
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      ( )     ->Optional[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= 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=0.01	)
			parser.add_argument('''--output_dir'''		,						type=a		,						default='''./results'''	)
			return parser.parse_args()


__lowerCAmelCase					   =       load('accuracy')


def    a      ( a	)     ->List[str]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= eval_pred
			SCREAMING_SNAKE_CASE     						= np.argmax(a		,						axis=1	)
			return metric.compute(predictions=a		,						references=a	)





class     lowerCamelCase      (       __lowerCamelCase					):







	def __init__(     self      :str   ,     lowercase      :List[Any]			)		->      None:







				"""simple docstring"""






				super().__init__()
				SCREAMING_SNAKE_CASE     						= trainer







	def  snake_case__     (     self      :Dict   ,     lowercase      :Tuple   ,     lowercase      :List[str]   ,     lowercase      :Optional[int]   ,     **lowercase      :Union[str, Any]			)		->      Optional[Any]:







				"""simple docstring"""






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



def    a      ( )     ->List[Any]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= get_args()
			set_seed(args.seed	)

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

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

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

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

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

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

			SCREAMING_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'''		,						)

			SCREAMING_SNAKE_CASE     						= 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()

import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Optional[Any]    		=	'MCTCTFeatureExtractor'
	UpperCamelCase_  :					List[Any]    		=	'AutoTokenizer'







	def __init__(     self      :Tuple   ,     lowercase      :List[str]   ,     lowercase      :Dict			)		->      Tuple:







				"""simple docstring"""






				super().__init__(lowercase   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= self.feature_extractor
				SCREAMING_SNAKE_CASE     						= False







	def __call__(     self      :Union[str, Any]   ,     *lowercase      :Union[str, Any]   ,     **lowercase      :str			)		->      int:







				"""simple docstring"""






				if self._in_target_context_manager:
							return self.current_processor(*lowercase   ,     **lowercase			)

				if "raw_speech" in kwargs:
							warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''			)
							SCREAMING_SNAKE_CASE     						= kwargs.pop('''raw_speech'''			)
				else:
							SCREAMING_SNAKE_CASE     						= kwargs.pop('''audio'''   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= kwargs.pop('''sampling_rate'''   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= kwargs.pop('''text'''   ,     lowercase			)
				if len(lowercase			) > 0:
							SCREAMING_SNAKE_CASE     						= args[0]
							SCREAMING_SNAKE_CASE     						= args[1:]

				if audio is None and text is None:
							raise ValueError('''You need to specify either an `audio` or `text` input to process.'''			)

				if audio is not None:
							SCREAMING_SNAKE_CASE     						= self.feature_extractor(lowercase   ,     *lowercase   ,     sampling_rate=lowercase   ,     **lowercase			)
				if text is not None:
							SCREAMING_SNAKE_CASE     						= self.tokenizer(lowercase   ,     **lowercase			)

				if text is None:
							return inputs
				elif audio is None:
							return encodings
				else:
							SCREAMING_SNAKE_CASE     						= encodings['''input_ids''']
							return inputs







	def  snake_case__     (     self      :Dict   ,     *lowercase      :Union[str, Any]   ,     **lowercase      :List[str]			)		->      Union[str, Any]:







				"""simple docstring"""






				return self.tokenizer.batch_decode(*lowercase   ,     **lowercase			)







	def  snake_case__     (     self      :List[Any]   ,     *lowercase      :List[Any]   ,     **lowercase      :List[str]			)		->      int:







				"""simple docstring"""






				if self._in_target_context_manager:
							return self.current_processor.pad(*lowercase   ,     **lowercase			)

				SCREAMING_SNAKE_CASE     						= kwargs.pop('''input_features'''   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= kwargs.pop('''labels'''   ,     lowercase			)
				if len(lowercase			) > 0:
							SCREAMING_SNAKE_CASE     						= args[0]
							SCREAMING_SNAKE_CASE     						= args[1:]

				if input_features is not None:
							SCREAMING_SNAKE_CASE     						= self.feature_extractor.pad(lowercase   ,     *lowercase   ,     **lowercase			)
				if labels is not None:
							SCREAMING_SNAKE_CASE     						= self.tokenizer.pad(lowercase   ,     **lowercase			)

				if labels is None:
							return input_features
				elif input_features is None:
							return labels
				else:
							SCREAMING_SNAKE_CASE     						= labels['''input_ids''']
							return input_features







	def  snake_case__     (     self      :Dict   ,     *lowercase      :List[str]   ,     **lowercase      :Union[str, Any]			)		->      List[Any]:







				"""simple docstring"""






				return self.tokenizer.decode(*lowercase   ,     **lowercase			)







	@contextmanager
	def  snake_case__     (     self      :str			)		->      Union[str, Any]:







				"""simple docstring"""






				warnings.warn(
				    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
				    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
				    '''your audio inputs, or in a separate call.'''			)
				SCREAMING_SNAKE_CASE     						= True
				SCREAMING_SNAKE_CASE     						= self.tokenizer
				yield
				SCREAMING_SNAKE_CASE     						= self.feature_extractor
				SCREAMING_SNAKE_CASE     						= False