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()
  
  
  UpperCAmelCase_       :				Optional[Any]		   =      logging.getLogger(__name__)
  
  
  
  
  
  
  @dataclass
  class     lowerCamelCase_			:
  	_lowercase		:      str    				=							field(
  	    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}  )
  	_lowercase		:      Optional[str]    				=							field(
  	    default=_lowercase	,			metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
  	_lowercase		:      Optional[str]    				=							field(
  	    default='''NER'''	,			metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}  )
  	_lowercase		:      Optional[str]    				=							field(
  	    default=_lowercase	,			metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}  )
  	_lowercase		:      bool    				=							field(default=_lowercase	,			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.
  	_lowercase		:      Optional[str]    				=							field(
  	    default=_lowercase	,			metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}	,			)
  
  
  
  
  
  
  @dataclass
  class     lowerCamelCase_			:
  	_lowercase		:      str    				=							field(
  	    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}  )
  	_lowercase		:      Optional[str]    				=							field(
  	    metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}  )
  	_lowercase		:      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.'''
  	        )
  	    }	,			)
  	_lowercase		:      bool    				=							field(
  	    default=_lowercase	,			metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}  )
  
  
  
  
  
  
  def        __SCREAMING_SNAKE_CASE    (      )				->							Union[str, Any]:
  	# 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   : int								= HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)					)
  	__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   : List[str]								= import_module("""tasks"""					)
  
  	try:
  		__A   : int								= 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 ,)
  	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""" ,a__					)
  
  	# Prepare Token Classification task
  	__A   : Optional[Any]								= token_classification_task.get_labels(data_args.labels					)
  	__A   : Dict[int, str]								= dict(enumerate(a__					)					)
  	__A   : Optional[int]								= 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   : Tuple								= 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   : int								= 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   : str								= TFAutoModelForTokenClassification.from_pretrained(
  		    model_args.model_name_or_path ,from_pt=bool(""".bin""" in model_args.model_name_or_path					) ,config=a__ ,cache_dir=model_args.cache_dir ,)
  
  	# Get datasets
  	__A   : List[str]								= (
  	    TFTokenClassificationDataset(
  	        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   : Dict								= (
  	    TFTokenClassificationDataset(
  	        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__						:      np.ndarray ,a__						:      np.ndarray					) -> Tuple[List[int], List[int]]:
  		__A   : Optional[Any]								= np.argmax(a__ ,axis=2					)
  		__A						,							__A   : Any								= preds.shape
  		__A   : int								= [[] for _ in range(a__					)]
  		__A   : Dict								= [[] for _ in range(a__					)]
  
  		for i in range(a__					):
  			for j in range(a__					):
  				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(a__						:      EvalPrediction					) -> Dict:
  		__A						,							__A   : Union[str, Any]								= align_predictions(p.predictions ,p.label_ids					)
  
  		return {
  		    "precision": precision_score(a__ ,a__					),
  		    "recall": recall_score(a__ ,a__					),
  		    "f1": fa_score(a__ ,a__					),
  		}
  
  	# Initialize our Trainer
  	__A   : List[str]								= TFTrainer(
  	    model=a__ ,args=a__ ,train_dataset=train_dataset.get_dataset() if train_dataset else None ,eval_dataset=eval_dataset.get_dataset() if eval_dataset else None ,compute_metrics=a__ ,)
  
  	# Training
  	if training_args.do_train:
  		trainer.train()
  		trainer.save_model()
  		tokenizer.save_pretrained(training_args.output_dir					)
  
  	# Evaluation
  	__A   : List[str]								= {}
  	if training_args.do_eval:
  		logger.info("""*** Evaluate ***"""					)
  
  		__A   : int								= trainer.evaluate()
  		__A   : List[Any]								= os.path.join(training_args.output_dir ,"""eval_results.txt"""					)
  
  		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]								= TFTokenClassificationDataset(
  		    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   : Tuple								= trainer.predict(test_dataset.get_dataset()					)
  		__A						,							__A   : Tuple								= align_predictions(a__ ,a__					)
  		__A   : Any								= classification_report(a__ ,a__					)
  
  		logger.info("""\n%s""" ,a__					)
  
  		__A   : Any								= os.path.join(training_args.output_dir ,"""test_results.txt"""					)
  
  		with open(a__ ,"""w"""					) as writer:
  			writer.write("""%s\n""" % report					)
  
  		# Save predictions
  		__A   : Dict								= os.path.join(training_args.output_dir ,"""test_predictions.txt"""					)
  
  		with open(a__ ,"""w"""					) as writer:
  			with open(os.path.join(data_args.data_dir ,"""test.txt"""					) ,"""r"""					) as f:
  				__A   : int								= 0
  
  				for line in f:
  					if line.startswith("""-DOCSTART-"""					) or line == "" or line == "\n":
  						writer.write(a__					)
  
  						if not preds_list[example_id]:
  							example_id += 1
  					elif preds_list[example_id]:
  						__A   : Tuple								= line.split()[0] + """ """ + preds_list[example_id].pop(0					) + """\n"""
  
  						writer.write(a__					)
  					else:
  						logger.warning("""Maximum sequence length exceeded: No prediction for '%s'.""" ,line.split()[0]					)
  
  	return results
  
  
  if __name__ == "__main__":
  			main()
  
