"""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   (	)	->Union[str, Any]:
  a__:     Optional[int]										=	argparse.ArgumentParser()
  parser.add_argument('--model_ckpt'		,     type=A__		,     default='microsoft/unixcoder-base-nine'   )
  parser.add_argument('--num_epochs'		,     type=A__		,     default=5   )
  parser.add_argument('--batch_size'		,     type=A__		,     default=6   )
  parser.add_argument('--gradient_accumulation_steps'		,     type=A__		,     default=1   )
  parser.add_argument('--freeze'		,     type=A__		,     default=A__   )
  parser.add_argument('--learning_rate'		,     type=A__		,     default=5e-4   )
  parser.add_argument('--seed'		,     type=A__		,     default=0   )
  parser.add_argument('--lr_scheduler_type'		,     type=A__		,     default='cosine'   )
  parser.add_argument('--num_warmup_steps'		,     type=A__		,     default=10   )
  parser.add_argument('--weight_decay'		,     type=A__		,     default=0.01   )
  parser.add_argument('--output_dir'		,     type=A__		,     default='./results'   )
  return parser.parse_args()


lowercase__					       =	load('accuracy')







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Dict:
  a__							,    a__:     Dict										=	eval_pred
  a__:     Optional[int]										=	np.argmax(A__		,     axis=1   )
  return metric.compute(predictions=A__		,     references=A__   )




class    __snake_case       (     lowerCamelCase__				):



      def __init__(						self    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        super().__init__()
        a__:     Any										=	trainer



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			**lowercase)	->			Optional[Any]:





        '''simple docstring'''





        if control.should_evaluate:
          a__:     int										=	deepcopy(lowercase__)
          self._trainer.evaluate(eval_dataset=self._trainer.train_dataset    ,			metric_key_prefix='train')
          return control_copy







def 						__a   (	)	->Tuple:
  a__:     Tuple										=	get_args()
  set_seed(args.seed   )

  a__:     Union[str, Any]										=	load_dataset('codeparrot/codecomplex'		,     split='train'   )
  a__:     Optional[Any]										=	dataset.train_test_split(test_size=0.2   )
  a__:     Union[str, Any]										=	train_test['test'].train_test_split(test_size=0.5   )
  a__:     Any										=	DatasetDict(
      {
          'train': train_test['train'],
          'test': test_validation['train'],
          'valid': test_validation['test'],
      }   )

  print('Loading tokenizer and model'   )
  a__:     Union[str, Any]										=	AutoTokenizer.from_pretrained(args.model_ckpt   )
  a__:     Tuple										=	tokenizer.eos_token
  a__:     List[Any]										=	AutoModelForSequenceClassification.from_pretrained(args.model_ckpt		,     num_labels=7   )
  a__:     Dict										=	model.config.eos_token_id

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

  a__:     Optional[int]										=	ClassLabel(num_classes=7		,     names=list(set(train_test_validation['train']['complexity']   )   )   )

  def tokenize(_SCREAMING_SNAKE_CASE   ):
    a__:     Dict										=	tokenizer(example['src']		,     truncation=A__		,     max_length=1024   )
    a__:     Optional[Any]										=	labels.straint(example['complexity']   )
    return {
        "input_ids": inputs["input_ids"],
        "attention_mask": inputs["attention_mask"],
        "label": label,
    }

  a__:     Optional[Any]										=	train_test_validation.map(
      A__		,     batched=A__		,     remove_columns=train_test_validation['train'].column_names		,     )
  a__:     Optional[Any]										=	DataCollatorWithPadding(tokenizer=A__   )

  a__:     Tuple										=	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'		,     )

  a__:     Dict										=	Trainer(
      model=A__		,     args=A__		,     train_dataset=tokenized_datasets['train']		,     eval_dataset=tokenized_datasets['valid']		,     tokenizer=A__		,     data_collator=A__		,     compute_metrics=A__		,     )

  print('Training...'   )
  trainer.add_callback(CustomCallback(A__   )   )
  trainer.train()


if __name__ == "__main__":
 main()





'''simple docstring'''






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase


def 		_A     (    A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 if isinstance(A__     ,				A__      ):
  __lowercase							  = np.full((len(A__      ), sequence_length, 2)     ,				A__      )
 else:
  __lowercase							  = np.full((len(A__      ), sequence_length)     ,				A__      )

 for i, tensor in enumerate(A__      ):
  if padding_side == "right":
   if isinstance(A__     ,				A__      ):
    __lowercase							  = tensor[:sequence_length]
   else:
    __lowercase							  = tensor[:sequence_length]
  else:
   if isinstance(A__     ,				A__      ):
    __lowercase							  = tensor[:sequence_length]
   else:
    __lowercase							  = tensor[:sequence_length]

 return out_tensor.tolist()


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = ord(A__      )
 if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
  return True
 __lowercase							  = unicodedata.category(A__      )
 if cat.startswith('''P'''      ):
  return True
 return False






@dataclass
class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  PreTrainedTokenizerBase
  SCREAMING_SNAKE_CASE    :  Union[bool, str, PaddingStrategy]							=			True
  SCREAMING_SNAKE_CASE    :  Optional[int]							=			None
  SCREAMING_SNAKE_CASE    :  Optional[int]							=			None
  SCREAMING_SNAKE_CASE    :  int							=			-1_0_0
  SCREAMING_SNAKE_CASE    :  str							=			"pt"






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     List[str]			):
   import torch

   __lowercase							  = '''label''' if '''label''' in features[0].keys() else '''labels'''
   __lowercase							  = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
   __lowercase							  = self.tokenizer.pad(
       lowercase__					,padding=self.padding					,max_length=self.max_length					,pad_to_multiple_of=self.pad_to_multiple_of					,return_tensors='''pt''' if labels is None else None					,)

   if labels is None:
    return batch

   __lowercase							  = torch.tensor(batch['''entity_ids''']			).shape[1]
   __lowercase							  = self.tokenizer.padding_side
   if padding_side == "right":
    __lowercase							  = [
        list(lowercase__			) + [self.label_pad_token_id] * (sequence_length - len(lowercase__			)) for label in labels
    ]
   else:
    __lowercase							  = [
        [self.label_pad_token_id] * (sequence_length - len(lowercase__			)) + list(lowercase__			) for label in labels
    ]

   __lowercase							  = [feature['''ner_tags'''] for feature in features]
   __lowercase							  = padding_tensor(lowercase__					,-1					,lowercase__					,lowercase__			)
   __lowercase							  = [feature['''original_entity_spans'''] for feature in features]
   __lowercase							  = padding_tensor(lowercase__					,(-1, -1)					,lowercase__					,lowercase__			)
   __lowercase							  = {k: torch.tensor(lowercase__					,dtype=torch.intaa			) for k, v in batch.items()}

   return batch

