





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


__UpperCAmelCase       :   Tuple				       =							load("accuracy")







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







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Optional[Any]							,				A :    str		):
         super().__init__()
         __snake_case:						List[Any]   =						trainer






       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    Any							,				A :    List[Any]							,				A :    List[Any]							,				**A :    List[Any]		):
         if control.should_evaluate:
           __snake_case:						List[str]   =						deepcopy(A		)
           self._trainer.evaluate(eval_dataset=self._trainer.train_dataset							,				metric_key_prefix="""train"""		)
           return control_copy







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

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

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

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

  __snake_case:						List[str]   =						ClassLabel(num_classes=7					,					names=list(set(train_test_validation["""train"""]["""complexity"""])))

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

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

  __snake_case:						Dict   =						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"""					,					)

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

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


if __name__ == "__main__":
      main()













__UpperCAmelCase       :   int				       =							[
    "Audio",
    "Array2D",
    "Array3D",
    "Array4D",
    "Array5D",
    "ClassLabel",
    "Features",
    "Sequence",
    "Value",
    "Image",
    "Translation",
    "TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages






