


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 							__lowercase     (				):
							a__    =  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=1_0     )
							parser.add_argument('--weight_decay'      ,					type=__lowerCAmelCase      ,					default=0.01     )
							parser.add_argument('--output_dir'      ,					type=__lowerCAmelCase      ,					default='./results'     )
							return parser.parse_args()


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






def 							__lowercase     (				__lowerCAmelCase							:       Dict     ):
							a__   ,      a__    =  eval_pred
							a__    =  np.argmax(__lowerCAmelCase      ,					axis=1     )
							return metric.compute(predictions=__lowerCAmelCase      ,					references=__lowerCAmelCase     )







class  snake_case_     (lowerCamelCase_   ):
					def __init__(   self     :Optional[int]    ,__snake_case     :Optional[Any]   )      ->					None:
												super().__init__()
												a__    =  trainer



					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :Tuple    ,__snake_case     :List[Any]    ,__snake_case     :Optional[int]    ,**__snake_case     :List[Any]   )      ->					Any:
												if control.should_evaluate:
																			a__    =  deepcopy(__snake_case   )
																			self._trainer.evaluate(eval_dataset=self._trainer.train_dataset    ,metric_key_prefix='train'   )
																			return control_copy






def 							__lowercase     (				):
							a__    =  get_args()
							set_seed(args.seed     )

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

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

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

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

							def tokenize(__lowerCAmelCase							:       List[Any]     ):
														a__    =  tokenizer(example['src']      ,					truncation=__lowerCAmelCase      ,					max_length=1_0_2_4     )
														a__    =  labels.straint(example['complexity']     )
														return {
														    "input_ids": inputs["input_ids"],
														    "attention_mask": inputs["attention_mask"],
														    "label": label,
														}

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

							a__    =  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__    =  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()




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case							: Optional[Any]      =   {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Optional[int]      =   [
						    '''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FocalNetForImageClassification''',
						    '''FocalNetForMaskedImageModeling''',
						    '''FocalNetBackbone''',
						    '''FocalNetModel''',
						    '''FocalNetPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_focalnet import (
												    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FocalNetBackbone,
												    FocalNetForImageClassification,
												    FocalNetForMaskedImageModeling,
												    FocalNetModel,
												    FocalNetPreTrainedModel,
												)

else:
						import sys

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




