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   : 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   : 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   : 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   : Tuple								= trainer.predict(test_dataset.get_dataset()					)
    __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: 
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast
  from ...utils import logging
  
  
  UpperCAmelCase_       :				int		   =      logging.get_logger(__name__)
  
  UpperCAmelCase_       :				Any		   =      {
      '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
      # See all PLBART models at https://huggingface.co/models?filter=plbart
  }
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      str    				=							'''plbart'''
  	_lowercase		:      str    				=							['''past_key_values''']
  	_lowercase		:      Any    				=							{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  
  
  
  
  	def __init__(    self	:    List[Any]				,			__A	:    str=5_0005				,			__A	:    Union[str, Any]=1024				,			__A	:    int=6				,			__A	:    Dict=3072				,			__A	:    Union[str, Any]=12				,			__A	:    Tuple=6				,			__A	:    Tuple=3072				,			__A	:    Optional[int]=12				,			__A	:    int=0.0				,			__A	:    List[Any]=0.0				,			__A	:    Union[str, Any]=True				,			__A	:    Any=True				,			__A	:    Optional[int]="gelu"				,			__A	:    Optional[int]=768				,			__A	:    Any=0.1				,			__A	:    Union[str, Any]=0.1				,			__A	:    Union[str, Any]=0.0				,			__A	:    int=0.0_2				,			__A	:    Union[str, Any]=0.0				,			__A	:    Tuple=True				,			__A	:    List[str]=1				,			__A	:    str=0				,			__A	:    List[Any]=2				,			__A	:    Any=2				,			**__A	:    Union[str, Any]				,			):
  		__A   : List[Any]								= vocab_size
  		__A   : str								= max_position_embeddings
  		__A   : Union[str, Any]								= d_model
  		__A   : List[str]								= encoder_ffn_dim
  		__A   : Optional[Any]								= encoder_layers
  		__A   : Tuple								= encoder_attention_heads
  		__A   : List[Any]								= decoder_ffn_dim
  		__A   : Optional[int]								= decoder_layers
  		__A   : Any								= decoder_attention_heads
  		__A   : Optional[int]								= dropout
  		__A   : Any								= attention_dropout
  		__A   : Tuple								= activation_dropout
  		__A   : List[Any]								= activation_function
  		__A   : Tuple								= init_std
  		__A   : List[Any]								= encoder_layerdrop
  		__A   : str								= decoder_layerdrop
  		__A   : Union[str, Any]								= classifier_dropout
  		__A   : Optional[Any]								= use_cache
  		__A   : List[str]								= encoder_layers
  		__A   : Optional[int]								= scale_embedding  # scale factor will be sqrt(d_model) if True
  		super().__init__(
  		    pad_token_id=__A				,			bos_token_id=__A				,			eos_token_id=__A				,			is_encoder_decoder=__A				,			forced_eos_token_id=__A				,			**__A				,			)
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Any       ):
  		return OrderedDict(
  		    [
  		        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
  		        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
  		    ]       )
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Tuple       ):
  		if self.use_past:
  			return OrderedDict(
  			    [
  			        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			        ("""past_keys""", {0: """batch""", 2: """sequence"""}),
  			        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			    ]       )
  		else:
  			return OrderedDict(
  			    [
  			        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			    ]       )
  
  
  
  
  
  