




'''simple docstring'''



import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


UpperCamelCase_    	=				logging.getLogger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				Any        =						'''sequence-classification'''







       def __init__(      self,							A  ):







             '''simple docstring'''







             if type(A  ) == dict:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						Namespace(**A  )
             SCREAMING_SNAKE_CASE   :      str    					=						glue_output_modes[hparams.task]
             SCREAMING_SNAKE_CASE   :      Tuple    					=						glue_tasks_num_labels[hparams.task]

             super().__init__(A,							A,							self.mode  )







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







             return self.model(**A  )







       def        UpperCamelCase_							(      self,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

             if self.config.model_type not in ["distilbert", "bart"]:
                   SCREAMING_SNAKE_CASE   :      str    					=						batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self(**A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						outputs[0]

             SCREAMING_SNAKE_CASE   :      str    					=						self.trainer.lr_schedulers[0]['scheduler']
             SCREAMING_SNAKE_CASE   :      Any    					=						{'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
             return {"loss": loss, "log": tensorboard_logs}







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.hparams
             SCREAMING_SNAKE_CASE   :      Any    					=						processors[args.task]()
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						processor.get_labels()

             for mode in ["train", "dev"]:
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						self._feature_file(A  )
                   if os.path.exists(A  ) and not args.overwrite_cache:
                         logger.info('Loading features from cached file %s',							A  )
                   else:
                         logger.info('Creating features from dataset file at %s',							args.data_dir  )
                         SCREAMING_SNAKE_CASE   :      int    					=						(
                             processor.get_dev_examples(args.data_dir  )
                             if mode == 'dev'
                             else processor.get_train_examples(args.data_dir  )
                         )
                         SCREAMING_SNAKE_CASE   :      Dict    					=						convert_examples_to_features(
                             A,							self.tokenizer,							max_length=args.max_seq_length,							label_list=self.labels,							output_mode=args.glue_output_mode,							)
                         logger.info('Saving features into cached file %s',							A  )
                         torch.save(A,							A  )







       def        UpperCamelCase_							(      self,							A,							A,							A = False  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						'dev' if mode == 'test' else mode

             SCREAMING_SNAKE_CASE   :      str    					=						self._feature_file(A  )
             logger.info('Loading features from cached file %s',							A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						torch.load(A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.tensor([f.input_ids for f in features],							dtype=torch.long  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						torch.tensor([f.attention_mask for f in features],							dtype=torch.long  )
             SCREAMING_SNAKE_CASE   :      int    					=						torch.tensor([f.token_type_ids for f in features],							dtype=torch.long  )
             if self.hparams.glue_output_mode == "classification":
                   SCREAMING_SNAKE_CASE   :      int    					=						torch.tensor([f.label for f in features],							dtype=torch.long  )
             elif self.hparams.glue_output_mode == "regression":
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						torch.tensor([f.label for f in features],							dtype=torch.float  )

             return DataLoader(
                 TensorDataset(A,							A,							A,							A  ),							batch_size=A,							shuffle=A,							)







       def        UpperCamelCase_							(      self,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

             if self.config.model_type not in ["distilbert", "bart"]:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self(**A  )
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						outputs[:2]
             SCREAMING_SNAKE_CASE   :      Any    					=						logits.detach().cpu().numpy()
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						inputs['labels'].detach().cpu().numpy()

             return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						torch.stack([x['val_loss'] for x in outputs]  ).mean().detach().cpu().item()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						np.concatenate([x['pred'] for x in outputs],							axis=0  )

             if self.hparams.glue_output_mode == "classification":
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						np.argmax(A,							axis=1  )
             elif self.hparams.glue_output_mode == "regression":
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						np.squeeze(A  )

             SCREAMING_SNAKE_CASE   :      Any    					=						np.concatenate([x['target'] for x in outputs],							axis=0  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[[] for _ in range(out_label_ids.shape[0]  )]
             SCREAMING_SNAKE_CASE   :      int    					=						[[] for _ in range(out_label_ids.shape[0]  )]

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task,							A,							A  )}

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						dict(results.items()  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						results
             return ret, preds_list, out_label_list







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						self._eval_end(A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						ret['log']
             return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Tuple    					=						self._eval_end(A  )
             SCREAMING_SNAKE_CASE   :      str    					=						ret['log']
             # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
             return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







       @staticmethod
       def        UpperCamelCase_							(      A,							A  ):







             '''simple docstring'''







             BaseTransformer.add_model_specific_args(A,							A  )
             parser.add_argument(
                 '--max_seq_length',							default=128,							type=A,							help=(
                     'The maximum total input sequence length after tokenization. Sequences longer '
                     'than this will be truncated, sequences shorter will be padded.'
                 ),							)

             parser.add_argument(
                 '--task',							default='',							type=A,							required=A,							help='The GLUE task to run',							)
             parser.add_argument(
                 '--gpus',							default=0,							type=A,							help='The number of GPUs allocated for this, it is by default 0 meaning none',							)

             parser.add_argument(
                 '--overwrite_cache',							action='store_true',							help='Overwrite the cached training and evaluation sets'  )

             return parser




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						argparse.ArgumentParser()
      add_generic_args(__UpperCamelCase      ,os.getcwd()    )
      SCREAMING_SNAKE_CASE   :      List[str]    					=						GLUETransformer.add_model_specific_args(__UpperCamelCase      ,os.getcwd()    )
      SCREAMING_SNAKE_CASE   :      Dict    					=						parser.parse_args()

      # If output_dir not provided, a folder will be generated in pwd
      if args.output_dir is None:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						os.path.join(
                './results'      ,f"{args.task}_{time.strftime('%Y%m%d_%H%M%S'    )}"      ,)
            os.makedirs(args.output_dir    )

      SCREAMING_SNAKE_CASE   :      str    					=						GLUETransformer(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      str    					=						generic_train(__UpperCamelCase      ,__UpperCamelCase    )

      # Optionally, predict on dev set and write to output_dir
      if args.do_predict:
            SCREAMING_SNAKE_CASE   :      List[str]    					=						sorted(glob.glob(os.path.join(args.output_dir      ,'checkpoint-epoch=*.ckpt'    )      ,recursive=__UpperCamelCase    )    )
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						model.load_from_checkpoint(checkpoints[-1]    )
            return trainer.test(__UpperCamelCase    )


if __name__ == "__main__":
      main()








'''simple docstring'''



class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						val
             SCREAMING_SNAKE_CASE   :      Any    					=						None
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						None







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if self.val:
                   if val < self.val:
                         if self.left is None:
                               SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Node(A  )
                         else:
                               self.left.insert(A  )
                   elif val > self.val:
                         if self.right is None:
                               SCREAMING_SNAKE_CASE   :      int    					=						Node(A  )
                         else:
                               self.right.insert(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      int    					=						val




def   lowercase__(			__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      if root:
            inorder(root.left      ,__UpperCamelCase    )
            res.append(root.val    )
            inorder(root.right      ,__UpperCamelCase    )




def   lowercase__(			__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      if len(__UpperCamelCase    ) == 0:
            return arr
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Node(arr[0]    )
      for i in range(1      ,len(__UpperCamelCase    )    ):
            root.insert(arr[i]    )
      # Traverse BST in order.
      SCREAMING_SNAKE_CASE   :      Dict    					=						[]
      inorder(__UpperCamelCase      ,__UpperCamelCase    )
      return res


if __name__ == "__main__":
      print(tree_sort([1_0, 1, 3, 2, 9, 1_4, 1_3]))


