





class A_      :




   def __init__(self			:Union[str, Any]				)->      None:
         # numbermap keys are the number and its values are lists of indexes sorted
         # in ascending order
         __A      =       {}
         # indexmap keys are an index and it's values are the number at that index
         __A      =       {}




   def _lowerCAmelCase   (self			:Any					,     _UpperCamelCase			:list | str | range					,     _UpperCamelCase			:int				)->      list[int]:
         if isinstance(_UpperCamelCase					,     (range, str)				):
               __A      =       list(_UpperCamelCase				)
         elif not isinstance(_UpperCamelCase					,     _UpperCamelCase				):
               raise TypeError(
                   '''binary_search_delete() only accepts either a list, range or str'''				)

         __A      =       0
         __A      =       len(_UpperCamelCase				) - 1

         while low <= high:
               __A      =       (low + high) // 2
               if array[mid] == item:
                     array.pop(_UpperCamelCase				)
                     return array
               elif array[mid] < item:
                     __A      =       mid + 1
               else:
                     __A      =       mid - 1
         raise ValueError(
             '''Either the item is not in the array or the array was unsorted'''				)




   def _lowerCAmelCase   (self			:Optional[Any]					,     _UpperCamelCase			:list | str | range					,     _UpperCamelCase			:int				)->      list[int]:
         if isinstance(_UpperCamelCase					,     (range, str)				):
               __A      =       list(_UpperCamelCase				)
         elif not isinstance(_UpperCamelCase					,     _UpperCamelCase				):
               raise TypeError(
                   '''binary_search_insert() only accepts either a list, range or str'''				)

         __A      =       0
         __A      =       len(_UpperCamelCase				) - 1

         while low <= high:
               __A      =       (low + high) // 2
               if array[mid] == index:
                     # If the item already exists in the array,
                     # insert it after the existing item
                     array.insert(mid + 1					,     _UpperCamelCase				)
                     return array
               elif array[mid] < index:
                     __A      =       mid + 1
               else:
                     __A      =       mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
         array.insert(_UpperCamelCase					,     _UpperCamelCase				)
         return array




   def _lowerCAmelCase   (self			:Union[str, Any]					,     _UpperCamelCase			:int					,     _UpperCamelCase			:int				)->      None:
         # Remove previous index
         if index in self.indexmap:
               __A      =       self.indexmap[index]
               if len(self.numbermap[n]				) == 1:
                     del self.numbermap[n]
               else:
                     __A      =       self.binary_search_delete(self.numbermap[n]					,     _UpperCamelCase				)

        # Set new index
         __A      =       number

         # Number not seen before or empty so insert number value
         if number not in self.numbermap:
               __A      =       [index]

         # Here we need to perform a binary search insertion in order to insert
         # The item in the correct place
         else:
               __A      =       self.binary_search_insert(
                   self.numbermap[number]					,     _UpperCamelCase				)






   def _lowerCAmelCase   (self			:List[Any]					,     _UpperCamelCase			:int				)->      int:
         return self.numbermap.get(_UpperCamelCase					,     [-1]				)[0]


if __name__ == "__main__":
       import doctest

       doctest.testmod()












import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


snake_case__      :							str									=  logging.getLogger(__name__)





@dataclass
class A_      :
   lowerCAmelCase__       =				field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}     )
   lowerCAmelCase__       =				field(
       default=_lowerCamelCase				,   metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}     )
   lowerCAmelCase__       =				field(
       default="""NER"""				,   metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}     )
   lowerCAmelCase__       =				field(
       default=_lowerCamelCase				,   metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}     )
   lowerCAmelCase__       =				field(default=_lowerCamelCase				,   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.
   lowerCAmelCase__       =				field(
       default=_lowerCamelCase				,   metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}				,   )





@dataclass
class A_      :
   lowerCAmelCase__       =				field(
       metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}     )
   lowerCAmelCase__       =				field(
       metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}     )
   lowerCAmelCase__       =				field(
       default=128				,   metadata={
           """help""": (
               """The maximum total input sequence length after tokenization. Sequences longer """
               """than this will be truncated, sequences shorter will be padded."""
           )
       }				,   )
   lowerCAmelCase__       =				field(
       default=_lowerCamelCase				,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )





def _a       (			)			->		int:







      '''simple docstring'''






      __A      =       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
      __A     ,		__A     ,		__A      =       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      =       import_module('''tasks'''				)

      try:
            __A      =       getattr(lowerCamelCase      ,       model_args.task_type				)
            __A      =       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      ,       )
      logger.info(
          '''n_replicas: %s, distributed training: %s, 16-bits training: %s'''      ,       training_args.n_replicas      ,       bool(training_args.n_replicas > 1				)      ,       training_args.fpaa      ,       )
      logger.info('''Training/evaluation parameters %s'''      ,       lowerCamelCase				)

