




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


_snake_case										=       load("accuracy")







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







class 						lowercase							(   UpperCamelCase__		):

     def __init__(  self		,     _a				)						->							None:
           super().__init__()
           _A							:     Dict			=						trainer



     def 					a__	(  self		,     _a		,     _a		,     _a		,     **_a				)						->							str:
           if control.should_evaluate:
                 _A							:     Tuple			=						deepcopy(_a				)
                 self._trainer.evaluate(eval_dataset=self._trainer.train_dataset		,     metric_key_prefix="""train"""				)
                 return control_copy







def 				lowerCAmelCase_	(     ):
      _A							:     List[Any]			=						get_args()
      set_seed(args.seed     )

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

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

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

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

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

      _A							:     Optional[Any]			=						train_test_validation.map(
          snake_case_,batched=snake_case_,remove_columns=train_test_validation["""train"""].column_names,)
      _A							:     List[str]			=						DataCollatorWithPadding(tokenizer=snake_case_     )

      _A							:     int			=						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							:     Dict			=						Trainer(
          model=snake_case_,args=snake_case_,train_dataset=tokenized_datasets["""train"""],eval_dataset=tokenized_datasets["""valid"""],tokenizer=snake_case_,data_collator=snake_case_,compute_metrics=snake_case_,)

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


if __name__ == "__main__":
  main()







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )
      if n == 0:
            return 0
      _A							:     Tuple			=						float("""-inf"""     )
      for i in range(1,n + 1     ):
            _A							:     str			=						max(
                snake_case_,prices[i - 1] + naive_cut_rod_recursive(n - i,snake_case_     )     )

      return max_revue







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )
      _A							:     Dict			=						[float("""-inf"""     ) for _ in range(n + 1     )]
      return _top_down_cut_rod_recursive(snake_case_,snake_case_,snake_case_     )







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      if max_rev[n] >= 0:
            return max_rev[n]
      elif n == 0:
            return 0
      else:
            _A							:     List[str]			=						float("""-inf"""     )
            for i in range(1,n + 1     ):
                  _A							:     Optional[Any]			=						max(
                      snake_case_,prices[i - 1] + _top_down_cut_rod_recursive(n - i,snake_case_,snake_case_     ),)

            _A							:     Tuple			=						max_revenue

      return max_rev[n]







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      _enforce_args(snake_case_,snake_case_     )

      # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
      # length 0.
      _A							:     List[Any]			=						[float("""-inf"""     ) for _ in range(n + 1     )]
      _A							:     Any			=						0

      for i in range(1,n + 1     ):
            _A							:     Optional[Any]			=						max_rev[i]
            for j in range(1,i + 1     ):
                  _A							:     int			=						max(snake_case_,prices[j - 1] + max_rev[i - j]     )

            _A							:     int			=						max_revenue_i

      return max_rev[n]







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      if n < 0:
            _A							:     Optional[Any]			=						f'''n must be greater than or equal to 0. Got n = {n}'''
            raise ValueError(snake_case_     )

      if n > len(snake_case_     ):
            _A							:     Any			=						(
                """Each integral piece of rod must have a corresponding price. """
                f'''Got n = {n} but length of prices = {len(snake_case_     )}'''
            )
            raise ValueError(snake_case_     )







def 				lowerCAmelCase_	(     ):
      _A							:     Tuple			=						[6, 10, 12, 15, 20, 23]
      _A							:     List[Any]			=						len(snake_case_     )

      # the best revenue comes from cutting the rod into 6 pieces, each
      # of length 1 resulting in a revenue of 6 * 6 = 36.
      _A							:     Any			=						36

      _A							:     List[Any]			=						top_down_cut_rod(snake_case_,snake_case_     )
      _A							:     List[Any]			=						bottom_up_cut_rod(snake_case_,snake_case_     )
      _A							:     Dict			=						naive_cut_rod_recursive(snake_case_,snake_case_     )

      assert expected_max_revenue == max_rev_top_down
      assert max_rev_top_down == max_rev_bottom_up
      assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
  main()

