




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()







from __future__ import annotations

lowerCamelCase__ :   Optional[int]					=							[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCamelCase__ :   List[Any]					=							[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)

      for i in range(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for j in range(i + 1			,			__UpperCAmelCase							):
                  if arr[i] < arr[j]:
                        SCREAMING_SNAKE_CASE_							=			arr[j]
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      for i, outer in enumerate(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for inner in arr[i + 1 :]:
                  if outer < inner:
                        SCREAMING_SNAKE_CASE_							=			inner
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			[-1] * arr_size

      for index in reversed(range(__UpperCAmelCase							)							):
            if stack:
                  while stack[-1] <= arr[index]:
                        stack.pop()
                        if not stack:
                              break
            if stack:
                  SCREAMING_SNAKE_CASE_							=			stack[-1]
            stack.append(arr[index]							)
      return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   lowerCamelCase__ :   List[str]					=							(
       'from __main__ import arr, next_greatest_element_slow, '
       'next_greatest_element_fast, next_greatest_element'
   )
   print(
       'next_greatest_element_slow():',
       timeit('next_greatest_element_slow(arr)', setup=setup),
   )
   print(
       'next_greatest_element_fast():',
       timeit('next_greatest_element_fast(arr)', setup=setup),
   )
   print(
       '     next_greatest_element():',
       timeit('next_greatest_element(arr)', setup=setup),
   )