


"""simple docstring"""
import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput



@dataclass
class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

		lowerCamelCase__           =42
		lowerCamelCase__           =None
		lowerCamelCase__           =None



class      _UpperCAmelCase					(				unittest.TestCase							):






		'''simple docstring'''

		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							ModelOutputTest(a=30   )
			self.assertEqual(x.a							,			30   )
			self.assertIsNone(x.b   )
			self.assertIsNone(x.c   )
			with self.assertRaises(a_   ):
				__snake_case      :		str         =							x.d



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							ModelOutputTest(a=30							,			b=10   )
			self.assertEqual(x[0]							,			30   )
			self.assertEqual(x[1]							,			10   )
			self.assertEqual(x[:2]							,			(30, 10)   )
			self.assertEqual(x[:]							,			(30, 10)   )

			__snake_case      :		List[Any]         =							ModelOutputTest(a=30							,			c=10   )
			self.assertEqual(x[0]							,			30   )
			self.assertEqual(x[1]							,			10   )
			self.assertEqual(x[:2]							,			(30, 10)   )
			self.assertEqual(x[:]							,			(30, 10)   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Optional[int]         =							ModelOutputTest(a=30							,			b=10   )
			self.assertEqual(x['''a''']							,			30   )
			self.assertEqual(x['''b''']							,			10   )
			with self.assertRaises(a_   ):
				__snake_case      :		Optional[int]         =							x['''c''']

			__snake_case      :		Any         =							ModelOutputTest(a=30							,			c=10   )
			self.assertEqual(x['''a''']							,			30   )
			self.assertEqual(x['''c''']							,			10   )
			with self.assertRaises(a_   ):
				__snake_case      :		Optional[int]         =							x['''b''']



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Optional[int]         =							ModelOutputTest(a=30   )
			self.assertEqual(list(x.keys()   )							,			['''a''']   )
			self.assertEqual(list(x.values()   )							,			[30]   )
			self.assertEqual(list(x.items()   )							,			[('''a''', 30)]   )
			self.assertEqual(list(a_   )							,			['''a''']   )

			__snake_case      :		Optional[int]         =							ModelOutputTest(a=30							,			b=10   )
			self.assertEqual(list(x.keys()   )							,			['''a''', '''b''']   )
			self.assertEqual(list(x.values()   )							,			[30, 10]   )
			self.assertEqual(list(x.items()   )							,			[('''a''', 30), ('''b''', 10)]   )
			self.assertEqual(list(a_   )							,			['''a''', '''b''']   )

			__snake_case      :		Optional[Any]         =							ModelOutputTest(a=30							,			c=10   )
			self.assertEqual(list(x.keys()   )							,			['''a''', '''c''']   )
			self.assertEqual(list(x.values()   )							,			[30, 10]   )
			self.assertEqual(list(x.items()   )							,			[('''a''', 30), ('''c''', 10)]   )
			self.assertEqual(list(a_   )							,			['''a''', '''c''']   )

			with self.assertRaises(a_   ):
				__snake_case      :		Optional[int]         =							x.update({'''d''': 20}   )
			with self.assertRaises(a_   ):
				del x["a"]
			with self.assertRaises(a_   ):
				__snake_case      :		Union[str, Any]         =							x.pop('''a'''   )
			with self.assertRaises(a_   ):
				__snake_case      :		Optional[Any]         =							x.setdefault('''d'''							,			32   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[str]         =							ModelOutputTest(a=30   )
			__snake_case      :		Tuple         =							10
			self.assertEqual(x.a							,			10   )
			self.assertEqual(x['''a''']							,			10   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[str]         =							ModelOutputTest(a=30   )
			__snake_case      :		Any         =							10
			self.assertEqual(x.a							,			10   )
			self.assertEqual(x['''a''']							,			10   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		int         =							ModelOutputTest({'''a''': 30, '''b''': 10}   )
			self.assertEqual(list(x.keys()   )							,			['''a''', '''b''']   )
			self.assertEqual(x.a							,			30   )
			self.assertEqual(x.b							,			10   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							ModelOutputTest([('''a''', 30), ('''b''', 10)]   )
			self.assertEqual(list(x.keys()   )							,			['''a''', '''b''']   )
			self.assertEqual(x.a							,			30   )
			self.assertEqual(x.b							,			10   )

			with self.assertRaises(a_   ):
				__snake_case      :		List[Any]         =							ModelOutputTest([('''a''', 30), (10, 10)]   )

			__snake_case      :		Union[str, Any]         =							ModelOutputTest(a=(30, 30)   )
			self.assertEqual(list(x.keys()   )							,			['''a''']   )
			self.assertEqual(x.a							,			(30, 30)   )









"""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()


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



@dataclass
class      _UpperCAmelCase					:






		'''simple docstring'''

		lowerCamelCase__           =field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}							)
		lowerCamelCase__           =field(
		    default=__snake_case,							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=__snake_case,							metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}							)
		lowerCamelCase__           =field(default=__snake_case,							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=__snake_case,							metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'},							)



@dataclass
class      _UpperCAmelCase					:






		'''simple docstring'''

		lowerCamelCase__           =field(
		    metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}							)
		lowerCamelCase__           =field(
		    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=__snake_case,							metadata={'help': 'Overwrite the cached training and evaluation sets'}							)






def 						lowercase				(	)	->Dict:
	"""simple docstring"""





