'''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        __lowercase						(      )				->					List[str]:
     """simple docstring"""




     __a       =   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()


SCREAMING_SNAKE_CASE_      =			load('accuracy')




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Optional[Any]:
     """simple docstring"""




     __a ,						__a       =   eval_pred
     __a       =   np.argmax(__SCREAMING_SNAKE_CASE				,  axis=1		)
     return metric.compute(predictions=__SCREAMING_SNAKE_CASE				,  references=__SCREAMING_SNAKE_CASE		)


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  def __init__(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Tuple						):





       '''simple docstring'''

       super().__init__()
       __a       =   trainer


  def __a						(					self     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							int				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							**SCREAMING_SNAKE_CASE__     :							Union[str, Any]						):





       '''simple docstring'''

       if control.should_evaluate:
            __a       =   deepcopy(SCREAMING_SNAKE_CASE__						)
            self._trainer.evaluate(eval_dataset=self._trainer.train_dataset				,							metric_key_prefix="""train"""						)
            return control_copy




def        __lowercase						(      )				->					List[Any]:
     """simple docstring"""




     __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(__SCREAMING_SNAKE_CASE		):
          __a       =   tokenizer(example["""src"""]				,  truncation=__SCREAMING_SNAKE_CASE				,  max_length=1024		)
          __a       =   labels.straint(example["""complexity"""]		)
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

     __a       =   train_test_validation.map(
         __SCREAMING_SNAKE_CASE				,  batched=__SCREAMING_SNAKE_CASE				,  remove_columns=train_test_validation["""train"""].column_names				,  )
     __a       =   DataCollatorWithPadding(tokenizer=__SCREAMING_SNAKE_CASE		)

     __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=__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()










'''simple docstring'''







from __future__ import annotations




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  )				->					tuple[str, float]:
     """simple docstring"""




     if (stress, tangential_force, area).count(0		) != 1:
          raise ValueError("""You cannot supply more or less than 2 values"""		)
     elif stress < 0:
          raise ValueError("""Stress cannot be negative"""		)
     elif tangential_force < 0:
          raise ValueError("""Tangential Force cannot be negative"""		)
     elif area < 0:
          raise ValueError("""Area cannot be negative"""		)
     elif stress == 0:
          return (
              "stress",
              tangential_force / area,
          )
     elif tangential_force == 0:
          return (
              "tangential_force",
              stress * area,
          )
     else:
          return (
              "area",
              tangential_force / stress,
          )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






