





from typing import Any





class 			A__   :




   """simple docstring"""



   def __init__( self ,  lowercase)      ->						List[str]:







          '''simple docstring'''

          a__						:     Any			=    data
          a__						:     str			=    None

   def __repr__( self)      ->						str:







          '''simple docstring'''

          return F'Node({self.data})'





class 			A__   :




   """simple docstring"""



   def __init__( self)      ->						Any:







          '''simple docstring'''

          a__						:     Dict			=    None

   def __iter__( self)      ->						Any:







          '''simple docstring'''

          a__						:     Optional[int]			=    self.head
          while node:
                 yield node.data
                 a__						:     Dict			=    node.next

   def __len__( self)      ->						int:







          '''simple docstring'''

          return sum(1 for _ in self)

   def __repr__( self)      ->						str:







          '''simple docstring'''

          return "->".join([str(lowercase) for item in self])

   def __getitem__( self ,  lowercase)      ->						Any:







          '''simple docstring'''

          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 ,  lowercase ,  lowercase)      ->						None:







          '''simple docstring'''

          if not 0 <= index < len(self):
                 raise ValueError('list index out of range.')
          a__						:     Any			=    self.head
          for _ in range(lowercase):
                 a__						:     int			=    current.next
          a__						:     Union[str, Any]			=    data

   def 							__lowercase		( self ,  lowercase)      ->						None:







          '''simple docstring'''

          self.insert_nth(len(self) ,  lowercase)

   def 							__lowercase		( self ,  lowercase)      ->						None:







          '''simple docstring'''

          self.insert_nth(0 ,  lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase)      ->						None:







          '''simple docstring'''

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

   def 							__lowercase		( self)      ->						None:  # print every node data







          '''simple docstring'''

          print(self)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          return self.delete_nth(0)

   def 							__lowercase		( self)      ->						Any:  # delete from tail







          '''simple docstring'''

          return self.delete_nth(len(self) - 1)

   def 							__lowercase		( self ,  lowercase = 0)      ->						Any:







          '''simple docstring'''

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

   def 							__lowercase		( self)      ->						bool:







          '''simple docstring'''

          return self.head is None

   def 							__lowercase		( self)      ->						None:







          '''simple docstring'''

          a__						:     Tuple			=    None
          a__						:     Tuple			=    self.head

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



def A_     (		)  ->		None:
       a__						:     List[str]			=    LinkedList()
       assert linked_list.is_empty() is True
       assert str(A__    ) == ""

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

       linked_list.insert_head(0    )
       linked_list.insert_tail(11    )
       assert str(A__    ) == "->".join(str(A__    ) 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(A__    ) == 9
       assert str(A__    ) == "->".join(str(A__    ) 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    ):
              a__						:     List[Any]			=    -i
       assert all(linked_list[i] == -i for i in range(0		,				9    )    ) is True

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



def A_     (		)  ->		None:
       a__						:     List[Any]			=    [
           -9,
           100,
           Node(7734_5112    ),
           'dlrow olleH',
           7,
           5555,
           0,
           -1_92.5_55_55,
           'Hello, world!',
           77.9,
           Node(10    ),
           None,
           None,
           12.20,
       ]
       a__						:     Dict			=    LinkedList()

       for i in test_input:
              linked_list.insert_tail(A__    )

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

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

       # Delete the tail
       a__						:     Tuple			=    linked_list.delete_tail()
       assert result == 12.2
       assert (
           str(A__    ) == "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
       a__						:     List[str]			=    linked_list.delete_nth(10    )
       assert result is None
       assert (
           str(A__    ) == "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(A__    )
           == "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(A__    )
       assert (
           str(A__    )
           == "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(A__    )
           == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
           "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
       )



def A_     (		)  ->		str:
       from doctest import testmod

       testmod()

       a__						:     Union[str, Any]			=    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(A__    )
       print('\nReading/changing Node data using indexing:'    )
       print(F'Element at Position 1: {linked_list[1]}'    )
       a__						:     Union[str, Any]			=    input('Enter New Value: '    ).strip()
       print('New list:'    )
       print(A__    )
       print(F'length of linked_list is : {len(A__    )}'    )


if __name__ == "__main__":
    main()









import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase				:	Union[str, Any]      						=  logging.getLogger(__name__)





