code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  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      :				Union[str, Any]											=					logging.getLogger(__name__)
  
  
  
  
  
  @dataclass
  class 					_UpperCAmelCase							:
  		a     :     str     						=field(
  		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}     )
  		a     :     Optional[str]     						=field(
  		    default=lowerCAmelCase_			,      metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}     )
  		a     :     Optional[str]     						=field(
  		    default="""NER"""			,      metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}     )
  		a     :     Optional[str]     						=field(
  		    default=lowerCAmelCase_			,      metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}     )
  		a     :     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.
  		a     :     Optional[str]     						=field(
  		    default=lowerCAmelCase_			,      metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,      )
  
  
  
  
  
  @dataclass
  class 					_UpperCAmelCase							:
  		a     :     str     						=field(
  		    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}     )
  		a     :     Optional[str]     						=field(
  		    metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}     )
  		a     :     int     						=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."""
  		        )
  		    }			,      )
  		a     :     bool     						=field(
  		    default=lowerCAmelCase_			,      metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      int:
  			# 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.
  			__lowerCAmelCase      		=       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)			)
  			__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       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      		=       import_module("""tasks"""			)
  
  			try:
  						__lowerCAmelCase      		=       getattr(lowercase      ,			model_args.task_type			)
  						__lowerCAmelCase      		=       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"""      ,			lowercase			)
  
  			# Prepare Token Classification task
  			__lowerCAmelCase      		=       token_classification_task.get_labels(data_args.labels			)
  			__lowerCAmelCase      		=       dict(enumerate(lowercase			)			)
  			__lowerCAmelCase      		=       len(lowercase			)
  
  			# Load pretrained model and tokenizer
  			#
  			# Distributed training:
  			# The .from_pretrained methods guarantee that only one local process can concurrently
  			# download model & vocab.
  
  			__lowerCAmelCase      		=       AutoConfig.from_pretrained(
  			    model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,			num_labels=lowercase      ,			idalabel=lowercase      ,			labelaid={label: i for i, label in enumerate(lowercase			)}      ,			cache_dir=model_args.cache_dir      ,			)
  			__lowerCAmelCase      		=       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      		=       TFAutoModelForTokenClassification.from_pretrained(
  						    model_args.model_name_or_path      ,			from_pt=bool(""".bin""" in model_args.model_name_or_path			)      ,			config=lowercase      ,			cache_dir=model_args.cache_dir      ,			)
  
  			# Get datasets
  			__lowerCAmelCase      		=       (
  			    TFTokenClassificationDataset(
  			        token_classification_task=lowercase      ,			data_dir=data_args.data_dir      ,			tokenizer=lowercase      ,			labels=lowercase      ,			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      		=       (
  			    TFTokenClassificationDataset(
  			        token_classification_task=lowercase      ,			data_dir=data_args.data_dir      ,			tokenizer=lowercase      ,			labels=lowercase      ,			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(lowercase      ,			lowercase			) -> Tuple[List[int], List[int]]:
  						__lowerCAmelCase      		=       np.argmax(lowercase      ,			axis=2			)
  						__lowerCAmelCase		, __lowerCAmelCase      		=       preds.shape
  						__lowerCAmelCase      		=       [[] for _ in range(lowercase			)]
  						__lowerCAmelCase      		=       [[] for _ in range(lowercase			)]
  
  						for i in range(lowercase			):
  									for j in range(lowercase			):
  												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(lowercase			) -> Dict:
  						__lowerCAmelCase		, __lowerCAmelCase      		=       align_predictions(p.predictions      ,			p.label_ids			)
  
  						return {
  						    "precision": precision_score(lowercase      ,			lowercase			),
  						    "recall": recall_score(lowercase      ,			lowercase			),
  						    "f1": fa_score(lowercase      ,			lowercase			),
  						}
  
  			# Initialize our Trainer
  			__lowerCAmelCase      		=       TFTrainer(
  			    model=lowercase      ,			args=lowercase      ,			train_dataset=train_dataset.get_dataset() if train_dataset else None      ,			eval_dataset=eval_dataset.get_dataset() if eval_dataset else None      ,			compute_metrics=lowercase      ,			)
  
  			# Training
  			if training_args.do_train:
  						trainer.train()
  						trainer.save_model()
  						tokenizer.save_pretrained(training_args.output_dir			)
  
  			# Evaluation
  			__lowerCAmelCase      		=       {}
  			if training_args.do_eval:
  						logger.info("""*** Evaluate ***"""			)
  
  						__lowerCAmelCase      		=       trainer.evaluate()
  						__lowerCAmelCase      		=       os.path.join(training_args.output_dir      ,			"""eval_results.txt"""			)
  
  						with open(lowercase      ,			"""w"""			) as writer:
  									logger.info("""***** Eval results *****"""			)
  
  									for key, value in result.items():
  												logger.info("""  %s = %s"""      ,			lowercase      ,			lowercase			)
  												writer.write("""%s = %s\n""" % (key, value)			)
  
  									results.update(lowercase			)
  
      # Predict
  			if training_args.do_predict:
  						__lowerCAmelCase      		=       TFTokenClassificationDataset(
  						    token_classification_task=lowercase      ,			data_dir=data_args.data_dir      ,			tokenizer=lowercase      ,			labels=lowercase      ,			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      		=       trainer.predict(test_dataset.get_dataset()			)
  						__lowerCAmelCase		, __lowerCAmelCase      		=       align_predictions(lowercase      ,			lowercase			)
  						__lowerCAmelCase      		=       classification_report(lowercase      ,			lowercase			)
  
  						logger.info("""\n%s"""      ,			lowercase			)
  
  						__lowerCAmelCase      		=       os.path.join(training_args.output_dir      ,			"""test_results.txt"""			)
  
  						with open(lowercase      ,			"""w"""			) as writer:
  									writer.write("""%s\n""" % report			)
  
  						# Save predictions
  						__lowerCAmelCase      		=       os.path.join(training_args.output_dir      ,			"""test_predictions.txt"""			)
  
  						with open(lowercase      ,			"""w"""			) as writer:
  									with open(os.path.join(data_args.data_dir      ,			"""test.txt"""			)      ,			"""r"""			) as f:
  												__lowerCAmelCase      		=       0
  
  												for line in f:
  															if line.startswith("""-DOCSTART-"""			) or line == "" or line == "\n":
  																		writer.write(lowercase			)
  
  																		if not preds_list[example_id]:
  																					example_id += 1
  															elif preds_list[example_id]:
  																		__lowerCAmelCase      		=       line.split()[0] + """ """ + preds_list[example_id].pop(0			) + """\n"""
  
  																		writer.write(lowercase			)
  															else:
  																		logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""      ,			line.split()[0]			)
  
  			return results
  
  
  if __name__ == "__main__":
  	main()
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterable, Iterator
  from typing import Any, Generic, TypeVar
  
  _a      :				Dict											=					TypeVar("""T""", bound=bool)
  
  
  
  
  
  class 					_UpperCAmelCase							(		Generic[T]     ):
  		def __init__(						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       value
  					__lowerCAmelCase      		=       None
  					__lowerCAmelCase      		=       None
  		@property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return self._value
  
  
  
  
  
  
  
  		@staticmethod
  		def 						lowerCamelCase__    (						__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					if not roota:
  								return roota
  
  					if not roota:
  								return roota
  
  					if roota.value > roota.value:
  								__lowerCAmelCase		, __lowerCAmelCase      		=       roota, roota
  
  					__lowerCAmelCase      		=       roota
  					__lowerCAmelCase      		=       roota.right
  					__lowerCAmelCase      		=       roota.left
  					__lowerCAmelCase      		=       SkewNode.merge(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  
  					return result
  
  
  
  
  
  class 					_UpperCAmelCase							(		Generic[T]     ):
  		def __init__(						self,__SCREAMING_SNAKE_CASE = ()    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       None
  					if data:
  								for item in data:
  											self.insert(__SCREAMING_SNAKE_CASE    )
  		def __bool__(						self    ):
  
  
  
  					'''simple docstring'''
  
  					return self._root is not None
  		def __iter__(						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       []
  					while self:
  								result.append(self.pop()    )
  
  					# Pushing items back to the heap not to clear it.
  					for item in result:
  								self.insert(__SCREAMING_SNAKE_CASE    )
  
  					return iter(__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       SkewNode.merge(self._root,SkewNode(__SCREAMING_SNAKE_CASE    )    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.top()
  					__lowerCAmelCase      		=       (
  					    SkewNode.merge(self._root.left,self._root.right    ) if self._root else None
  					)
  
  					return result
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					if not self._root:
  								raise IndexError("""Can't get top element for the empty heap."""    )
  					return self._root.value
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       None
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  