



"""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     UpperCamelCase_   (   )						->       Dict:

  """simple docstring"""




  lowerCAmelCase_       :    int								=			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=0.01						)
  parser.add_argument('--output_dir'   ,       type=lowerCAmelCase__   ,       default='./results'						)
  return parser.parse_args()


lowercase__		:    int				   =     load("""accuracy""")

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]						)						->       int:

  """simple docstring"""




  lowerCAmelCase_						,lowerCAmelCase_       :    List[str]								=			eval_pred
  lowerCAmelCase_       :    Optional[Any]								=			np.argmax(lowerCAmelCase__   ,       axis=1						)
  return metric.compute(predictions=lowerCAmelCase__   ,       references=lowerCAmelCase__						)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      def __init__(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]	):
        super().__init__()
        lowerCAmelCase_       :    Dict								=			trainer





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      **SCREAMING_SNAKE_CASE_    :				str	):
        if control.should_evaluate:
          lowerCAmelCase_       :    Tuple								=			deepcopy(SCREAMING_SNAKE_CASE_	)
          self._trainer.evaluate(eval_dataset=self._trainer.train_dataset   ,      metric_key_prefix='train'	)
          return control_copy




def     UpperCamelCase_   (   )						->       Any:

  """simple docstring"""




  lowerCAmelCase_       :    Union[str, Any]								=			get_args()
  set_seed(args.seed						)

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

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

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

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

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

  lowerCAmelCase_       :    str								=			train_test_validation.map(
      lowerCAmelCase__   ,       batched=lowerCAmelCase__   ,       remove_columns=train_test_validation['train'].column_names   ,       )
  lowerCAmelCase_       :    Tuple								=			DataCollatorWithPadding(tokenizer=lowerCAmelCase__						)

  lowerCAmelCase_       :    List[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'   ,       )

  lowerCAmelCase_       :    List[Any]								=			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"""







from diffusers.utils.testing_utils import require_onnxruntime







@require_onnxruntime
class 				UpperCamelCase__       :




      """simple docstring"""




      pass