@dataclass
class 			A__   :




   """simple docstring"""



   __A       :    str	  =  field(
       metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}     )
   __A       :    Optional[str]	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}     )
   __A       :    Optional[str]	  =  field(
       default='''NER'''			,  metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}     )
   __A       :    Optional[str]	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}     )
   __A       :    bool	  =  field(default=__UpperCAmelCase			,  metadata={'''help''': '''Set this flag to use fast tokenization.'''}     )
   # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
   # or just modify its tokenizer_config.json.
   __A       :    Optional[str]	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,  )





@dataclass
class 			A__   :




   """simple docstring"""



   __A       :    str	  =  field(
       metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}     )
   __A       :    Optional[str]	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}			,  )
   __A       :    int	  =  field(
       default=1_2_8			,  metadata={
           '''help''': (
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )
       }			,  )
   __A       :    bool	  =  field(
       default=__UpperCAmelCase			,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )



def A_     (		)  ->		Dict:
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.

       a__						:     List[str]			=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
       if len(sys.argv    ) == 2 and sys.argv[1].endswith('.json'    ):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              a__			,							a__			,							a__						:     int			=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
       else:
              a__			,							a__			,							a__						:     List[Any]			=    parser.parse_args_into_dataclasses()

       if (
           os.path.exists(training_args.output_dir    )
           and os.listdir(training_args.output_dir    )
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
                  ' --overwrite_output_dir to overcome.'    )

       a__						:     Optional[Any]			=    import_module('tasks'    )
       try:
              a__						:     List[Any]			=    getattr(A__		,				model_args.task_type    )
              a__						:     TokenClassificationTask			=    token_classification_task_clazz()
       except AttributeError:
              raise ValueError(
                  F'Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
                  F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'    )

       # Setup logging
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'		,				datefmt='%m/%d/%Y %H:%M:%S'		,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		,				)
       logger.warning(
           'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'		,				training_args.local_rank		,				training_args.device		,				training_args.n_gpu		,				bool(training_args.local_rank != -1    )		,				training_args.fpaa		,				)
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank    ):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info('Training/evaluation parameters %s'		,				A__    )

       # Set seed
       set_seed(training_args.seed    )

       # Prepare CONLL-2003 task
       a__						:     Tuple			=    token_classification_task.get_labels(data_args.labels    )
       a__						:     Dict[int, str]			=    dict(enumerate(A__    )    )
       a__						:     Union[str, Any]			=    len(A__    )

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       a__						:     Optional[Any]			=    AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path		,				num_labels=A__		,				idalabel=A__		,				labelaid={label: i for i, label in enumerate(A__    )}		,				cache_dir=model_args.cache_dir		,				)
       a__						:     str			=    AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,				cache_dir=model_args.cache_dir		,				use_fast=model_args.use_fast		,				)
       a__						:     List[Any]			=    AutoModelForTokenClassification.from_pretrained(
           model_args.model_name_or_path		,				from_tf=bool('.ckpt' in model_args.model_name_or_path    )		,				config=A__		,				cache_dir=model_args.cache_dir		,				)

       # Get datasets
       a__						:     int			=    (
           TokenClassificationDataset(
               token_classification_task=A__		,				data_dir=data_args.data_dir		,				tokenizer=A__		,				labels=A__		,				model_type=config.model_type		,				max_seq_length=data_args.max_seq_length		,				overwrite_cache=data_args.overwrite_cache		,				mode=Split.train		,				)
           if training_args.do_train
           else None
       )
       a__						:     Optional[int]			=    (
           TokenClassificationDataset(
               token_classification_task=A__		,				data_dir=data_args.data_dir		,				tokenizer=A__		,				labels=A__		,				model_type=config.model_type		,				max_seq_length=data_args.max_seq_length		,				overwrite_cache=data_args.overwrite_cache		,				mode=Split.dev		,				)
           if training_args.do_eval
           else None
       )