  
  
  
  
  
style_context: 
  
  import math
  
  
  
  
  
  
  class     lowerCamelCase_			:
  
  
  
  
  	def __init__(    self	:    Dict				,			__A	:    int				,			__A	:    int				,			__A	:    int				,			__A	:    Any       ):
  		__A   : Union[str, Any]								= x
  		__A   : Union[str, Any]								= y
  		__A   : Optional[int]								= step_size
  		__A   : Dict								= function_to_optimize
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
  		return self.function(self.x				,			self.y       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    int       ):
  		__A   : Dict								= self.step_size
  		return [
  		    SearchProblem(__A				,			__A				,			__A				,			self.function       )
  		    for x, y in (
  		        (self.x - step_size, self.y - step_size),
  		        (self.x - step_size, self.y),
  		        (self.x - step_size, self.y + step_size),
  		        (self.x, self.y - step_size),
  		        (self.x, self.y + step_size),
  		        (self.x + step_size, self.y - step_size),
  		        (self.x + step_size, self.y),
  		        (self.x + step_size, self.y + step_size),
  		    )
  		]
  
  
  
  
  	def __hash__(    self	:    str       ):
  		return hash(str(self       )       )
  
  
  
  
  	def __eq__(    self	:    Union[str, Any]				,			__A	:    Optional[int]       ):
  		if isinstance(__A				,			__A       ):
  			return hash(str(self       )       ) == hash(str(__A       )       )
  		return False
  
  
  
  
  	def __str__(    self	:    List[Any]       ):
  		return F"""x: {self.x} y: {self.y}"""
  
  
  
  
  
  
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[Any] ,a__						:      bool = True ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      bool = False ,a__						:      int = 10000 ,)				->							SearchProblem:
  	__A   : int								= search_prob
  	__A   : List[str]								= []  # list to store the current score at each iteration
  	__A   : Optional[Any]								= 0
  	__A   : Any								= False
  	__A   : Dict								= set()
  	while not solution_found and iterations < max_iter:
  		visited.add(a__					)
  		iterations += 1
  		__A   : Union[str, Any]								= current_state.score()
  		scores.append(a__					)
  		__A   : List[Any]								= current_state.get_neighbors()
  		__A   : Tuple								= -math.inf
  		__A   : str								= math.inf
  		__A   : Optional[int]								= None  # to hold the next best neighbor
  		for neighbor in neighbors:
  			if neighbor in visited:
  				continue  # do not want to visit the same state again
  			if (
  			    neighbor.x > max_x
  			    or neighbor.x < min_x
  			    or neighbor.y > max_y
  			    or neighbor.y < min_y
  			):
  				continue  # neighbor outside our bounds
  			__A   : int								= neighbor.score() - current_score
  			if find_max:  # finding max
  				# going to direction with greatest ascent
  				if change > max_change and change > 0:
  					__A   : str								= change
  					__A   : Optional[int]								= neighbor
  			else:  # finding min
  				# to direction with greatest descent
  				if change < min_change and change < 0:
  					__A   : List[Any]								= change
  					__A   : List[str]								= neighbor
  		if next_state is not None:
  			# we found at least one neighbor which improved the current state
  			__A   : Tuple								= next_state
  		else:
  			# since we have no neighbor that improves the solution we stop the search
  			__A   : str								= True
  
  	if visualization:
  		from matplotlib import pyplot as plt
  
  		plt.plot(range(a__					) ,a__					)
  		plt.xlabel("""Iterations"""					)
  		plt.ylabel("""Function values"""					)
  		plt.show()
  
  	return current_state
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  			def        __SCREAMING_SNAKE_CASE    (      a__						:      str ,a__						:      Any					)				->							Dict:
  				return (x**2) + (y**2)
  
  			# starting the problem with initial coordinates (3, 4)
  			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  			UpperCAmelCase_       :				Any		   =      hill_climbing(prob, find_max=False)
  			print(
  			    '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
  			    f"""{local_min.score()}"""
  			)
  
  			# starting the problem with initial coordinates (12, 47)
  			UpperCAmelCase_       :				List[Any]		   =      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  			UpperCAmelCase_       :				Any		   =      hill_climbing(
  			    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
  			)
  			print(
  			    '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
  			    f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
  			)
  			def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      List[str]					)				->							Any:
  				return (3 * x**2) - (6 * y)
  
  			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  			UpperCAmelCase_       :				Dict		   =      hill_climbing(prob, find_max=True)
  
  
  
  
  
  
  
  			print(
  			    '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
  			    f"""{local_min.score()}"""
  			)
  
  
  
  
  
  