




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__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[Any]  =     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=0.01 )
     parser.add_argument('''--output_dir'''					,		type=_lowercase					,		default='''./results''' )
     return parser.parse_args()


__a							     =					load('accuracy')







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_,       UpperCAmelCase_     :				List[str]  =     eval_pred
     UpperCAmelCase_     :				str  =     np.argmax(_lowercase					,		axis=1 )
     return metric.compute(predictions=_lowercase					,		references=_lowercase )

class     __a(	_a  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE    )					->						None:
         super().__init__()
         UpperCAmelCase_     :				List[Any]  =     trainer





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						Any:
         if control.should_evaluate:
              UpperCAmelCase_     :				Union[str, Any]  =     deepcopy(_SCREAMING_SNAKE_CASE    )
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset  ,metric_key_prefix='''train'''    )
              return control_copy







def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				str  =     get_args()
     set_seed(args.seed )

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

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

     if args.freeze:
          for param in model.roberta.parameters():
               UpperCAmelCase_     :				Optional[int]  =     False

     UpperCAmelCase_     :				List[Any]  =     ClassLabel(num_classes=7					,		names=list(set(train_test_validation['''train''']['''complexity'''] ) ) )

     def tokenize(_lowercase ):
          UpperCAmelCase_     :				Dict  =     tokenizer(example['''src''']					,		truncation=_lowercase					,		max_length=1024 )
          UpperCAmelCase_     :				Tuple  =     labels.straint(example['''complexity'''] )
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

     UpperCAmelCase_     :				Dict  =     train_test_validation.map(
         _lowercase					,		batched=_lowercase					,		remove_columns=train_test_validation['''train'''].column_names					,		)
     UpperCAmelCase_     :				List[Any]  =     DataCollatorWithPadding(tokenizer=_lowercase )

     UpperCAmelCase_     :				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'''					,		)

     UpperCAmelCase_     :				int  =     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()





# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__a							     =					'2.13.1'

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('3.7'):
     raise ImportWarning(
         'To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'
     )

if version.parse(pyarrow.__version__).major < 8:
     raise ImportWarning(
         'To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'
         'If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'
     )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__a							     =					concatenate_datasets
__a							     =					DownloadConfig
__a							     =					DownloadManager
__a							     =					DownloadMode
__a							     =					DownloadConfig
__a							     =					DownloadMode
__a							     =					DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager