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_    :					Dict             =logging.getLogger(__name__)
  
  
  
  
  
  @dataclass
  class        lowercase_				:
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		field(
  							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
  							snake_case_        =		field(
  							    default=UpperCamelCase__			,metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''})
  							snake_case_        =		field(
  							    default='''NER'''			,metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''})
  							snake_case_        =		field(
  							    default=UpperCamelCase__			,metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''})
  							snake_case_        =		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.
  							snake_case_        =		field(
  							    default=UpperCamelCase__			,metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,)
  
  
  
  
  
  @dataclass
  class        lowercase_				:
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		field(
  							    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''})
  							snake_case_        =		field(
  							    metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''})
  							snake_case_        =		field(
  							    default=1_28			,metadata={
  							        '''help''': (
  							            '''The maximum total input sequence length after tokenization. Sequences longer '''
  							            '''than this will be truncated, sequences shorter will be padded.'''
  							        )
  							    }			,)
  							snake_case_        =		field(
  							    default=UpperCamelCase__			,metadata={'''help''': '''Overwrite the cached training and evaluation sets'''})
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""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(UpperCAmelCase__       ,    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"""       ,    UpperCAmelCase__      )
  
  						# Prepare Token Classification task
  						a_					=		token_classification_task.get_labels(data_args.labels      )
  						a_					=		dict(enumerate(UpperCAmelCase__      )      )
  						a_					=		len(UpperCAmelCase__      )
  
  						# 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=UpperCAmelCase__       ,    idalabel=UpperCAmelCase__       ,    labelaid={label: i for i, label in enumerate(UpperCAmelCase__      )}       ,    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=UpperCAmelCase__       ,    cache_dir=model_args.cache_dir       ,    )
  
  						# Get datasets
  						a_					=		(
  						    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
  						)
  						a_					=		(
  						    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]]:
  												a_					=		np.argmax(UpperCAmelCase__       ,    axis=2      )
  												a_			,   a_					=		preds.shape
  												a_					=		[[] for _ in range(UpperCAmelCase__      )]
  												a_					=		[[] 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:
  												a_			,   a_					=		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
  						a_					=		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
  						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(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:
  												a_					=		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       ,    )
  
  												a_			,   a_			,   a_					=		trainer.predict(test_dataset.get_dataset()      )
  												a_			,   a_					=		align_predictions(UpperCAmelCase__       ,    UpperCAmelCase__      )
  												a_					=		classification_report(UpperCAmelCase__       ,    UpperCAmelCase__      )
  
  												logger.info("""\n%s"""       ,    UpperCAmelCase__      )
  
  												a_					=		os.path.join(training_args.output_dir       ,    """test_results.txt"""      )
  
  												with open(UpperCAmelCase__       ,    """w"""      ) as writer:
  																		writer.write("""%s\n""" % report      )
  
  												# Save predictions
  												a_					=		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:
  																								a_					=		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]:
  																																				a_					=		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: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  A_    :					Optional[Any]             ={
      """configuration_wav2vec2_conformer""": [
          """WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
          """Wav2Vec2ConformerConfig""",
      ],
  }
  
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	A_    :					Union[str, Any]             =[
  	    """WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
  	    """Wav2Vec2ConformerForAudioFrameClassification""",
  	    """Wav2Vec2ConformerForCTC""",
  	    """Wav2Vec2ConformerForPreTraining""",
  	    """Wav2Vec2ConformerForSequenceClassification""",
  	    """Wav2Vec2ConformerForXVector""",
  	    """Wav2Vec2ConformerModel""",
  	    """Wav2Vec2ConformerPreTrainedModel""",
  	]
  
  if TYPE_CHECKING:
  	from .configuration_wavaveca_conformer import (
  	    WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
  	    WavaVecaConformerConfig,
  	)
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_wavaveca_conformer import (
  		    WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    WavaVecaConformerForAudioFrameClassification,
  		    WavaVecaConformerForCTC,
  		    WavaVecaConformerForPreTraining,
  		    WavaVecaConformerForSequenceClassification,
  		    WavaVecaConformerForXVector,
  		    WavaVecaConformerModel,
  		    WavaVecaConformerPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	A_    :					Union[str, Any]             =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)