




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()






"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def       _snake_case						(						*UpperCamelCase						:		str			,		UpperCamelCase						:		Optional[Union[Dict, Any]] = None			,		UpperCamelCase						:		Tuple=True			,		UpperCamelCase						:		Optional[int]=2						):
 from .. import __version__

 UpperCAmelCase	:					Tuple	=	take_from
 UpperCAmelCase	:					Optional[Any]	=	()
 if not isinstance(args[0]			,		UpperCamelCase						):
  UpperCAmelCase	:					List[str]	=	(args,)

 for attribute, version_name, message in args:
  if version.parse(version.parse(UpperCamelCase						).base_version						) >= version.parse(UpperCamelCase						):
   raise ValueError(
       F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
       F" version {__version__} is >= {version_name}"						)

  UpperCAmelCase	:					Optional[int]	=	None
  if isinstance(UpperCamelCase			,		UpperCamelCase						) and attribute in deprecated_kwargs:
   values += (deprecated_kwargs.pop(UpperCamelCase						),)
   UpperCAmelCase	:					List[str]	=	F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
  elif hasattr(UpperCamelCase			,		UpperCamelCase						):
   values += (getattr(UpperCamelCase			,		UpperCamelCase						),)
   UpperCAmelCase	:					List[Any]	=	F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
  elif deprecated_kwargs is None:
   UpperCAmelCase	:					int	=	F"`{attribute}` is deprecated and will be removed in version {version_name}."

  if warning is not None:
   UpperCAmelCase	:					Optional[Any]	=	warning + """ """ if standard_warn else """"""
   warnings.warn(warning + message			,		UpperCamelCase			,		stacklevel=UpperCamelCase						)

 if isinstance(UpperCamelCase			,		UpperCamelCase						) and len(UpperCamelCase						) > 0:
  UpperCAmelCase	:					Optional[int]	=	inspect.getouterframes(inspect.currentframe()						)[1]
  UpperCAmelCase	:					Union[str, Any]	=	call_frame.filename
  UpperCAmelCase	:					List[Any]	=	call_frame.lineno
  UpperCAmelCase	:					List[str]	=	call_frame.function
  UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	next(iter(deprecated_kwargs.items()						)						)
  raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"						)

 if len(UpperCamelCase						) == 0:
  return
 elif len(UpperCamelCase						) == 1:
  return values[0]
 return values
