

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A_			:					str    			=     {
    'configuration_wav2vec2': ['WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Wav2Vec2Config'],
    'feature_extraction_wav2vec2': ['Wav2Vec2FeatureExtractor'],
    'processing_wav2vec2': ['Wav2Vec2Processor'],
    'tokenization_wav2vec2': ['Wav2Vec2CTCTokenizer', 'Wav2Vec2Tokenizer'],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					Optional[int]    			=     [
	    'WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'Wav2Vec2ForAudioFrameClassification',
	    'Wav2Vec2ForCTC',
	    'Wav2Vec2ForMaskedLM',
	    'Wav2Vec2ForPreTraining',
	    'Wav2Vec2ForSequenceClassification',
	    'Wav2Vec2ForXVector',
	    'Wav2Vec2Model',
	    'Wav2Vec2PreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					Dict    			=     [
	    'TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFWav2Vec2ForCTC',
	    'TFWav2Vec2Model',
	    'TFWav2Vec2PreTrainedModel',
	    'TFWav2Vec2ForSequenceClassification',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					Tuple    			=     [
	    'FlaxWav2Vec2ForCTC',
	    'FlaxWav2Vec2ForPreTraining',
	    'FlaxWav2Vec2Model',
	    'FlaxWav2Vec2PreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
	from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
	from .processing_wavaveca import WavaVecaProcessor
	from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_wavaveca import (
		    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    WavaVecaForAudioFrameClassification,
		    WavaVecaForCTC,
		    WavaVecaForMaskedLM,
		    WavaVecaForPreTraining,
		    WavaVecaForSequenceClassification,
		    WavaVecaForXVector,
		    WavaVecaModel,
		    WavaVecaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_wavaveca import (
		    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFWavaVecaForCTC,
		    TFWavaVecaForSequenceClassification,
		    TFWavaVecaModel,
		    TFWavaVecaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_wavaveca import (
		    FlaxWavaVecaForCTC,
		    FlaxWavaVecaForPreTraining,
		    FlaxWavaVecaModel,
		    FlaxWavaVecaPreTrainedModel,
		)


else:
	import sys

	A_			:					Union[str, Any]    			=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






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


A_			:					Any    			=     load('accuracy')

def 		UpperCamelCase   (lowercase_:					Dict   )	-> Any:
	A__  ,  A__				:       int	  =						eval_pred
	A__				:       List[str]	  =						np.argmax(lowercase_       , axis=1   )
	return metric.compute(predictions=lowercase_       , references=lowercase_   )



class        _a		(__magic_name__							):


					'''simple docstring'''






					def __init__(					self       ,			A__     ):
						super().__init__()
						A__				:       Any	  =						trainer






					def     __A     (					self       ,			A__       ,			A__       ,			A__       ,			**A__     ):
						if control.should_evaluate:
							A__				:       Dict	  =						deepcopy(A__     )
							self._trainer.evaluate(eval_dataset=self._trainer.train_dataset       ,			metric_key_prefix="""train"""     )
							return control_copy



def 		UpperCamelCase   ()	-> Optional[int]:
	A__				:       Optional[Any]	  =						get_args()
	set_seed(args.seed   )

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

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

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

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

	def tokenize(lowercase_:					Optional[int]   ):
		A__				:       Union[str, Any]	  =						tokenizer(example["""src"""]       , truncation=lowercase_       , max_length=1024   )
		A__				:       Any	  =						labels.straint(example["""complexity"""]   )
		return {
		    "input_ids": inputs["input_ids"],
		    "attention_mask": inputs["attention_mask"],
		    "label": label,
		}

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

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

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


if __name__ == "__main__":
	main()



