


"""simple docstring"""
import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast






def 						lowercase				(	_snake_case		: List[Any]	,  _snake_case		: List[Any]=100	,  _snake_case		: Dict=" "	)	->Dict:
	"""simple docstring"""





	__snake_case      :		List[str]         =							text.split(_snake_case	)
	return [character.join(text[i : i + n]	).strip() for i in range(0	,  len(_snake_case	)	,  _snake_case	)]






def 						lowercase				(	_snake_case		: List[str]	)	->Optional[Any]:
	"""simple docstring"""





	__snake_case      ,				__snake_case      :		Union[str, Any]         =							[], []
	for title, text in zip(documents['''title''']	,  documents['''text''']	):
		if text is not None:
			for passage in split_text(_snake_case	):
				titles.append(title if title is not None else ''''''	)
				texts.append(_snake_case	)
	return {"title": titles, "text": texts}






def 						lowercase				(	_snake_case		: List[Any]	,  _snake_case		: Any	,  _snake_case		: Dict	,  _snake_case		: Optional[Any]	,  _snake_case		: Dict	,  _snake_case		: Dict	)	->Dict:
	"""simple docstring"""





	__snake_case      :		Any         =							load_dataset(
	    '''csv'''	,  data_files=[csv_path]	,  split='''train'''	,  delimiter='''\t'''	,  column_names=['''title''', '''text''']	)
	__snake_case      :		Tuple         =							kb_dataset.map(
	    _snake_case	,  batched=_snake_case	,  num_proc=1	)  # if you want you can load already splitted csv.
	__snake_case      :		Any         =							[kb_dataset.shard(_snake_case	,  _snake_case	,  contiguous=_snake_case	) for i in range(_snake_case	)]
	__snake_case      :		Union[str, Any]         =							kb_list[process_num]

	__snake_case      :		List[Any]         =							'''data_''' + str(_snake_case	)
	__snake_case      :		Tuple         =							os.path.join(_snake_case	,  _snake_case	)

	__snake_case      :		Any         =							DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''	)
	__snake_case      :		List[Any]         =							ctx_encoder.to(device=_snake_case	)

	def embed(
	    _snake_case		: dict	,  _snake_case		: DPRContextEncoder	,  _snake_case		: DPRContextEncoderTokenizerFast	,  _snake_case		: Any	) -> dict:
		__snake_case      :		Optional[int]         =							ctx_tokenizer(
		    documents['''title''']	,  documents['''text''']	,  truncation=_snake_case	,  padding='''longest'''	,  return_tensors='''pt'''	)['''input_ids''']
		__snake_case      :		Optional[Any]         =							ctx_encoder(input_ids.to(device=_snake_case	)	,  return_dict=_snake_case	).pooler_output
		return {"embeddings": embeddings.detach().cpu().numpy()}

	__snake_case      :		Dict         =							Features(
	    {'''text''': Value('''string'''	), '''title''': Value('''string'''	), '''embeddings''': Sequence(Value('''float32'''	)	)}	)  # optional, save as float32 instead of float64 to save space

	__snake_case      :		Union[str, Any]         =							data_shrad.map(
	    partial(_snake_case	,  ctx_encoder=_snake_case	,  ctx_tokenizer=_snake_case	,  device=_snake_case	)	,  batched=_snake_case	,  batch_size=16	,  features=_snake_case	,  )
	dataset.save_to_disk(_snake_case	)






def 						lowercase				(	_snake_case		: List[str]	,  _snake_case		: Any	)	->Union[str, Any]:
	"""simple docstring"""





	__snake_case      :		Dict         =							[]

	for shard_address in glob(str(_snake_case	) + '''/*/'''	):
		data_shard_list.append(load_from_disk(_snake_case	)	)

	__snake_case      :		str         =							concatenate_datasets(_snake_case	)
	faiss.omp_set_num_threads(96	)

	__snake_case      :		str         =							faiss.IndexHNSWFlat(768	,  128	,  faiss.METRIC_INNER_PRODUCT	)
	concat.add_faiss_index('''embeddings'''	,  custom_index=_snake_case	)
	concat.get_index('''embeddings'''	).save(
	    _snake_case	)  # since we load the index in to memory,we can directly update the index in the disk









"""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()