	__snake_case      :		str         =							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
	__snake_case      ,				__snake_case      ,				__snake_case      :		Dict         =							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      :		List[Any]         =							import_module('''tasks'''	)

	try:
		__snake_case      :		Optional[int]         =							getattr(_snake_case	,  model_args.task_type	)
		__snake_case      :		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'''	,  _snake_case	)

	# Prepare Token Classification task
	__snake_case      :		Union[str, Any]         =							token_classification_task.get_labels(data_args.labels	)
	__snake_case      :		Dict[int, str]         =							dict(enumerate(_snake_case	)	)
	__snake_case      :		List[Any]         =							len(_snake_case	)

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

	__snake_case      :		Any         =							AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,  num_labels=_snake_case	,  idalabel=_snake_case	,  labelaid={label: i for i, label in enumerate(_snake_case	)}	,  cache_dir=model_args.cache_dir	,  )
	__snake_case      :		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	,  )

	with training_args.strategy.scope():
		__snake_case      :		Optional[int]         =							TFAutoModelForTokenClassification.from_pretrained(
		    model_args.model_name_or_path	,  from_pt=bool('''.bin''' in model_args.model_name_or_path	)	,  config=_snake_case	,  cache_dir=model_args.cache_dir	,  )

	# Get datasets
	__snake_case      :		Any         =							(
	    TFTokenClassificationDataset(
	        token_classification_task=_snake_case	,  data_dir=data_args.data_dir	,  tokenizer=_snake_case	,  labels=_snake_case	,  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      :		List[Any]         =							(
	    TFTokenClassificationDataset(
	        token_classification_task=_snake_case	,  data_dir=data_args.data_dir	,  tokenizer=_snake_case	,  labels=_snake_case	,  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(_snake_case		: np.ndarray	,  _snake_case		: np.ndarray	) -> Tuple[List[int], List[int]]:
		__snake_case      :		List[Any]         =							np.argmax(_snake_case	,  axis=2	)
		__snake_case      ,				__snake_case      :		Dict         =							preds.shape
		__snake_case      :		Union[str, Any]         =							[[] for _ in range(_snake_case	)]
		__snake_case      :		str         =							[[] for _ in range(_snake_case	)]

		for i in range(_snake_case	):
			for j in range(_snake_case	):
				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(_snake_case		: EvalPrediction	) -> Dict:
		__snake_case      ,				__snake_case      :		Optional[int]         =							align_predictions(p.predictions	,  p.label_ids	)

		return {
		    "precision": precision_score(_snake_case	,  _snake_case	),
		    "recall": recall_score(_snake_case	,  _snake_case	),
		    "f1": fa_score(_snake_case	,  _snake_case	),
		}

	# Initialize our Trainer
	__snake_case      :		Optional[Any]         =							TFTrainer(
	    model=_snake_case	,  args=_snake_case	,  train_dataset=train_dataset.get_dataset() if train_dataset else None	,  eval_dataset=eval_dataset.get_dataset() if eval_dataset else None	,  compute_metrics=_snake_case	,  )

	# Training
	if training_args.do_train:
		trainer.train()
		trainer.save_model()
		tokenizer.save_pretrained(training_args.output_dir	)

	# Evaluation
	__snake_case      :		Union[str, Any]         =							{}
	if training_args.do_eval:
		logger.info('''*** Evaluate ***'''	)

		__snake_case      :		int         =							trainer.evaluate()
		__snake_case      :		List[Any]         =							os.path.join(training_args.output_dir	,  '''eval_results.txt'''	)

		with open(_snake_case	,  '''w'''	) as writer:
			logger.info('''***** Eval results *****'''	)

			for key, value in result.items():
				logger.info('''  %s = %s'''	,  _snake_case	,  _snake_case	)
				writer.write('''%s = %s\n''' % (key, value)	)

			results.update(_snake_case	)

    # Predict
	if training_args.do_predict:
		__snake_case      :		Optional[Any]         =							TFTokenClassificationDataset(
		    token_classification_task=_snake_case	,  data_dir=data_args.data_dir	,  tokenizer=_snake_case	,  labels=_snake_case	,  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      :		str         =							trainer.predict(test_dataset.get_dataset()	)
		__snake_case      ,				__snake_case      :		Optional[int]         =							align_predictions(_snake_case	,  _snake_case	)
		__snake_case      :		Dict         =							classification_report(_snake_case	,  _snake_case	)

		logger.info('''\n%s'''	,  _snake_case	)

		__snake_case      :		List[str]         =							os.path.join(training_args.output_dir	,  '''test_results.txt'''	)

		with open(_snake_case	,  '''w'''	) as writer:
			writer.write('''%s\n''' % report	)

		# Save predictions
		__snake_case      :		List[str]         =							os.path.join(training_args.output_dir	,  '''test_predictions.txt'''	)

		with open(_snake_case	,  '''w'''	) as writer:
			with open(os.path.join(data_args.data_dir	,  '''test.txt'''	)	,  '''r'''	) as f:
				__snake_case      :		Union[str, Any]         =							0

				for line in f:
					if line.startswith('''-DOCSTART-'''	) or line == "" or line == "\n":
						writer.write(_snake_case	)

						if not preds_list[example_id]:
							example_id += 1
					elif preds_list[example_id]:
						__snake_case      :		List[str]         =							line.split()[0] + ''' ''' + preds_list[example_id].pop(0	) + '''\n'''

						writer.write(_snake_case	)
					else:
						logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''	,  line.split()[0]	)

	return results


if __name__ == "__main__":
			main()





