


from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


lowerCAmelCase       =							'\\n@misc{wu2016googles,\n      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n'

lowerCAmelCase       =							'\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe \'GLEU score\'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore\'s range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n'

lowerCAmelCase       =							'\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    \'google_bleu\': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results["google_bleu"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results["google_bleu"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',\n        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',\n        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']\n        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',\n        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',\n        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']\n        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',\n        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',\n        ...          \'heed\', \'the\', \'cat\', \'commands\']\n        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',\n        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',\n        ...          \'of\', \'the\', \'cat\']\n\n        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',\n        ...         \'interested\', \'in\', \'world\', \'history\']\n        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',\n        ...          \'because\', \'he\', \'read\', \'the\', \'book\']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric("google_bleu")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results["google_bleu"], 2))\n        0.4\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,	_KWARGS_DESCRIPTION			)
class        _a							(			datasets.Metric			):

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   MetricInfo:





									"""simple docstring"""







									return datasets.MetricInfo(
									    description=_DESCRIPTION    ,						citation=_CITATION    ,						inputs_description=_KWARGS_DESCRIPTION    ,						features=datasets.Features(
									        {
									            '''predictions''': datasets.Sequence(datasets.Value('''string'''    ,						id='''token'''							)    ,						id='''sequence'''							),
									            '''references''': datasets.Sequence(
									                datasets.Sequence(datasets.Value('''string'''    ,						id='''token'''							)    ,						id='''sequence'''							)    ,						id='''references'''							),
									        }							)    ,						)


							def       lowerCamelCase_ (							self:	Any    ,						UpperCamelCase_:	List[List[List[str]]]    ,						UpperCamelCase_:	List[List[str]]    ,						UpperCamelCase_:	int = 1    ,						UpperCamelCase_:	int = 4    ,						)			->   Dict[str, float]:





									"""simple docstring"""







									return {
									    "google_bleu": gleu_score.corpus_gleu(
									        list_of_references=UpperCamelCase_    ,						hypotheses=UpperCamelCase_    ,						min_len=UpperCamelCase_    ,						max_len=UpperCamelCase_							)
									}






import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


lowerCAmelCase       =							logging.getLogger(__name__)


class        _a							(			UpperCamelCase__			):
							_lowercase	:			Union[str, Any]        = '''token-classification'''

							def __init__(							self:	int    ,						UpperCamelCase_:	Optional[Any]							)			->   Dict:





									"""simple docstring"""







									if type(UpperCamelCase_							) == dict:
											lowercase__            =      Namespace(**UpperCamelCase_							)
									lowercase__            =      import_module('''tasks'''							)
									try:
											lowercase__            =      getattr(UpperCamelCase_    ,						hparams.task_type							)
											lowercase__            =      token_classification_task_clazz()
									except AttributeError:
											raise ValueError(
											    f'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
											    f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'							)
									lowercase__            =      self.token_classification_task.get_labels(hparams.labels							)
									lowercase__            =      CrossEntropyLoss().ignore_index
									super().__init__(UpperCamelCase_    ,						len(self.labels							)    ,						self.mode							)

							def       lowerCamelCase_ (							self:	Tuple    ,						**UpperCamelCase_:	Optional[int]							)			->   str:





									"""simple docstring"""







									return self.model(**UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	List[Any]    ,						UpperCamelCase_:	int							)			->   int:





									"""simple docstring"""







									lowercase__            =      {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
									if self.config.model_type != "distilbert":
											lowercase__            =      (
											    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
											)  # XLM and RoBERTa don"t use token_type_ids

									lowercase__            =      self(**UpperCamelCase_							)
									lowercase__            =      outputs[0]
									# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
									return {"loss": loss}

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      self.hparams
									for mode in ["train", "dev", "test"]:
											lowercase__            =      self._feature_file(UpperCamelCase_							)
											if os.path.exists(UpperCamelCase_							) and not args.overwrite_cache:
													logger.info('''Loading features from cached file %s'''    ,						UpperCamelCase_							)
													lowercase__            =      torch.load(UpperCamelCase_							)
											else:
													logger.info('''Creating features from dataset file at %s'''    ,						args.data_dir							)
													lowercase__            =      self.token_classification_task.read_examples_from_file(args.data_dir    ,						UpperCamelCase_							)
													lowercase__            =      self.token_classification_task.convert_examples_to_features(
													    UpperCamelCase_    ,						self.labels    ,						args.max_seq_length    ,						self.tokenizer    ,						cls_token_at_end=bool(self.config.model_type in ['''xlnet''']							)    ,						cls_token=self.tokenizer.cls_token    ,						cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0    ,						sep_token=self.tokenizer.sep_token    ,						sep_token_extra=UpperCamelCase_    ,						pad_on_left=bool(self.config.model_type in ['''xlnet''']							)    ,						pad_token=self.tokenizer.pad_token_id    ,						pad_token_segment_id=self.tokenizer.pad_token_type_id    ,						pad_token_label_id=self.pad_token_label_id    ,						)
													logger.info('''Saving features into cached file %s'''    ,						UpperCamelCase_							)
													torch.save(UpperCamelCase_    ,						UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	bool = False							)			->   DataLoader:





