


'''simple docstring'''




import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_lowerCAmelCase  :		int		     =	logging.getLogger(__name__)



@dataclass
class  lowerCAmelCase				:
	_lowerCamelCase   :					str			      = field(
	    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
	_lowerCamelCase   :					Optional[str]			      = field(
	    default=a      ,				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=a      ,				metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
	_lowerCamelCase   :					bool			      = field(default=a      ,				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=a      ,				metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,				)



@dataclass
class  lowerCAmelCase				:
	_lowerCamelCase   :					str			      = field(
	    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}							)
	_lowerCamelCase   :					Optional[str]			      = field(
	    default=a      ,				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=a      ,				metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)


def 			__UpperCamelCase    (    )    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
	if len(sys.argv				) == 2 and sys.argv[1].endswith('.json'				):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    List[str]      							= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
	else:
		lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    Optional[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					:    Dict      							= import_module('tasks'				)
	try:
		lowerCAmelCase					:    List[str]      							= getattr(_A					,   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 if training_args.local_rank in [-1, 0] else logging.WARN					,   )
	logger.warning(
	    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'					,   training_args.local_rank					,   training_args.device					,   training_args.n_gpu					,   bool(training_args.local_rank != -1				)					,   training_args.fpaa					,   )
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank				):
		transformers.utils.logging.set_verbosity_info()
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
	logger.info('Training/evaluation parameters %s'					,   _A				)

	# Set seed
	set_seed(training_args.seed				)

	# Prepare CONLL-2003 task
	lowerCAmelCase					:    Optional[Any]      							= token_classification_task.get_labels(data_args.labels				)
	lowerCAmelCase					:    Dict[int, str]      							= dict(enumerate(_A				)				)
	lowerCAmelCase					:    Optional[Any]      							= len(_A				)

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	lowerCAmelCase					:    List[str]      							= 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					,   )
	lowerCAmelCase					:    Tuple      							= 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					,   )
	lowerCAmelCase					:    Optional[Any]      							= AutoModelForTokenClassification.from_pretrained(
	    model_args.model_name_or_path					,   from_tf=bool('.ckpt' in model_args.model_name_or_path				)					,   config=_A					,   cache_dir=model_args.cache_dir					,   )

	# Get datasets
	lowerCAmelCase					:    Dict      							= (
	    TokenClassificationDataset(
	        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
	)
	lowerCAmelCase					:    Union[str, Any]      							= (
	    TokenClassificationDataset(
	        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]]:
		lowerCAmelCase					:    List[Any]      							= np.argmax(_A					,   axis=2				)

		lowerCAmelCase							,   lowerCAmelCase					:    List[Any]      							= preds.shape

		lowerCAmelCase					:    str      							= [[] for _ in range(_A				)]
		lowerCAmelCase					:    Optional[int]      							= [[] for _ in range(_A				)]

		for i in range(_A				):
			for j in range(_A				):
				if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
					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:
		lowerCAmelCase							,   lowerCAmelCase					:    Optional[Any]      							= align_predictions(p.predictions					,   p.label_ids				)
		return {
		    "accuracy_score": accuracy_score(_A					,   _A				),
		    "precision": precision_score(_A					,   _A				),
		    "recall": recall_score(_A					,   _A				),
		    "f1": fa_score(_A					,   _A				),
		}

	# Data collator
	lowerCAmelCase					:    Optional[int]      							= DataCollatorWithPadding(_A					,   pad_to_multiple_of=8				) if training_args.fpaa else None

	# Initialize our Trainer
	lowerCAmelCase					:    List[str]      							= Trainer(
	    model=_A					,   args=_A					,   train_dataset=_A					,   eval_dataset=_A					,   compute_metrics=_A					,   data_collator=_A					,   )

	# Training
	if training_args.do_train:
		trainer.train(
		    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
		trainer.save_model()
		# For convenience, we also re-save the tokenizer to the same directory,
		# so that you can share your model easily on huggingface.co/models =)
		if trainer.is_world_process_zero():
			tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
	lowerCAmelCase					:    str      							= {}
	if training_args.do_eval:
		logger.info('*** Evaluate ***'				)

		lowerCAmelCase					:    Union[str, Any]      							= trainer.evaluate()

		lowerCAmelCase					:    Optional[int]      							= os.path.join(training_args.output_dir					,   'eval_results.txt'				)
		if trainer.is_world_process_zero():
			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:
		lowerCAmelCase					:    Optional[Any]      							= TokenClassificationDataset(
		    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					,   )

		lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    Any      							= trainer.predict(_A				)
		lowerCAmelCase							,   lowerCAmelCase					:    List[Any]      							= align_predictions(_A					,   _A				)

		lowerCAmelCase					:    Optional[Any]      							= os.path.join(training_args.output_dir					,   'test_results.txt'				)
		if trainer.is_world_process_zero():
			with open(_A					,   'w'				) as writer:
				for key, value in metrics.items():
					logger.info('  %s = %s'					,   _A					,   _A				)
					writer.write('%s = %s\n' % (key, value)				)

        # Save predictions
		lowerCAmelCase					:    List[Any]      							= os.path.join(training_args.output_dir					,   'test_predictions.txt'				)
		if trainer.is_world_process_zero():
			with open(_A					,   'w'				) as writer:
				with open(os.path.join(data_args.data_dir					,   'test.txt'				)					,   'r'				) as f:
					token_classification_task.write_predictions_to_file(_A					,   _A					,   _A				)

	return results

def 			__UpperCamelCase    (    _A    :				Optional[Any]				)    ->      int:




	"""simple docstring"""





	main()


if __name__ == "__main__":
				main()




'''simple docstring'''




def 			__UpperCamelCase    (    _A    :				int				)    ->      bool:




	"""simple docstring"""





	return number & 1 == 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()
