


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 __UpperCAmelCase       (		)						->					List[str]:



       '''simple docstring'''





       UpperCAmelCase__										=     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=1_0			)
       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 __UpperCAmelCase       (		__A			)						->					List[str]:



       '''simple docstring'''





       UpperCAmelCase__										=     eval_pred
       UpperCAmelCase__										=     np.argmax(_lowercase ,						axis=1			)
       return metric.compute(predictions=_lowercase ,						references=_lowercase			)


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					str		,						_lowercase      :					int ):




         """simple docstring"""







         super().__init__()
         UpperCAmelCase__										=     trainer






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					List[Any]		,						_lowercase      :					Optional[Any]		,						_lowercase      :					int		,						**_lowercase      :					Any ):




         """simple docstring"""







         if control.should_evaluate:
                UpperCAmelCase__										=     deepcopy(UpperCamelCase_ )
                self._trainer.evaluate(eval_dataset=self._trainer.train_dataset		,						metric_key_prefix="train" )
                return control_copy





def __UpperCAmelCase       (		)						->					Optional[Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     get_args()
       set_seed(args.seed			)

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

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

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

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

       def tokenize(__A			):
              UpperCAmelCase__										=     tokenizer(example["src"] ,						truncation=_lowercase ,						max_length=1_0_2_4			)
              UpperCAmelCase__										=     labels.straint(example["complexity"]			)
              return {
                  "input_ids": inputs["input_ids"],
                  "attention_mask": inputs["attention_mask"],
                  "label": label,
              }

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

       UpperCAmelCase__										=     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__										=     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()









from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A      = {"configuration_mmbt": ["MMBTConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A      = ["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]


if TYPE_CHECKING:
    from .configuration_mmbt import MMBTConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
    import sys

    A      = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





