




"""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  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		Dict								=  argparse.ArgumentParser()
  parser.add_argument("""--model_ckpt"""		,       type=snake_case__		,       default="""microsoft/unixcoder-base-nine"""   )
  parser.add_argument("""--num_epochs"""		,       type=snake_case__		,       default=5   )
  parser.add_argument("""--batch_size"""		,       type=snake_case__		,       default=6   )
  parser.add_argument("""--gradient_accumulation_steps"""		,       type=snake_case__		,       default=1   )
  parser.add_argument("""--freeze"""		,       type=snake_case__		,       default=snake_case__   )
  parser.add_argument("""--learning_rate"""		,       type=snake_case__		,       default=5e-4   )
  parser.add_argument("""--seed"""		,       type=snake_case__		,       default=0   )
  parser.add_argument("""--lr_scheduler_type"""		,       type=snake_case__		,       default="""cosine"""   )
  parser.add_argument("""--num_warmup_steps"""		,       type=snake_case__		,       default=10   )
  parser.add_argument("""--weight_decay"""		,       type=snake_case__		,       default=0.01   )
  parser.add_argument("""--output_dir"""		,       type=snake_case__		,       default="""./results"""   )
  return parser.parse_args()


A_      =    load('''accuracy''')


def  UpperCAmelCase__	(snake_case__							: Dict   ):
  """simple docstring"""

  _snake_case		, _snake_case							:		Optional[int]								=  eval_pred
  _snake_case							:		Tuple								=  np.argmax(snake_case__		,       axis=1   )
  return metric.compute(predictions=snake_case__		,       references=snake_case__   )






class  lowercase(					__a			):







 '''simple docstring'''





 def __init__(    self:		List[Any],				a_:		Optional[int]		):




   '''simple docstring'''



   super().__init__()
   _snake_case							:		Dict								=  trainer


 def 			UpperCamelCase_       (    self:		int,				a_:		Optional[Any],				a_:		Union[str, Any],				a_:		Any,				**a_:		Tuple		):




   '''simple docstring'''



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







def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		List[Any]								=  get_args()
  set_seed(args.seed   )

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

  print("""Loading tokenizer and model"""   )
  _snake_case							:		Union[str, Any]								=  AutoTokenizer.from_pretrained(args.model_ckpt   )
  _snake_case							:		Optional[Any]								=  tokenizer.eos_token
  _snake_case							:		List[str]								=  AutoModelForSequenceClassification.from_pretrained(args.model_ckpt		,       num_labels=7   )
  _snake_case							:		str								=  model.config.eos_token_id

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

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

  def tokenize(snake_case__							: Union[str, Any]   ):
    _snake_case							:		Dict								=  tokenizer(example["""src"""]		,       truncation=snake_case__		,       max_length=10_24   )
    _snake_case							:		List[Any]								=  labels.straint(example["""complexity"""]   )
    return {
        "input_ids": inputs["input_ids"],
        "attention_mask": inputs["attention_mask"],
        "label": label,
    }

  _snake_case							:		Union[str, Any]								=  train_test_validation.map(
      snake_case__		,       batched=snake_case__		,       remove_columns=train_test_validation["""train"""].column_names		,       )
  _snake_case							:		List[str]								=  DataCollatorWithPadding(tokenizer=snake_case__   )

  _snake_case							:		List[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"""		,       )

  _snake_case							:		int								=  Trainer(
      model=snake_case__		,       args=snake_case__		,       train_dataset=tokenized_datasets["""train"""]		,       eval_dataset=tokenized_datasets["""valid"""]		,       tokenizer=snake_case__		,       data_collator=snake_case__		,       compute_metrics=snake_case__		,       )

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


if __name__ == "__main__":
     main()








"""simple docstring"""


from typing import TYPE_CHECKING

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


A_      =    {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    ['''MaskFormerFeatureExtractor''']
     A_      =    ['''MaskFormerImageProcessor''']


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    [
         '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MaskFormerForInstanceSegmentation''',
         '''MaskFormerModel''',
         '''MaskFormerPreTrainedModel''',
     ]
     A_      =    [
         '''MaskFormerSwinBackbone''',
         '''MaskFormerSwinModel''',
         '''MaskFormerSwinPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
     from .configuration_maskformer_swin import MaskFormerSwinConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_maskformer import MaskFormerFeatureExtractor
          from .image_processing_maskformer import MaskFormerImageProcessor
     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_maskformer import (
              MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              MaskFormerForInstanceSegmentation,
              MaskFormerModel,
              MaskFormerPreTrainedModel,
          )
          from .modeling_maskformer_swin import (
              MaskFormerSwinBackbone,
              MaskFormerSwinModel,
              MaskFormerSwinPreTrainedModel,
          )


else:
     import sys

     A_      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)


