






import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


A_						:						List[str]						      =					logging.getLogger(__name__)




class 							_lowerCAmelCase(			__SCREAMING_SNAKE_CASE   ):





       """simple docstring"""







       a							:						Dict										="""token-classification"""





       def __init__(   self    ,     _lowerCamelCase				):

         if type(A_				) == dict:
           UpperCamelCase_:   int        =							Namespace(**A_				)
         UpperCamelCase_:   Optional[Any]        =							import_module('tasks'				)
         try:
           UpperCamelCase_:   int        =							getattr(A_    ,     hparams.task_type				)
           UpperCamelCase_:   List[str]        =							token_classification_task_clazz()
         except AttributeError:
           raise ValueError(
               f'''Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
               f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''				)
         UpperCamelCase_:   int        =							self.token_classification_task.get_labels(hparams.labels				)
         UpperCamelCase_:   int        =							CrossEntropyLoss().ignore_index
         super().__init__(A_    ,     len(self.labels				)    ,     self.mode				)





       def      _a       (   self    ,     **_lowerCamelCase				):
         return self.model(**A_				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   Tuple        =							{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
         if self.config.model_type != "distilbert":
           UpperCamelCase_:   Optional[int]        =							(
               batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
           )  # XLM and RoBERTa don"t use token_type_ids

         UpperCamelCase_:   Optional[Any]        =							self(**A_				)
         UpperCamelCase_:   Union[str, Any]        =							outputs[0]
         # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss}





       def      _a       (   self				):
         UpperCamelCase_:   List[str]        =							self.hparams
         for mode in ["train", "dev", "test"]:
           UpperCamelCase_:   str        =							self._feature_file(A_				)
           if os.path.exists(A_				) and not args.overwrite_cache:
             logger.info('Loading features from cached file %s'    ,     A_				)
             UpperCamelCase_:   List[str]        =							torch.load(A_				)
           else:
             logger.info('Creating features from dataset file at %s'    ,     args.data_dir				)
             UpperCamelCase_:   List[Any]        =							self.token_classification_task.read_examples_from_file(args.data_dir    ,     A_				)
             UpperCamelCase_:   List[Any]        =							self.token_classification_task.convert_examples_to_features(
                 A_    ,     self.labels    ,     args.max_seq_length    ,     self.tokenizer    ,     cls_token_at_end=bool(self.config.model_type in ['xlnet']				)    ,     cls_token=self.tokenizer.cls_token    ,     cls_token_segment_id=2 if self.config.model_type in ['xlnet'] else 0    ,     sep_token=self.tokenizer.sep_token    ,     sep_token_extra=A_    ,     pad_on_left=bool(self.config.model_type in ['xlnet']				)    ,     pad_token=self.tokenizer.pad_token_id    ,     pad_token_segment_id=self.tokenizer.pad_token_type_id    ,     pad_token_label_id=self.pad_token_label_id    ,     )
             logger.info('Saving features into cached file %s'    ,     A_				)
             torch.save(A_    ,     A_				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = False				):
         UpperCamelCase_:   Optional[Any]        =							self._feature_file(A_				)
         logger.info('Loading features from cached file %s'    ,     A_				)
         UpperCamelCase_:   Tuple        =							torch.load(A_				)
         UpperCamelCase_:   List[str]        =							torch.tensor([f.input_ids for f in features]    ,     dtype=torch.long				)
         UpperCamelCase_:   Any        =							torch.tensor([f.attention_mask for f in features]    ,     dtype=torch.long				)
         if features[0].token_type_ids is not None:
           UpperCamelCase_:   int        =							torch.tensor([f.token_type_ids for f in features]    ,     dtype=torch.long				)
         else:
           UpperCamelCase_:   str        =							torch.tensor([0 for f in features]    ,     dtype=torch.long				)
           # HACK(we will not use this anymore soon)
         UpperCamelCase_:   List[Any]        =							torch.tensor([f.label_ids for f in features]    ,     dtype=torch.long				)
         return DataLoader(
             TensorDataset(A_    ,     A_    ,     A_    ,     A_				)    ,     batch_size=A_				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         """Compute validation""" ""
         UpperCamelCase_:   Tuple        =							{'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
         if self.config.model_type != "distilbert":
           UpperCamelCase_:   Any        =							(
               batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
           )  # XLM and RoBERTa don"t use token_type_ids
         UpperCamelCase_:   List[Any]        =							self(**A_				)
         UpperCamelCase_ ,UpperCamelCase_:   Tuple        =							outputs[:2]
         UpperCamelCase_:   Any        =							logits.detach().cpu().numpy()
         UpperCamelCase_:   Optional[int]        =							inputs['labels'].detach().cpu().numpy()
         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}





       def      _a       (   self    ,     _lowerCamelCase				):
         UpperCamelCase_:   Optional[int]        =							torch.stack([x['val_loss'] for x in outputs]				).mean()
         UpperCamelCase_:   int        =							np.concatenate([x['pred'] for x in outputs]    ,     axis=0				)
         UpperCamelCase_:   Union[str, Any]        =							np.argmax(A_    ,     axis=2				)
         UpperCamelCase_:   Optional[int]        =							np.concatenate([x['target'] for x in outputs]    ,     axis=0				)

