code: 
  
  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()
  
  
  __A   :     Optional[Any]      =			logging.getLogger(__name__)
  
  
  
  
  
  
  @dataclass
  class    __A				:
      lowerCAmelCase_							:		str						=							field(
          metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
      lowerCAmelCase_							:		Optional[str]						=							field(
          default=lowerCAmelCase						,						metadata={"help": "Pretrained config name or path if not the same as model_name"} )
      lowerCAmelCase_							:		Optional[str]						=							field(
          default="NER"						,						metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
      lowerCAmelCase_							:		Optional[str]						=							field(
          default=lowerCAmelCase						,						metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
      lowerCAmelCase_							:		bool						=							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_							:		Optional[str]						=							field(
          default=lowerCAmelCase						,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}						,						)
  
  
  
  
  
  
  @dataclass
  class    __A				:
      lowerCAmelCase_							:		str						=							field(
          metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
      lowerCAmelCase_							:		Optional[str]						=							field(
          metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} )
      lowerCAmelCase_							:		int						=							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_							:		bool						=							field(
          default=lowerCAmelCase						,						metadata={"help": "Overwrite the cached training and evaluation sets"} )
  
  
  
  def 		SCREAMING_SNAKE_CASE__				(							)					-> Optional[int]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Tuple    			=      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
      lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase				:			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.'				)
  
      lowerCAmelCase				:			str    			=      import_module('tasks'				)
  
      try:
          lowerCAmelCase				:			Dict    			=      getattr(_UpperCAmelCase,      model_args.task_type				)
          lowerCAmelCase				:			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,      )
      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',      _UpperCAmelCase				)
  
      # Prepare Token Classification task
      lowerCAmelCase				:			Tuple    			=      token_classification_task.get_labels(data_args.labels				)
      lowerCAmelCase				:			Dict[int, str]    			=      dict(enumerate(_UpperCAmelCase				)				)
      lowerCAmelCase				:			Dict    			=      len(_UpperCAmelCase				)
  
      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
  
      lowerCAmelCase				:			Optional[Any]    			=      AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path,      num_labels=_UpperCAmelCase,      idalabel=_UpperCAmelCase,      labelaid={label: i for i, label in enumerate(_UpperCAmelCase				)},      cache_dir=model_args.cache_dir,      )
      lowerCAmelCase				:			Any    			=      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():
          lowerCAmelCase				:			Optional[int]    			=      TFAutoModelForTokenClassification.from_pretrained(
              model_args.model_name_or_path,      from_pt=bool('.bin' in model_args.model_name_or_path				),      config=_UpperCAmelCase,      cache_dir=model_args.cache_dir,      )
  
      # Get datasets
      lowerCAmelCase				:			str    			=      (
          TFTokenClassificationDataset(
              token_classification_task=_UpperCAmelCase,      data_dir=data_args.data_dir,      tokenizer=_UpperCAmelCase,      labels=_UpperCAmelCase,      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
      )
      lowerCAmelCase				:			List[str]    			=      (
          TFTokenClassificationDataset(
              token_classification_task=_UpperCAmelCase,      data_dir=data_args.data_dir,      tokenizer=_UpperCAmelCase,      labels=_UpperCAmelCase,      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(_UpperCAmelCase,      _UpperCAmelCase				) -> Tuple[List[int], List[int]]:
          lowerCAmelCase				:			Optional[Any]    			=      np.argmax(_UpperCAmelCase,      axis=2				)
          lowerCAmelCase			,  lowerCAmelCase				:			Tuple    			=      preds.shape
          lowerCAmelCase				:			Tuple    			=      [[] for _ in range(_UpperCAmelCase				)]
          lowerCAmelCase				:			Optional[int]    			=      [[] for _ in range(_UpperCAmelCase				)]
  
          for i in range(_UpperCAmelCase				):
              for j in range(_UpperCAmelCase				):
                  if label_ids[i, j] != -100:
                      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(_UpperCAmelCase				) -> Dict:
          lowerCAmelCase			,  lowerCAmelCase				:			str    			=      align_predictions(p.predictions,      p.label_ids				)
  
          return {
              "precision": precision_score(_UpperCAmelCase,      _UpperCAmelCase				),
              "recall": recall_score(_UpperCAmelCase,      _UpperCAmelCase				),
              "f1": fa_score(_UpperCAmelCase,      _UpperCAmelCase				),
          }
  
      # Initialize our Trainer
      lowerCAmelCase				:			Tuple    			=      TFTrainer(
          model=_UpperCAmelCase,      args=_UpperCAmelCase,      train_dataset=train_dataset.get_dataset() if train_dataset else None,      eval_dataset=eval_dataset.get_dataset() if eval_dataset else None,      compute_metrics=_UpperCAmelCase,      )
  
      # Training
      if training_args.do_train:
          trainer.train()
          trainer.save_model()
          tokenizer.save_pretrained(training_args.output_dir				)
  
      # Evaluation
      lowerCAmelCase				:			List[str]    			=      {}
      if training_args.do_eval:
          logger.info('*** Evaluate ***'				)
  
          lowerCAmelCase				:			Tuple    			=      trainer.evaluate()
          lowerCAmelCase				:			Optional[int]    			=      os.path.join(training_args.output_dir,      'eval_results.txt'				)
  
          with open(_UpperCAmelCase,      'w'				) as writer:
              logger.info('***** Eval results *****'				)
  
              for key, value in result.items():
                  logger.info('  %s = %s',      _UpperCAmelCase,      _UpperCAmelCase				)
                  writer.write('%s = %s\n' % (key, value)				)
  
              results.update(_UpperCAmelCase				)
  
