



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      __lowercase(						):


      """simple docstring"""



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


a_    :  Union[str, Any]						  =		load('accuracy')

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



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





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self					,      __a     ):






           '''simple docstring'''



           super().__init__()
           lowerCamelCase             =		trainer







     def     _a   (self					,      __a					,      __a					,      __a					,      **__a     ):






           '''simple docstring'''



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



def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		get_args()
      set_seed(args.seed			)

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

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

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

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

      def tokenize(UpperCAmelCase__			):
            lowerCamelCase             =		tokenizer(example["src"] , truncation=UpperCAmelCase__ , max_length=1024			)
            lowerCamelCase             =		labels.straint(example["complexity"]			)
            return {
                "input_ids": inputs["input_ids"],
                "attention_mask": inputs["attention_mask"],
                "label": label,
            }

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

      lowerCamelCase             =		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" , )

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

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


if __name__ == "__main__":
     main()




from typing import TYPE_CHECKING

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


a_    :  Tuple						  =		{'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :  Union[str, Any]						  =		[
         'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
         'UniSpeechForCTC',
         'UniSpeechForPreTraining',
         'UniSpeechForSequenceClassification',
         'UniSpeechModel',
         'UniSpeechPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_unispeech import (
              UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
              UniSpeechForCTC,
              UniSpeechForPreTraining,
              UniSpeechForSequenceClassification,
              UniSpeechModel,
              UniSpeechPreTrainedModel,
          )

else:
     import sys

     a_    :  Optional[Any]						  =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)