       def align_predictions(A__		,				A__    ) -> Tuple[List[int], List[int]]:
              a__						:     Union[str, Any]			=    np.argmax(A__		,				axis=2    )

              a__			,							a__						:     Dict			=    preds.shape

              a__						:     Union[str, Any]			=    [[] for _ in range(A__    )]
              a__						:     Optional[int]			=    [[] for _ in range(A__    )]

              for i in range(A__    ):
                     for j in range(A__    ):
                            if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                                   out_label_list[i].append(label_map[label_ids[i][j]]    )
                                   preds_list[i].append(label_map[preds[i][j]]    )

              return preds_list, out_label_list

       def compute_metrics(A__    ) -> Dict:
              a__			,							a__						:     Union[str, Any]			=    align_predictions(p.predictions		,				p.label_ids    )
              return {
                  "accuracy_score": accuracy_score(A__		,				A__    ),
                  "precision": precision_score(A__		,				A__    ),
                  "recall": recall_score(A__		,				A__    ),
                  "f1": fa_score(A__		,				A__    ),
              }

       # Data collator
       a__						:     Union[str, Any]			=    DataCollatorWithPadding(A__		,				pad_to_multiple_of=8    ) if training_args.fpaa else None

       # Initialize our Trainer
       a__						:     List[str]			=    Trainer(
           model=A__		,				args=A__		,				train_dataset=A__		,				eval_dataset=A__		,				compute_metrics=A__		,				data_collator=A__		,				)

       # Training
       if training_args.do_train:
              trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
              trainer.save_model()
              # For convenience, we also re-save the tokenizer to the same directory,
              # so that you can share your model easily on huggingface.co/models =)
              if trainer.is_world_process_zero():
                     tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
       a__						:     Any			=    {}
       if training_args.do_eval:
              logger.info('*** Evaluate ***'    )

              a__						:     Optional[Any]			=    trainer.evaluate()

              a__						:     List[Any]			=    os.path.join(training_args.output_dir		,				'eval_results.txt'    )
              if trainer.is_world_process_zero():
                     with open(A__		,				'w'    ) as writer:
                            logger.info('***** Eval results *****'    )
                            for key, value in result.items():
                                   logger.info('  %s = %s'		,				A__		,				A__    )
                                   writer.write('%s = %s\n' % (key, value)    )

                     results.update(A__    )

    # Predict
       if training_args.do_predict:
              a__						:     Optional[Any]			=    TokenClassificationDataset(
                  token_classification_task=A__		,				data_dir=data_args.data_dir		,				tokenizer=A__		,				labels=A__		,				model_type=config.model_type		,				max_seq_length=data_args.max_seq_length		,				overwrite_cache=data_args.overwrite_cache		,				mode=Split.test		,				)

              a__			,							a__			,							a__						:     Any			=    trainer.predict(A__    )
              a__			,							a__						:     Union[str, Any]			=    align_predictions(A__		,				A__    )

              a__						:     Optional[int]			=    os.path.join(training_args.output_dir		,				'test_results.txt'    )
              if trainer.is_world_process_zero():
                     with open(A__		,				'w'    ) as writer:
                            for key, value in metrics.items():
                                   logger.info('  %s = %s'		,				A__		,				A__    )
                                   writer.write('%s = %s\n' % (key, value)    )

        # Save predictions
              a__						:     Tuple			=    os.path.join(training_args.output_dir		,				'test_predictions.txt'    )
              if trainer.is_world_process_zero():
                     with open(A__		,				'w'    ) as writer:
                            with open(os.path.join(data_args.data_dir		,				'test.txt'    )		,				'r'    ) as f:
                                   token_classification_task.write_predictions_to_file(A__		,				A__		,				A__    )

       return results



def A_     (		A__    )  ->		Tuple:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
    main()


