



'''simple docstring'''






from typing import Any



class 							lowercase       :


 """simple docstring"""


 def __init__(							self  ,a_							)		->   Dict:
        _UpperCAmelCase  :						Tuple			     = data
        _UpperCAmelCase  :						Dict			     = None







 def __repr__(							self							)		->   Optional[int]:
        return f'''Node({self.data})'''







class 							lowercase       :


 """simple docstring"""


 def __init__(							self							)		->   List[str]:
        _UpperCAmelCase  :						Tuple			     = None







 def __iter__(							self							)		->   Tuple:
        _UpperCAmelCase  :						List[str]			     = self.head
        while node:
               yield node.data
               _UpperCAmelCase  :						Any			     = node.next







 def __len__(							self							)		->   Union[str, Any]:
        return sum(1 for _ in self							)







 def __repr__(							self							)		->   Tuple:
        return "->".join([str(snake_case__							) for item in self]							)







 def __getitem__(							self  ,a_							)		->   Dict:





        if not 0 <= index < len(self							):
               raise ValueError("""list index out of range."""							)
        for i, node in enumerate(self							):
               if i == index:
                      return node
        return None







 def __setitem__(							self  ,a_  ,a_							)		->   List[str]:





        if not 0 <= index < len(self							):
               raise ValueError("""list index out of range."""							)
        _UpperCAmelCase  :						Optional[int]			     = self.head
        for _ in range(snake_case__							):
               _UpperCAmelCase  :						str			     = current.next
        _UpperCAmelCase  :						Tuple			     = data







 def _snake_case (							self  ,a_							)		->   Any:
        self.insert_nth(len(self							)  ,snake_case__							)







 def _snake_case (							self  ,a_							)		->   int:
        self.insert_nth(0  ,snake_case__							)







 def _snake_case (							self  ,a_  ,a_							)		->   Optional[int]:





        if not 0 <= index <= len(self							):
               raise IndexError("""list index out of range"""							)
        _UpperCAmelCase  :						Dict			     = Node(snake_case__							)
        if self.head is None:
               _UpperCAmelCase  :						List[Any]			     = new_node
        elif index == 0:
               _UpperCAmelCase  :						int			     = self.head  # link new_node to head
               _UpperCAmelCase  :						str			     = new_node
        else:
               _UpperCAmelCase  :						Optional[Any]			     = self.head
               for _ in range(index - 1							):
                      _UpperCAmelCase  :						int			     = temp.next
               _UpperCAmelCase  :						List[Any]			     = temp.next
               _UpperCAmelCase  :						Optional[Any]			     = new_node







 def _snake_case (							self							)		->   Optional[int]:  # print every node data
        print(self							)







 def _snake_case (							self							)		->   Any:
        return self.delete_nth(0							)







 def _snake_case (							self							)		->   Optional[int]:  # delete from tail
        return self.delete_nth(len(self							) - 1							)







 def _snake_case (							self  ,a_ = 0							)		->   str:





        if not 0 <= index <= len(self							) - 1:  # test if index is valid
               raise IndexError("""List index out of range."""							)
        _UpperCAmelCase  :						Tuple			     = self.head  # default first node
        if index == 0:
               _UpperCAmelCase  :						Optional[int]			     = self.head.next
        else:
               _UpperCAmelCase  :						Union[str, Any]			     = self.head
               for _ in range(index - 1							):
                      _UpperCAmelCase  :						Optional[Any]			     = temp.next
               _UpperCAmelCase  :						str			     = temp.next
               _UpperCAmelCase  :						str			     = temp.next.next
        return delete_node.data







 def _snake_case (							self							)		->   Dict:
        return self.head is None







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						Union[str, Any]			     = None
        _UpperCAmelCase  :						Tuple			     = self.head

        while current:
               # Store the current node's next node.
               _UpperCAmelCase  :						Union[str, Any]			     = current.next
               # Make the current node's next point backwards
               _UpperCAmelCase  :						Any			     = prev
               # Make the previous node be the current node
               _UpperCAmelCase  :						Dict			     = current
               # Make the current node the next node (to progress iteration)
               _UpperCAmelCase  :						Optional[int]			     = next_node
        # Return prev in order to put the head at the end
        _UpperCAmelCase  :						Union[str, Any]			     = prev







def     snake_case_					(     )->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = LinkedList()
       assert linked_list.is_empty() is True
       assert str(lowerCAmelCase_		) == ""

       try:
              linked_list.delete_head()
              raise AssertionError  # This should not happen.
       except IndexError:
              assert True  # This should happen.

       try:
              linked_list.delete_tail()
              raise AssertionError  # This should not happen.
       except IndexError:
              assert True  # This should happen.

