

'''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 						__lowerCamelCase	(       )	->							str:






						"""simple docstring"""





						A__					:       List[str]     =argparse.ArgumentParser()
						parser.add_argument("""--model_ckpt""",		type=__snake_case,		default="""microsoft/unixcoder-base-nine"""	)
						parser.add_argument("""--num_epochs""",		type=__snake_case,		default=5	)
						parser.add_argument("""--batch_size""",		type=__snake_case,		default=6	)
						parser.add_argument("""--gradient_accumulation_steps""",		type=__snake_case,		default=1	)
						parser.add_argument("""--freeze""",		type=__snake_case,		default=__snake_case	)
						parser.add_argument("""--learning_rate""",		type=__snake_case,		default=5E-4	)
						parser.add_argument("""--seed""",		type=__snake_case,		default=0	)
						parser.add_argument("""--lr_scheduler_type""",		type=__snake_case,		default="""cosine"""	)
						parser.add_argument("""--num_warmup_steps""",		type=__snake_case,		default=10	)
						parser.add_argument("""--weight_decay""",		type=__snake_case,		default=0.01	)
						parser.add_argument("""--output_dir""",		type=__snake_case,		default="""./results"""	)
						return parser.parse_args()


__snake_case			:     Optional[Any]										=      load('accuracy')
def 						__lowerCamelCase	(       __snake_case					:		Tuple	)	->							Dict:






						"""simple docstring"""





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



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






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





									'''simple docstring'''
									super().__init__()
									A__					:       int     =trainer



			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						List[Any]		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						str		,      **lowerCAmelCase_	:						Union[str, Any]			)      ->		Optional[Any]:





									'''simple docstring'''
									if control.should_evaluate:
															A__					:       Optional[int]     =deepcopy(lowerCAmelCase_			)
															self._trainer.evaluate(eval_dataset=self._trainer.train_dataset		,      metric_key_prefix="""train"""			)
															return control_copy


def 						__lowerCamelCase	(       )	->							Optional[int]:






						"""simple docstring"""





						A__					:       Dict     =get_args()
						set_seed(args.seed	)

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

						print("""Loading tokenizer and model"""	)
						A__					:       List[Any]     =AutoTokenizer.from_pretrained(args.model_ckpt	)
						A__					:       Any     =tokenizer.eos_token
						A__					:       Dict     =AutoModelForSequenceClassification.from_pretrained(args.model_ckpt,		num_labels=7	)
						A__					:       Tuple     =model.config.eos_token_id

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

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

						def tokenize(__snake_case					:		List[str]	):
												A__					:       Optional[int]     =tokenizer(example["""src"""],		truncation=__snake_case,		max_length=1_024	)
												A__					:       Any     =labels.straint(example["""complexity"""]	)
												return {
												    "input_ids": inputs["input_ids"],
												    "attention_mask": inputs["attention_mask"],
												    "label": label,
												}

						A__					:       List[Any]     =train_test_validation.map(
						    __snake_case,		batched=__snake_case,		remove_columns=train_test_validation["""train"""].column_names,		)
						A__					:       Optional[int]     =DataCollatorWithPadding(tokenizer=__snake_case	)

						A__					:       int     =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""",		)

						A__					:       List[Any]     =Trainer(
						    model=__snake_case,		args=__snake_case,		train_dataset=tokenized_datasets["""train"""],		eval_dataset=tokenized_datasets["""valid"""],		tokenizer=__snake_case,		data_collator=__snake_case,		compute_metrics=__snake_case,		)

						print("""Training..."""	)
						trainer.add_callback(CustomCallback(__snake_case	)	)
						trainer.train()


if __name__ == "__main__":
		main()




'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


__snake_case			:     Any										=      {
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case			:     Optional[int]										=      [
		    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ASTForAudioClassification',
		    'ASTModel',
		    'ASTPreTrainedModel',
		]

try:
		if not is_speech_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case			:     List[Any]										=      ['ASTFeatureExtractor']

if TYPE_CHECKING:
		from .configuration_audio_spectrogram_transformer import (
		    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ASTConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_audio_spectrogram_transformer import (
				    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ASTForAudioClassification,
				    ASTModel,
				    ASTPreTrainedModel,
				)

		try:
				if not is_speech_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
		import sys

		__snake_case			:     Optional[Any]										=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

