





'''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


a__					:			Optional[Any]          =     logging.getLogger(__name__)






@dataclass
class 				__snake_case     :
	__lowerCAmelCase       						=  field(
	    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}							)
	__lowerCAmelCase       						=  field(
	    default=__magic_name__       ,       metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}							)
	__lowerCAmelCase       						=  field(
	    default='''NER'''       ,       metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}							)
	__lowerCAmelCase       						=  field(
	    default=__magic_name__       ,       metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}							)
	__lowerCAmelCase       						=  field(default=__magic_name__       ,       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       						=  field(
	    default=__magic_name__       ,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,       )






@dataclass
class 				__snake_case     :
	__lowerCAmelCase       						=  field(
	    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}							)
	__lowerCAmelCase       						=  field(
	    default=__magic_name__       ,       metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}       ,       )
	__lowerCAmelCase       						=  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       						=  field(
	    default=__magic_name__       ,       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.

	snake_case__	       =   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.
		snake_case__       ,  snake_case__       ,  snake_case__	       =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
	else:
		snake_case__       ,  snake_case__       ,  snake_case__	       =   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.'							)

	snake_case__	       =   import_module('tasks'							)
	try:
		snake_case__	       =   getattr(UpperCAmelCase_					,				model_args.task_type							)
		snake_case__	       =   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'					,				UpperCAmelCase_							)

	# Set seed
	set_seed(training_args.seed							)

	# Prepare CONLL-2003 task
	snake_case__	       =   token_classification_task.get_labels(data_args.labels							)
	snake_case__	       =   dict(enumerate(UpperCAmelCase_							)							)
	snake_case__	       =   len(UpperCAmelCase_							)

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

	snake_case__	       =   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					,				)
	snake_case__	       =   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					,				)
	snake_case__	       =   AutoModelForTokenClassification.from_pretrained(
	    model_args.model_name_or_path					,				from_tf=bool('.ckpt' in model_args.model_name_or_path							)					,				config=UpperCAmelCase_					,				cache_dir=model_args.cache_dir					,				)

	# Get datasets
	snake_case__	       =   (
	    TokenClassificationDataset(
	        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
	)
	snake_case__	       =   (
	    TokenClassificationDataset(
	        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]]:
		snake_case__	       =   np.argmax(UpperCAmelCase_					,				axis=2							)

		snake_case__       ,  snake_case__	       =   preds.shape

		snake_case__	       =   [[] for _ in range(UpperCAmelCase_							)]
		snake_case__	       =   [[] for _ in range(UpperCAmelCase_							)]

		for i in range(UpperCAmelCase_							):
			for j in range(UpperCAmelCase_							):
				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(UpperCAmelCase_							) -> Dict:
		snake_case__       ,  snake_case__	       =   align_predictions(p.predictions					,				p.label_ids							)
		return {
		    "accuracy_score": accuracy_score(UpperCAmelCase_					,				UpperCAmelCase_							),
		    "precision": precision_score(UpperCAmelCase_					,				UpperCAmelCase_							),
		    "recall": recall_score(UpperCAmelCase_					,				UpperCAmelCase_							),
		    "f1": fa_score(UpperCAmelCase_					,				UpperCAmelCase_							),
		}

	# Data collator
	snake_case__	       =   DataCollatorWithPadding(UpperCAmelCase_					,				pad_to_multiple_of=8							) if training_args.fpaa else None

	# Initialize our Trainer
	snake_case__	       =   Trainer(
	    model=UpperCAmelCase_					,				args=UpperCAmelCase_					,				train_dataset=UpperCAmelCase_					,				eval_dataset=UpperCAmelCase_					,				compute_metrics=UpperCAmelCase_					,				data_collator=UpperCAmelCase_					,				)

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

		snake_case__	       =   trainer.evaluate()

		snake_case__	       =   os.path.join(training_args.output_dir					,				'eval_results.txt'							)
		if trainer.is_world_process_zero():
			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:
		snake_case__	       =   TokenClassificationDataset(
		    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					,				)

		snake_case__       ,  snake_case__       ,  snake_case__	       =   trainer.predict(UpperCAmelCase_							)
		snake_case__       ,  snake_case__	       =   align_predictions(UpperCAmelCase_					,				UpperCAmelCase_							)

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

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

	return results




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Union[str, Any]:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
			main()










'''simple docstring'''




from math import loga




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->int:
	if a < 0:
		raise ValueError('Input value must be a positive integer'							)
	elif isinstance(UpperCAmelCase_					,				UpperCAmelCase_							):
		raise TypeError('Input value must be a \'int\' type'							)
	return 0 if (a == 0) else int(loga(a & -a							)							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