       for i in range(10		):
              assert len(lowerCAmelCase_		) == i
              linked_list.insert_nth(lowerCAmelCase_       ,   i + 1		)
       assert str(lowerCAmelCase_		) == "->".join(str(lowerCAmelCase_		) for i in range(1       ,   11		)		)

       linked_list.insert_head(0		)
       linked_list.insert_tail(11		)
       assert str(lowerCAmelCase_		) == "->".join(str(lowerCAmelCase_		) for i in range(0       ,   12		)		)

       assert linked_list.delete_head() == 0
       assert linked_list.delete_nth(9		) == 10
       assert linked_list.delete_tail() == 11
       assert len(lowerCAmelCase_		) == 9
       assert str(lowerCAmelCase_		) == "->".join(str(lowerCAmelCase_		) for i in range(1       ,   10		)		)

       assert all(linked_list[i] == i + 1 for i in range(0       ,   9		)		) is True

       for i in range(0       ,   9		):
              _UpperCAmelCase  :						List[Any]			     = -i
       assert all(linked_list[i] == -i for i in range(0       ,   9		)		) is True

       linked_list.reverse()
       assert str(lowerCAmelCase_		) == "->".join(str(lowerCAmelCase_		) for i in range(-8       ,   1		)		)




def     snake_case_					(     )->		List[str]:





       '''simple docstring'''


       _UpperCAmelCase  :						List[Any]			     = [
           -9,
           100,
           Node(77345112		),
           "dlrow olleH",
           7,
           5555,
           0,
           -1_9_2.5_5_5_5_5,
           "Hello, world!",
           7_7.9,
           Node(10		),
           None,
           None,
           1_2.2_0,
       ]
       _UpperCAmelCase  :						Tuple			     = LinkedList()

       for i in test_input:
              linked_list.insert_tail(lowerCAmelCase_		)

       # Check if it's empty or not
       assert linked_list.is_empty() is False
       assert (
           str(lowerCAmelCase_		) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
           "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
       )

       # Delete the head
       _UpperCAmelCase  :						Tuple			     = linked_list.delete_head()
       assert result == -9
       assert (
           str(lowerCAmelCase_		) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
           "Hello, world!->77.9->Node(10)->None->None->12.2"
       )

       # Delete the tail
       _UpperCAmelCase  :						List[Any]			     = linked_list.delete_tail()
       assert result == 1_2.2
       assert (
           str(lowerCAmelCase_		) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
           "Hello, world!->77.9->Node(10)->None->None"
       )

       # Delete a node in specific location in linked list
       _UpperCAmelCase  :						Union[str, Any]			     = linked_list.delete_nth(10		)
       assert result is None
       assert (
           str(lowerCAmelCase_		) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
           "Hello, world!->77.9->Node(10)->None"
       )

       # Add a Node instance to its head
       linked_list.insert_head(Node("""Hello again, world!"""		)		)
       assert (
           str(lowerCAmelCase_		)
           == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
           "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
       )

       # Add None to its tail
       linked_list.insert_tail(lowerCAmelCase_		)
       assert (
           str(lowerCAmelCase_		)
           == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
           "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
       )

       # Reverse the linked list
       linked_list.reverse()
       assert (
           str(lowerCAmelCase_		)
           == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
           "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
       )




def     snake_case_					(     )->		List[str]:





       '''simple docstring'''


       from doctest import testmod

       testmod()

       _UpperCAmelCase  :						Dict			     = LinkedList()
       linked_list.insert_head(input("""Inserting 1st at head """		).strip()		)
       linked_list.insert_head(input("""Inserting 2nd at head """		).strip()		)
       print("""\nPrint list:"""		)
       linked_list.print_list()
       linked_list.insert_tail(input("""\nInserting 1st at tail """		).strip()		)
       linked_list.insert_tail(input("""Inserting 2nd at tail """		).strip()		)
       print("""\nPrint list:"""		)
       linked_list.print_list()
       print("""\nDelete head"""		)
       linked_list.delete_head()
       print("""Delete tail"""		)
       linked_list.delete_tail()
       print("""\nPrint list:"""		)
       linked_list.print_list()
       print("""\nReverse linked list"""		)
       linked_list.reverse()
       print("""\nPrint list:"""		)
       linked_list.print_list()
       print("""\nString representation of linked list:"""		)
       print(lowerCAmelCase_		)
       print("""\nReading/changing Node data using indexing:"""		)
       print(F'''Element at Position 1: {linked_list[1]}'''		)
       _UpperCAmelCase  :						List[Any]			     = input("""Enter New Value: """		).strip()
       print("""New list:"""		)
       print(lowerCAmelCase_		)
       print(F'''length of linked_list is : {len(lowerCAmelCase_		)}'''		)


if __name__ == "__main__":
   main()







"""simple docstring"""







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


lowerCAmelCase__      				=   logging.getLogger(__name__)


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Tuple   ="token-classification"

       def __init__(						self		,     snake_case__						):