									"""simple docstring"""







									lowercase__            =      self._feature_file(UpperCamelCase_							)
									logger.info('''Loading features from cached file %s'''    ,						UpperCamelCase_							)
									lowercase__            =      torch.load(UpperCamelCase_							)
									lowercase__            =      torch.tensor([f.input_ids for f in features]    ,						dtype=torch.long							)
									lowercase__            =      torch.tensor([f.attention_mask for f in features]    ,						dtype=torch.long							)
									if features[0].token_type_ids is not None:
											lowercase__            =      torch.tensor([f.token_type_ids for f in features]    ,						dtype=torch.long							)
									else:
											lowercase__            =      torch.tensor([0 for f in features]    ,						dtype=torch.long							)
											# HACK(we will not use this anymore soon)
									lowercase__            =      torch.tensor([f.label_ids for f in features]    ,						dtype=torch.long							)
									return DataLoader(
									    TensorDataset(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							)    ,						batch_size=UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Any    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	List[Any]							)			->   Union[str, Any]:





									"""simple docstring"""







									"""Compute validation""" ""
									lowercase__            =      {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
									if self.config.model_type != "distilbert":
											lowercase__            =      (
											    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
											)  # XLM and RoBERTa don"t use token_type_ids
									lowercase__            =      self(**UpperCamelCase_							)
									lowercase__     ,		lowercase__            =      outputs[:2]
									lowercase__            =      logits.detach().cpu().numpy()
									lowercase__            =      inputs['''labels'''].detach().cpu().numpy()
									return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

							def       lowerCamelCase_ (							self:	Dict    ,						UpperCamelCase_:	List[str]							)			->   int:





									"""simple docstring"""







									lowercase__            =      torch.stack([x['''val_loss'''] for x in outputs]							).mean()
									lowercase__            =      np.concatenate([x['''pred'''] for x in outputs]    ,						axis=0							)
									lowercase__            =      np.argmax(UpperCamelCase_    ,						axis=2							)
									lowercase__            =      np.concatenate([x['''target'''] for x in outputs]    ,						axis=0							)

									lowercase__            =      dict(enumerate(self.labels							)							)
									lowercase__            =      [[] for _ in range(out_label_ids.shape[0]							)]
									lowercase__            =      [[] for _ in range(out_label_ids.shape[0]							)]

									for i in range(out_label_ids.shape[0]							):
											for j in range(out_label_ids.shape[1]							):
													if out_label_ids[i, j] != self.pad_token_label_id:
															out_label_list[i].append(label_map[out_label_ids[i][j]]							)
															preds_list[i].append(label_map[preds[i][j]]							)

									lowercase__            =      {
									    '''val_loss''': val_loss_mean,
									    '''accuracy_score''': accuracy_score(UpperCamelCase_    ,						UpperCamelCase_							),
									    '''precision''': precision_score(UpperCamelCase_    ,						UpperCamelCase_							),
									    '''recall''': recall_score(UpperCamelCase_    ,						UpperCamelCase_							),
									    '''f1''': fa_score(UpperCamelCase_    ,						UpperCamelCase_							),
									}

									lowercase__            =      dict(results.items()							)
									lowercase__            =      results
									return ret, preds_list, out_label_list

							def       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	List[Any]							)			->   Dict:





									"""simple docstring"""







									lowercase__     ,		lowercase__     ,		lowercase__            =      self._eval_end(UpperCamelCase_							)
									lowercase__            =      ret['''log''']
									return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	Dict							)			->   Dict:





									"""simple docstring"""







									lowercase__     ,		lowercase__     ,		lowercase__            =      self._eval_end(UpperCamelCase_							)

									# Converting to the dict required by pl
									# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
									# pytorch_lightning/trainer/logging.py#L139
									lowercase__            =      ret['''log''']
									# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
									return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


							@staticmethod
							def       lowerCamelCase_ (							UpperCamelCase_:	Optional[Any]    ,						UpperCamelCase_:	str							)			->   Optional[Any]:





									"""simple docstring"""







									BaseTransformer.add_model_specific_args(UpperCamelCase_    ,						UpperCamelCase_							)
									parser.add_argument(
									    '''--task_type'''    ,						default='''NER'''    ,						type=UpperCamelCase_    ,						help='''Task type to fine tune in training (e.g. NER, POS, etc)'''							)
									parser.add_argument(
									    '''--max_seq_length'''    ,						default=128    ,						type=UpperCamelCase_    ,						help=(
									        '''The maximum total input sequence length after tokenization. Sequences longer '''
									        '''than this will be truncated, sequences shorter will be padded.'''
									    )    ,						)

									parser.add_argument(
									    '''--labels'''    ,						default=''''''    ,						type=UpperCamelCase_    ,						help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''    ,						)
									parser.add_argument(
									    '''--gpus'''    ,						default=0    ,						type=UpperCamelCase_    ,						help='''The number of GPUs allocated for this, it is by default 0 meaning none'''    ,						)

									parser.add_argument(
									    '''--overwrite_cache'''    ,						action='''store_true'''    ,						help='''Overwrite the cached training and evaluation sets'''							)

									return parser


if __name__ == "__main__":
			lowerCAmelCase       =							argparse.ArgumentParser()
			add_generic_args(parser, os.getcwd())
			lowerCAmelCase       =							NERTransformer.add_model_specific_args(parser, os.getcwd())
			lowerCAmelCase       =							parser.parse_args()
			lowerCAmelCase       =							NERTransformer(args)
			lowerCAmelCase       =							generic_train(model, args)

			if args.do_predict:
						# See https://github.com/huggingface/transformers/issues/3159
						# pl use this default format to create a checkpoint:
						# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
						# /pytorch_lightning/callbacks/model_checkpoint.py#L322
						lowerCAmelCase       =							sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
						lowerCAmelCase       =							model.load_from_checkpoint(checkpoints[-1])
						trainer.test(model)


