


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()


UpperCAmelCase__     			=							logging.getLogger(__name__)







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







@dataclass
class    lowerCamelCase__     :
					SCREAMING_SNAKE_CASE__       =    field(
					    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''})
					SCREAMING_SNAKE_CASE__       =    field(
					    metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''})
					SCREAMING_SNAKE_CASE__       =    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.'''
					        )
					    }     ,							)
					SCREAMING_SNAKE_CASE__       =    field(
					    default=lowerCAmelCase     ,							metadata={'''help''': '''Overwrite the cached training and evaluation sets'''})



def        UpperCAmelCase_      (							)  ->    Optional[int]:







	"""simple docstring"""


	_lowercase							   =HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
	_lowercase							   =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.'''		)

	_lowercase							   =import_module('''tasks'''		)

	try:
		_lowercase							   =getattr(_UpperCAmelCase  ,		model_args.task_type		)
		_lowercase							   =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
	_lowercase							   =token_classification_task.get_labels(data_args.labels		)
	_lowercase							   =dict(enumerate(_UpperCAmelCase		)		)
	_lowercase							   =len(_UpperCAmelCase		)

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

	_lowercase							   =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  ,		)
	_lowercase							   =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():
		_lowercase							   =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
	_lowercase							   =(
	    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
	)
	_lowercase							   =(
	    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(__snake_case  ,		__snake_case		) -> Tuple[List[int], List[int]]:
		_lowercase							   =np.argmax(_UpperCAmelCase  ,		axis=2		)
		_lowercase							   =preds.shape
		_lowercase							   =[[] for _ in range(_UpperCAmelCase		)]
		_lowercase							   =[[] 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(__snake_case		) -> Dict:
		_lowercase							   =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
	_lowercase							   =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
	_lowercase							   ={}
	if training_args.do_eval:
		logger.info('''*** Evaluate ***'''		)

		_lowercase							   =trainer.evaluate()
		_lowercase							   =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:
		_lowercase							   =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  ,		)

		_lowercase							   =trainer.predict(test_dataset.get_dataset()		)
		_lowercase							   =align_predictions(_UpperCAmelCase  ,		_UpperCAmelCase		)
		_lowercase							   =classification_report(_UpperCAmelCase  ,		_UpperCAmelCase		)

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

		_lowercase							   =os.path.join(training_args.output_dir  ,		'''test_results.txt'''		)

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

		# Save predictions
		_lowercase							   =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:
				_lowercase							   =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]:
						_lowercase							   =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()





def        UpperCAmelCase_      (							__snake_case		)  ->    bool:







 """simple docstring"""


 if not isinstance(__snake_case  ,		__snake_case		):
  _lowercase							   =F"Input value of [number={number}] must be an integer"
  raise TypeError(__snake_case		)
 if number < 0 or number % 2 == 1:
  return False
 _lowercase							   =int(number**0.5		)
 return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

