


'''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			()				->					int:




     '''simple docstring'''



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


a_   :      int				    =      load("accuracy")






def 		_A			(lowerCAmelCase__					:str						)				->					List[Any]:




     '''simple docstring'''



     _a ,				_a      =							eval_pred
     _a      =							np.argmax(lowerCAmelCase__			,  axis=1						)
     return metric.compute(predictions=lowerCAmelCase__			,  references=lowerCAmelCase__						)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			__magic_name__  )   ->							None:
      super().__init__()
      _a      =							trainer




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			**__magic_name__  )   ->							List[Any]:
      if control.should_evaluate:
           _a      =							deepcopy(__magic_name__  )
           self._trainer.evaluate(eval_dataset=self._trainer.train_dataset ,			metric_key_prefix='train'  )
           return control_copy






def 		_A			()				->					Any:




     '''simple docstring'''



     _a      =							get_args()
     set_seed(args.seed						)

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

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

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

     _a      =							ClassLabel(num_classes=7			,  names=list(set(train_test_validation['train']['complexity']						)						)						)

     def tokenize(lowerCAmelCase__					:Optional[Any]						):
          _a      =							tokenizer(example['src']			,  truncation=lowerCAmelCase__			,  max_length=10_24						)
          _a      =							labels.straint(example['complexity']						)
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

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

     _a      =							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'			,  )

     _a      =							Trainer(
         model=lowerCAmelCase__			,  args=lowerCAmelCase__			,  train_dataset=tokenized_datasets['train']			,  eval_dataset=tokenized_datasets['valid']			,  tokenizer=lowerCAmelCase__			,  data_collator=lowerCAmelCase__			,  compute_metrics=lowerCAmelCase__			,  )

     print('Training...'						)
     trainer.add_callback(CustomCallback(lowerCAmelCase__						)						)
     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			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:Any			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:Dict						)				->					Optional[int]:




     '''simple docstring'''



     if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length, 2)			,  lowerCAmelCase__						)
     else:
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length)			,  lowerCAmelCase__						)

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

     return out_tensor.tolist()






def 		_A			(lowerCAmelCase__					:Any						)				->					Union[str, Any]:




     '''simple docstring'''



     _a      =							ord(lowerCAmelCase__						)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
          return True
     _a      =							unicodedata.category(lowerCAmelCase__						)
     if cat.startswith('P'						):
          return True
     return False






@dataclass
class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		-1_0_0
 _lowerCAmelCase	   =		"pt"
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Any:
      import torch

      _a      =							'label' if 'label' in features[0].keys() else 'labels'
      _a      =							[feature[label_name] for feature in features] if label_name in features[0].keys() else None
      _a      =							self.tokenizer.pad(
          __magic_name__ ,			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

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

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

      return batch