      # Predict
      if training_args.do_predict:
          lowerCAmelCase				:			List[Any]    			=      TFTokenClassificationDataset(
              token_classification_task=_UpperCAmelCase,      data_dir=data_args.data_dir,      tokenizer=_UpperCAmelCase,      labels=_UpperCAmelCase,      model_type=config.model_type,      max_seq_length=data_args.max_seq_length,      overwrite_cache=data_args.overwrite_cache,      mode=Split.test,      )
  
          lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase				:			Optional[int]    			=      trainer.predict(test_dataset.get_dataset()				)
          lowerCAmelCase			,  lowerCAmelCase				:			List[str]    			=      align_predictions(_UpperCAmelCase,      _UpperCAmelCase				)
          lowerCAmelCase				:			int    			=      classification_report(_UpperCAmelCase,      _UpperCAmelCase				)
  
          logger.info('\n%s',      _UpperCAmelCase				)
  
          lowerCAmelCase				:			Optional[int]    			=      os.path.join(training_args.output_dir,      'test_results.txt'				)
  
          with open(_UpperCAmelCase,      'w'				) as writer:
              writer.write('%s\n' % report				)
  
          # Save predictions
          lowerCAmelCase				:			int    			=      os.path.join(training_args.output_dir,      'test_predictions.txt'				)
  
          with open(_UpperCAmelCase,      'w'				) as writer:
              with open(os.path.join(data_args.data_dir,      'test.txt'				),      'r'				) as f:
                  lowerCAmelCase				:			int    			=      0
  
                  for line in f:
                      if line.startswith('-DOCSTART-'				) or line == "" or line == "\n":
                          writer.write(_UpperCAmelCase				)
  
                          if not preds_list[example_id]:
                              example_id += 1
                      elif preds_list[example_id]:
                          lowerCAmelCase				:			Optional[int]    			=      line.split()[0] + ' ' + preds_list[example_id].pop(0				) + '\n'
  
                          writer.write(_UpperCAmelCase				)
                      else:
                          logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.',      line.split()[0]				)
  
      return results
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
  
  
style_context: 
  
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      )					-> tuple[list[list[int]], list[list[str]]]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Union[str, Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Optional[int]    			=      len(_UpperCAmelCase				)
      lowerCAmelCase				:			List[Any]    			=      len(_UpperCAmelCase				)
  
      lowerCAmelCase				:			str    			=      [
          [0 for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
      lowerCAmelCase				:			str    			=      [
          ['0' for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
  
      for i in range(1,      len_source_seq + 1				):
          lowerCAmelCase				:			int    			=      i * delete_cost
          lowerCAmelCase				:			List[str]    			=      f"D{source_seq[i - 1]:c}"
  
      for i in range(1,      len_destination_seq + 1				):
          lowerCAmelCase				:			Tuple    			=      i * insert_cost
          lowerCAmelCase				:			Union[str, Any]    			=      f"I{destination_seq[i - 1]:c}"
  
      for i in range(1,      len_source_seq + 1				):
          for j in range(1,      len_destination_seq + 1				):
              if source_seq[i - 1] == destination_seq[j - 1]:
                  lowerCAmelCase				:			str    			=      costs[i - 1][j - 1] + copy_cost
                  lowerCAmelCase				:			Tuple    			=      f"C{source_seq[i - 1]:c}"
              else:
                  lowerCAmelCase				:			Optional[int]    			=      costs[i - 1][j - 1] + replace_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]				)
  
              if costs[i - 1][j] + delete_cost < costs[i][j]:
                  lowerCAmelCase				:			List[str]    			=      costs[i - 1][j] + delete_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"D{source_seq[i - 1]:c}"
  
              if costs[i][j - 1] + insert_cost < costs[i][j]:
                  lowerCAmelCase				:			Union[str, Any]    			=      costs[i][j - 1] + insert_cost
                  lowerCAmelCase				:			str    			=      f"I{destination_seq[j - 1]:c}"
  
      return costs, ops
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> list[str]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if i == 0 and j == 0:
          return []
      else:
          if ops[i][j][0] == "C" or ops[i][j][0] == "R":
              lowerCAmelCase				:			List[str]    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
          elif ops[i][j][0] == "D":
              lowerCAmelCase				:			int    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      _UpperCAmelCase				)
              seq.append(ops[i][j]				)
              return seq
          else:
              lowerCAmelCase				:			Tuple    			=      assemble_transformation(_UpperCAmelCase,      _UpperCAmelCase,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
  
  
  if __name__ == "__main__":
   __A     ,      __A   :     List[str]      =			compute_transform_tables('''Python''', '''Algorithms''', -1, 1, 2, 2)
  
   __A   :     Optional[int]      =			len(operations)
   __A   :     Optional[Any]      =			len(operations[0])
   __A   :     Any      =			assemble_transformation(operations, m - 1, n - 1)
  
   __A   :     int      =			list('''Python''')
   __A   :     Tuple      =			0
   __A   :     Tuple      =			0
  
   with open('''min_cost.txt''', '''w''') as file:
    for op in sequence:
     print(''''''.join(string))
  
     if op[0] == "C":
      file.write('''%-16s''' % '''Copy %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost -= 1
     elif op[0] == "R":
      __A   :     Any      =			op[2]
  
      file.write('''%-16s''' % ('''Replace %c''' % op[1] + ''' with ''' + str(op[2])))
      file.write('''\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 1
     elif op[0] == "D":
      string.pop(i)
  
      file.write('''%-16s''' % '''Delete %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 2
     else:
      string.insert(i, op[1])
  
      file.write('''%-16s''' % '''Insert %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 2
  
     i += 1
  
    print(''''''.join(string))
    print('''Cost: ''', cost)
  
    file.write('''\r\nMinimum cost: ''' + str(cost))
  
  
  
  
  
  