      # Prepare Token Classification task
      __A      =       token_classification_task.get_labels(data_args.labels				)
      __A      =       dict(enumerate(lowerCamelCase				)				)
      __A      =       len(lowerCamelCase				)

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

      __A      =       AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,       num_labels=lowerCamelCase      ,       idalabel=lowerCamelCase      ,       labelaid={label: i for i, label in enumerate(lowerCamelCase				)}      ,       cache_dir=model_args.cache_dir      ,       )
      __A      =       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      ,       )

      with training_args.strategy.scope():
            __A      =       TFAutoModelForTokenClassification.from_pretrained(
                model_args.model_name_or_path      ,       from_pt=bool('''.bin''' in model_args.model_name_or_path				)      ,       config=lowerCamelCase      ,       cache_dir=model_args.cache_dir      ,       )

      # Get datasets
      __A      =       (
          TFTokenClassificationDataset(
              token_classification_task=lowerCamelCase      ,       data_dir=data_args.data_dir      ,       tokenizer=lowerCamelCase      ,       labels=lowerCamelCase      ,       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      =       (
          TFTokenClassificationDataset(
              token_classification_task=lowerCamelCase      ,       data_dir=data_args.data_dir      ,       tokenizer=lowerCamelCase      ,       labels=lowerCamelCase      ,       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(lowerCamelCase:     np.ndarray      ,       lowerCamelCase:     np.ndarray				) -> Tuple[List[int], List[int]]:
            __A      =       np.argmax(lowerCamelCase      ,       axis=2				)
            __A     ,		__A      =       preds.shape
            __A      =       [[] for _ in range(lowerCamelCase				)]
            __A      =       [[] for _ in range(lowerCamelCase				)]

            for i in range(lowerCamelCase				):
                  for j in range(lowerCamelCase				):
                        if label_ids[i, j] != -1_00:
                              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(lowerCamelCase:     EvalPrediction				) -> Dict:
            __A     ,		__A      =       align_predictions(p.predictions      ,       p.label_ids				)

            return {
                "precision": precision_score(lowerCamelCase      ,       lowerCamelCase				),
                "recall": recall_score(lowerCamelCase      ,       lowerCamelCase				),
                "f1": fa_score(lowerCamelCase      ,       lowerCamelCase				),
            }

      # Initialize our Trainer
      __A      =       TFTrainer(
          model=lowerCamelCase      ,       args=lowerCamelCase      ,       train_dataset=train_dataset.get_dataset() if train_dataset else None      ,       eval_dataset=eval_dataset.get_dataset() if eval_dataset else None      ,       compute_metrics=lowerCamelCase      ,       )

      # Training
      if training_args.do_train:
            trainer.train()
            trainer.save_model()
            tokenizer.save_pretrained(training_args.output_dir				)

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

            __A      =       trainer.evaluate()
            __A      =       os.path.join(training_args.output_dir      ,       '''eval_results.txt'''				)

            with open(lowerCamelCase      ,       '''w'''				) as writer:
                  logger.info('''***** Eval results *****'''				)

                  for key, value in result.items():
                        logger.info('''  %s = %s'''      ,       lowerCamelCase      ,       lowerCamelCase				)
                        writer.write('''%s = %s\n''' % (key, value)				)

                  results.update(lowerCamelCase				)

    # Predict
      if training_args.do_predict:
            __A      =       TFTokenClassificationDataset(
                token_classification_task=lowerCamelCase      ,       data_dir=data_args.data_dir      ,       tokenizer=lowerCamelCase      ,       labels=lowerCamelCase      ,       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      =       trainer.predict(test_dataset.get_dataset()				)
            __A     ,		__A      =       align_predictions(lowerCamelCase      ,       lowerCamelCase				)
            __A      =       classification_report(lowerCamelCase      ,       lowerCamelCase				)

            logger.info('''\n%s'''      ,       lowerCamelCase				)

            __A      =       os.path.join(training_args.output_dir      ,       '''test_results.txt'''				)

            with open(lowerCamelCase      ,       '''w'''				) as writer:
                  writer.write('''%s\n''' % report				)

            # Save predictions
            __A      =       os.path.join(training_args.output_dir      ,       '''test_predictions.txt'''				)

            with open(lowerCamelCase      ,       '''w'''				) as writer:
                  with open(os.path.join(data_args.data_dir      ,       '''test.txt'''				)      ,       '''r'''				) as f:
                        __A      =       0

                        for line in f:
                              if line.startswith('''-DOCSTART-'''				) or line == "" or line == "\n":
                                    writer.write(lowerCamelCase				)

                                    if not preds_list[example_id]:
                                          example_id += 1
                              elif preds_list[example_id]:
                                    __A      =       line.split()[0] + ''' ''' + preds_list[example_id].pop(0				) + '''\n'''

                                    writer.write(lowerCamelCase				)
                              else:
                                    logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''      ,       line.split()[0]				)

      return results


if __name__ == "__main__":
       main()





