


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  (							)->Any:
   '''simple docstring'''






   snake_case_    			=    argparse.ArgumentParser()
   parser.add_argument("--model_ckpt"						,  type=lowercase__						,  default="microsoft/unixcoder-base-nine"							)
   parser.add_argument("--num_epochs"						,  type=lowercase__						,  default=5							)
   parser.add_argument("--batch_size"						,  type=lowercase__						,  default=6							)
   parser.add_argument("--gradient_accumulation_steps"						,  type=lowercase__						,  default=1							)
   parser.add_argument("--freeze"						,  type=lowercase__						,  default=lowercase__							)
   parser.add_argument("--learning_rate"						,  type=lowercase__						,  default=5e-4							)
   parser.add_argument("--seed"						,  type=lowercase__						,  default=0							)
   parser.add_argument("--lr_scheduler_type"						,  type=lowercase__						,  default="cosine"							)
   parser.add_argument("--num_warmup_steps"						,  type=lowercase__						,  default=10							)
   parser.add_argument("--weight_decay"						,  type=lowercase__						,  default=0.0_1							)
   parser.add_argument("--output_dir"						,  type=lowercase__						,  default="./results"							)
   return parser.parse_args()


SCREAMING_SNAKE_CASE		:List[Any]     						=   load('''accuracy''')



def 	_lowerCAmelCase  (							lowerCAmelCase_							:Any							)->Any:
   '''simple docstring'''






   snake_case_    			=    eval_pred
   snake_case_    			=    np.argmax(lowercase__						,  axis=1							)
   return metric.compute(predictions=lowercase__						,  references=lowercase__							)


class 			__lowerCAmelCase   (       A__    ):






  """simple docstring"""





  def __init__(   self				:				Any			, _lowerCAmelCase				:				Union[str, Any]     )      ->       None:




     """simple docstring"""
     super().__init__()
     snake_case_    			=    trainer



  def     lowerCAmelCase__     (   self				:				Dict			, _lowerCAmelCase				:				Optional[int]			, _lowerCAmelCase				:				Dict			, _lowerCAmelCase				:				List[str]			, **_lowerCAmelCase				:				int     )      ->       Optional[int]:




     """simple docstring"""
     if control.should_evaluate:
        snake_case_    			=    deepcopy(lowerCAmelCase__     )
        self._trainer.evaluate(eval_dataset=self._trainer.train_dataset			, metric_key_prefix="train"     )
        return control_copy






def 	_lowerCAmelCase  (							)->List[Any]:
   '''simple docstring'''






   snake_case_    			=    get_args()
   set_seed(args.seed							)

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

   print("Loading tokenizer and model"							)
   snake_case_    			=    AutoTokenizer.from_pretrained(args.model_ckpt							)
   snake_case_    			=    tokenizer.eos_token
   snake_case_    			=    AutoModelForSequenceClassification.from_pretrained(args.model_ckpt						,  num_labels=7							)
   snake_case_    			=    model.config.eos_token_id

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

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

   def tokenize(lowerCAmelCase_							:Dict							):
      snake_case_    			=    tokenizer(example["src"]						,  truncation=lowercase__						,  max_length=1_024							)
      snake_case_    			=    labels.straint(example["complexity"]							)
      return {
          "input_ids": inputs["input_ids"],
          "attention_mask": inputs["attention_mask"],
          "label": label,
      }

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

   snake_case_    			=    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=0.0_1						,  metric_for_best_model="accuracy"						,  run_name="complexity-java"						,  report_to="wandb"						,  )

   snake_case_    			=    Trainer(
       model=lowercase__						,  args=lowercase__						,  train_dataset=tokenized_datasets["train"]						,  eval_dataset=tokenized_datasets["valid"]						,  tokenizer=lowercase__						,  data_collator=lowercase__						,  compute_metrics=lowercase__						,  )

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


if __name__ == "__main__":
   main()








import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version


def        _UpperCamelCase							(      *lowercase__    ,       lowercase__ = None    ,       lowercase__=True    ,       lowercase__=2      ):
						from .. import __version__

						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		take_from
						__SCREAMING_SNAKE_CASE						:     List[str]					=		()
						if not isinstance(args[0]    ,       lowercase__      ):
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		(args,)

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

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

												if warning is not None:
																		__SCREAMING_SNAKE_CASE						:     Any					=		warning + ''' ''' if standard_warn else ''''''
																		warnings.warn(warning + message    ,       lowercase__    ,       stacklevel=lowercase__      )

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

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