         """simple docstring"""





         if type(snake_case__						) == dict:
           lowerCAmelCase     :     List[str]								=							Namespace(**snake_case__						)
         lowerCAmelCase     :     Optional[Any]								=							import_module("tasks"						)
         try:
           lowerCAmelCase     :     Dict								=							getattr(snake_case__		,     hparams.task_type						)
           lowerCAmelCase     :     TokenClassificationTask								=							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__()}"""						)
         lowerCAmelCase     :     Dict								=							self.token_classification_task.get_labels(hparams.labels						)
         lowerCAmelCase     :     str								=							CrossEntropyLoss().ignore_index
         super().__init__(snake_case__		,     len(self.labels						)		,     self.mode						)

       def        lowercase__							(						self		,     **snake_case__						):






         """simple docstring"""





         return self.model(**snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
         if self.config.model_type != "distilbert":
           lowerCAmelCase     :     Optional[int]								=							(
               batch[2] if self.config.model_type in ["bert", "xlnet"] else None
           )  # XLM and RoBERTa don"t use token_type_ids

         lowerCAmelCase     :     str								=							self(**snake_case__						)
         lowerCAmelCase     :     Tuple								=							outputs[0]
         # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss}

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							self.hparams
         for mode in ["train", "dev", "test"]:
           lowerCAmelCase     :     Optional[int]								=							self._feature_file(snake_case__						)
           if os.path.exists(snake_case__						) and not args.overwrite_cache:
             logger.info("Loading features from cached file %s"		,     snake_case__						)
             lowerCAmelCase     :     Dict								=							torch.load(snake_case__						)
           else:
             logger.info("Creating features from dataset file at %s"		,     args.data_dir						)
             lowerCAmelCase     :     Optional[int]								=							self.token_classification_task.read_examples_from_file(args.data_dir		,     snake_case__						)
             lowerCAmelCase     :     Optional[int]								=							self.token_classification_task.convert_examples_to_features(
                 snake_case__		,     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=snake_case__		,     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"		,     snake_case__						)
             torch.save(snake_case__		,     snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__ = False						):






         """simple docstring"""





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

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         """Compute validation""" ""
         lowerCAmelCase     :     str								=							{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
         if self.config.model_type != "distilbert":
           lowerCAmelCase     :     List[Any]								=							(
               batch[2] if self.config.model_type in ["bert", "xlnet"] else None
           )  # XLM and RoBERTa don"t use token_type_ids
         lowerCAmelCase     :     Any								=							self(**snake_case__						)
         lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							outputs[:2]
         lowerCAmelCase     :     Dict								=							logits.detach().cpu().numpy()
         lowerCAmelCase     :     Optional[Any]								=							inputs["labels"].detach().cpu().numpy()
         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							torch.stack([x["val_loss"] for x in outputs]						).mean()
         lowerCAmelCase     :     Optional[Any]								=							np.concatenate([x["pred"] for x in outputs]		,     axis=0						)
         lowerCAmelCase     :     List[Any]								=							np.argmax(snake_case__		,     axis=2						)
         lowerCAmelCase     :     Optional[int]								=							np.concatenate([x["target"] for x in outputs]		,     axis=0						)

         lowerCAmelCase     :     str								=							dict(enumerate(self.labels						)						)
         lowerCAmelCase     :     Optional[int]								=							[[] for _ in range(out_label_ids.shape[0]						)]
         lowerCAmelCase     :     Dict								=							[[] 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]]						)

         lowerCAmelCase     :     Dict								=							{
             "val_loss": val_loss_mean,
             "accuracy_score": accuracy_score(snake_case__		,     snake_case__						),
             "precision": precision_score(snake_case__		,     snake_case__						),
             "recall": recall_score(snake_case__		,     snake_case__						),
             "f1": fa_score(snake_case__		,     snake_case__						),
         }

         lowerCAmelCase     :     int								=							dict(results.items()						)
         lowerCAmelCase     :     Tuple								=							results
         return ret, preds_list, out_label_list

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





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

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     int								=							self._eval_end(snake_case__						)

         # Converting to the dict required by pl
         # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
         # pytorch_lightning/trainer/logging.py#L139
         lowerCAmelCase     :     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        lowercase__							(						snake_case__		,     snake_case__						):






         """simple docstring"""





         BaseTransformer.add_model_specific_args(snake_case__		,     snake_case__						)
         parser.add_argument(
             "--task_type"		,     default="NER"		,     type=snake_case__		,     help="Task type to fine tune in training (e.g. NER, POS, etc)"						)
         parser.add_argument(
             "--max_seq_length"		,     default=128		,     type=snake_case__		,     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=snake_case__		,     help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."		,     )
         parser.add_argument(
             "--gpus"		,     default=0		,     type=snake_case__		,     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__":
     lowerCAmelCase__      				=   argparse.ArgumentParser()
     add_generic_args(parser, os.getcwd())
     lowerCAmelCase__      				=   NERTransformer.add_model_specific_args(parser, os.getcwd())
     lowerCAmelCase__      				=   parser.parse_args()
     lowerCAmelCase__      				=   NERTransformer(args)
     lowerCAmelCase__      				=   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
          lowerCAmelCase__      				=   sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
          lowerCAmelCase__      				=   model.load_from_checkpoint(checkpoints[-1])
          trainer.test(model)





