelu




						UpperCamelCase__:     List[Any]										=    approximate_gelu_wrap
else:
						UpperCamelCase__:     List[str]										=    _gelu
						UpperCamelCase__:     Tuple										=    _gelu_new


UpperCamelCase__:     List[Any]										=    {
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}




def 				snake_case_  (      _lowerCAmelCase						:		Dict						)   ->     List[Any]:
							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(f"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}"""						)





