


'''simple docstring'''



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      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Union[str, 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()


__UpperCAmelCase      :str			    =       load("accuracy")




def      _a     (						_lowercase :     Union[str, Any]  ):





     '''simple docstring'''






     __UpperCAmelCase							,       __UpperCAmelCase     :			Dict  							=      eval_pred
     __UpperCAmelCase     :			Any  							=      np.argmax(_lowercase      , axis=1  )
     return metric.compute(predictions=_lowercase      , references=_lowercase  )



class      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		Union[str, Any]							,			snake_case					:		str      )						->							None:
          super().__init__()
          __UpperCAmelCase     :			Optional[Any]  							=      trainer






     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		Tuple							,			snake_case					:		List[str]							,			snake_case					:		Optional[int]							,			**snake_case					:		Optional[int]      )						->							int:
          if control.should_evaluate:
               __UpperCAmelCase     :			Union[str, Any]  							=      deepcopy(snake_case      )
               self._trainer.evaluate(eval_dataset=self._trainer.train_dataset							,			metric_key_prefix='''train'''      )
               return control_copy




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[int]  							=      get_args()
     set_seed(args.seed  )

     __UpperCAmelCase     :			Dict  							=      load_dataset('''codeparrot/codecomplex'''      , split='''train'''  )
     __UpperCAmelCase     :			List[Any]  							=      dataset.train_test_split(test_size=0.2  )
     __UpperCAmelCase     :			List[str]  							=      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     :			List[str]  							=      AutoTokenizer.from_pretrained(args.model_ckpt  )
     __UpperCAmelCase     :			str  							=      tokenizer.eos_token
     __UpperCAmelCase     :			Dict  							=      AutoModelForSequenceClassification.from_pretrained(args.model_ckpt      , num_labels=7  )
     __UpperCAmelCase     :			List[Any]  							=      model.config.eos_token_id

     if args.freeze:
          for param in model.roberta.parameters():
               __UpperCAmelCase     :			List[str]  							=      False

     __UpperCAmelCase     :			str  							=      ClassLabel(num_classes=7      , names=list(set(train_test_validation['''train''']['''complexity''']  )  )  )

     def tokenize(_lowercase :     Union[str, Any]  ):
          __UpperCAmelCase     :			Optional[int]  							=      tokenizer(example['''src''']      , truncation=_lowercase      , max_length=1024  )
          __UpperCAmelCase     :			Union[str, Any]  							=      labels.straint(example['''complexity''']  )
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

     __UpperCAmelCase     :			Tuple  							=      train_test_validation.map(
         _lowercase      , batched=_lowercase      , remove_columns=train_test_validation['''train'''].column_names      , )
     __UpperCAmelCase     :			Optional[Any]  							=      DataCollatorWithPadding(tokenizer=_lowercase  )

     __UpperCAmelCase     :			Optional[Any]  							=      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     :			Tuple  							=      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()



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase      :List[Any]			    =       {
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[Any]			    =       ["LxmertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[Any]			    =       [
          "LxmertEncoder",
          "LxmertForPreTraining",
          "LxmertForQuestionAnswering",
          "LxmertModel",
          "LxmertPreTrainedModel",
          "LxmertVisualFeatureEncoder",
          "LxmertXLayer",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[str]			    =       [
          "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFLxmertForPreTraining",
          "TFLxmertMainLayer",
          "TFLxmertModel",
          "TFLxmertPreTrainedModel",
          "TFLxmertVisualFeatureEncoder",
      ]


if TYPE_CHECKING:
      from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
      from .tokenization_lxmert import LxmertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_lxmert_fast import LxmertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_lxmert import (
                LxmertEncoder,
                LxmertForPreTraining,
                LxmertForQuestionAnswering,
                LxmertModel,
                LxmertPreTrainedModel,
                LxmertVisualFeatureEncoder,
                LxmertXLayer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_lxmert import (
                TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLxmertForPreTraining,
                TFLxmertMainLayer,
                TFLxmertModel,
                TFLxmertPreTrainedModel,
                TFLxmertVisualFeatureEncoder,
            )

else:
      import sys

      __UpperCAmelCase      :int			    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)