         UpperCamelCase_:   str        =							dict(enumerate(self.labels				)				)
         UpperCamelCase_:   Tuple        =							[[] for _ in range(out_label_ids.shape[0]				)]
         UpperCamelCase_:   Tuple        =							[[] for _ in range(out_label_ids.shape[0]				)]

         for i in range(out_label_ids.shape[0]				):
           for j in range(out_label_ids.shape[1]				):
             if out_label_ids[i, j] != self.pad_token_label_id:
               out_label_list[i].append(label_map[out_label_ids[i][j]]				)
               preds_list[i].append(label_map[preds[i][j]]				)

         UpperCamelCase_:   List[str]        =							{
             'val_loss': val_loss_mean,
             'accuracy_score': accuracy_score(A_    ,     A_				),
             'precision': precision_score(A_    ,     A_				),
             'recall': recall_score(A_    ,     A_				),
             'f1': fa_score(A_    ,     A_				),
         }

         UpperCamelCase_:   Optional[Any]        =							dict(results.items()				)
         UpperCamelCase_:   str        =							results
         return ret, preds_list, out_label_list





       def      _a       (   self    ,     _lowerCamelCase				):
         UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   Optional[Any]        =							self._eval_end(A_				)
         UpperCamelCase_:   Optional[int]        =							ret['log']
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





       def      _a       (   self    ,     _lowerCamelCase				):
         UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_:   List[Any]        =							self._eval_end(A_				)

         # Converting to the dict required by pl
         # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
         # pytorch_lightning/trainer/logging.py#L139
         UpperCamelCase_:   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      _a       (   _lowerCamelCase    ,     _lowerCamelCase				):
         BaseTransformer.add_model_specific_args(A_    ,     A_				)
         parser.add_argument(
             '--task_type'    ,     default='NER'    ,     type=A_    ,     help='Task type to fine tune in training (e.g. NER, POS, etc)'				)
         parser.add_argument(
             '--max_seq_length'    ,     default=1_2_8    ,     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(
             '--labels'    ,     default=''    ,     type=A_    ,     help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'    ,     )
         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


if __name__ == "__main__":
   A_						:						List[str]						      =					argparse.ArgumentParser()
   add_generic_args(parser, os.getcwd())
   A_						:						List[Any]						      =					NERTransformer.add_model_specific_args(parser, os.getcwd())
   A_						:						Optional[Any]						      =					parser.parse_args()
   A_						:						Any						      =					NERTransformer(args)
   A_						:						Any						      =					generic_train(model, args)

   if args.do_predict:
      # See https://github.com/huggingface/transformers/issues/3159
      # pl use this default format to create a checkpoint:
      # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
      # /pytorch_lightning/callbacks/model_checkpoint.py#L322
      A_						:						Optional[int]						      =					sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
      A_						:						Optional[int]						      =					model.load_from_checkpoint(checkpoints[-1])
      trainer.test(model)






import operator as op


def 							__snake_case							(    lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			lambda lowerCAmelCase_ ,							lowerCAmelCase_	: int(x / y	)  # noqa: E731 integer division operation
    SCREAMING_SNAKE_CASE__											=			{
        '''^''': op.pow,
        '''*''': op.mul,
        '''/''': div,
        '''+''': op.add,
        '''-''': op.sub,
    }  # operators & their respective operation

    # print table header
    print('''Symbol'''.center(8	) ,							'''Action'''.center(1_2	) ,							'''Stack''' ,							sep=''' | '''	)
    print('''-''' * (3_0 + len(lowerCAmelCase_	))	)

    for x in post_fix:
        if x.isdigit():  # if x in digit
            stack.append(lowerCAmelCase_	)  # append x to stack
            # output in tabular format
            print(x.rjust(8	) ,							('''push(''' + x + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | '''	)
        else:
            SCREAMING_SNAKE_CASE__											=			stack.pop()  # pop stack
            # output in tabular format
            print(''''''.rjust(8	) ,							('''pop(''' + b + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | '''	)

            SCREAMING_SNAKE_CASE__											=			stack.pop()  # pop stack
            # output in tabular format
            print(''''''.rjust(8	) ,							('''pop(''' + a + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | '''	)

            stack.append(
                str(opr[x](int(lowerCAmelCase_	) ,							int(lowerCAmelCase_	)	)	)	)  # evaluate the 2 values popped from stack & push result to stack
            # output in tabular format
            print(
                x.rjust(8	) ,							('''push(''' + a + x + b + ''')''').ljust(1_2	) ,							''','''.join(lowerCAmelCase_	) ,							sep=''' | ''' ,							)

    return int(stack[0]	)


if __name__ == "__main__":
  _A					:				Dict				=			input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
  print("""\n\tResult = """, solve(Postfix))
