




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      _SCREAMING_SNAKE_CASE     (				):


      '''simple docstring'''



      lowerCamelCase_							=       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()


lowerCamelCase							:						List[Any]					       =  load("accuracy")

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Optional[Any]	):


      '''simple docstring'''



      lowerCamelCase_	,							lowerCamelCase_							=       eval_pred
      lowerCamelCase_							=       np.argmax(lowercase      ,				axis=1	)
      return metric.compute(predictions=lowercase      ,				references=lowercase	)







class     A(		UpperCamelCase  ):



 '''simple docstring'''


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

       """simple docstring"""






       super().__init__()
       lowerCamelCase_							=       trainer





 def 							a__					(							self   :    Any		,				A_   :    Optional[Any]		,				A_   :    Tuple		,				A_   :    Union[str, Any]		,				**A_   :    Tuple    )  ->      List[Any]:

       """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      _SCREAMING_SNAKE_CASE     (				):


      '''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(lowercase      :			Union[str, Any]	):
            lowerCamelCase_							=       tokenizer(example['src']      ,				truncation=lowercase      ,				max_length=10_24	)
            lowerCamelCase_							=       labels.straint(example['complexity']	)
            return {
                "input_ids": inputs["input_ids"],
                "attention_mask": inputs["attention_mask"],
                "label": label,
            }

      lowerCamelCase_							=       train_test_validation.map(
          lowercase      ,				batched=lowercase      ,				remove_columns=train_test_validation['train'].column_names      ,				)
      lowerCamelCase_							=       DataCollatorWithPadding(tokenizer=lowercase	)

      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=0.01      ,				metric_for_best_model='accuracy'      ,				run_name='complexity-java'      ,				report_to='wandb'      ,				)

      lowerCamelCase_							=       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 copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__           =			logging.get_logger(__name__)

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[Any]      =							"""encoder-decoder"""
							snake_case  :   Optional[int]      =							True






							def __init__(    self	,			**__lowerCAmelCase	):
														super().__init__(**__lowerCAmelCase	)
														assert (
														    "encoder" in kwargs and "decoder" in kwargs
														), "Config has to be initialized with encoder and decoder config"
														UpperCamelCase__					  =      kwargs.pop("""encoder"""	)
														UpperCamelCase__					  =      encoder_config.pop("""model_type"""	)
														UpperCamelCase__					  =      kwargs.pop("""decoder"""	)
														UpperCamelCase__					  =      decoder_config.pop("""model_type"""	)

														from ..auto.configuration_auto import AutoConfig

														UpperCamelCase__					  =      AutoConfig.for_model(__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      AutoConfig.for_model(__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      True






							@classmethod
							def 					_lowerCamelCase		(    cls	,			__lowerCAmelCase	,			__lowerCAmelCase	,			**__lowerCAmelCase	):
														logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""	)
														UpperCamelCase__					  =      True
														UpperCamelCase__					  =      True

														return cls(encoder=encoder_config.to_dict()	,			decoder=decoder_config.to_dict()	,			**__lowerCAmelCase	)







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      copy.deepcopy(self.__dict__	)
														UpperCamelCase__					  =      self.encoder.to_dict()
														UpperCamelCase__					  =      self.decoder.to_dict()
														UpperCamelCase__					  =      self.__class__.model_type
														return output




