

'''simple docstring'''





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     snake_case_			(				__SCREAMING_SNAKE_CASE							):


							"""simple docstring"""





							__UpperCAmelCase       ="""token-classification"""




							def __init__( self					,						_A							):




												if type(_a							) == dict:
																	__lowerCAmelCase					=							Namespace(**_a							)
												__lowerCAmelCase					=							import_module('tasks'							)
												try:
																	__lowerCAmelCase					=							getattr(_a					,						hparams.task_type							)
																	__lowerCAmelCase					=							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__()}"""							)
												__lowerCAmelCase					=							self.token_classification_task.get_labels(hparams.labels							)
												__lowerCAmelCase					=							CrossEntropyLoss().ignore_index
												super().__init__(_a					,						len(self.labels							)					,						self.mode							)




							def        A__  ( self					,						**_A							):
												return self.model(**_a							)




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
												if self.config.model_type != "distilbert":
																	__lowerCAmelCase					=							(
																	    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
																	)  # XLM and RoBERTa don"t use token_type_ids

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




							def        A__  ( self							):
												__lowerCAmelCase					=							self.hparams
												for mode in ["train", "dev", "test"]:
																	__lowerCAmelCase					=							self._feature_file(_a							)
																	if os.path.exists(_a							) and not args.overwrite_cache:
																						logger.info('Loading features from cached file %s'					,						_a							)
																						__lowerCAmelCase					=							torch.load(_a							)
																	else:
																						logger.info('Creating features from dataset file at %s'					,						args.data_dir							)
																						__lowerCAmelCase					=							self.token_classification_task.read_examples_from_file(args.data_dir					,						_a							)
																						__lowerCAmelCase					=							self.token_classification_task.convert_examples_to_features(
																						    _a					,						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=_a					,						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'					,						_a							)
																						torch.save(_a					,						_a							)




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




							def        A__  ( self					,						_A					,						_A							):
												"""Compute validation""" ""
												__lowerCAmelCase					=							{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
												if self.config.model_type != "distilbert":
																	__lowerCAmelCase					=							(
																	    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
																	)  # XLM and RoBERTa don"t use token_type_ids
												__lowerCAmelCase					=							self(**_a							)
												__lowerCAmelCase					=							outputs[:2]
												__lowerCAmelCase					=							logits.detach().cpu().numpy()
												__lowerCAmelCase					=							inputs["""labels"""].detach().cpu().numpy()
												return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							torch.stack([x['val_loss'] for x in outputs]							).mean()
												__lowerCAmelCase					=							np.concatenate([x['pred'] for x in outputs]					,						axis=0							)
												__lowerCAmelCase					=							np.argmax(_a					,						axis=2							)
												__lowerCAmelCase					=							np.concatenate([x['target'] for x in outputs]					,						axis=0							)

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

												__lowerCAmelCase					=							{
												    """val_loss""": val_loss_mean,
												    """accuracy_score""": accuracy_score(_a					,						_a							),
												    """precision""": precision_score(_a					,						_a							),
												    """recall""": recall_score(_a					,						_a							),
												    """f1""": fa_score(_a					,						_a							),
												}

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




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							self._eval_end(_a							)
												__lowerCAmelCase					=							ret["""log"""]
												return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							self._eval_end(_a							)

												# Converting to the dict required by pl
												# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
												# pytorch_lightning/trainer/logging.py#L139
												__lowerCAmelCase					=							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        A__  ( _A					,						_A							):
												BaseTransformer.add_model_specific_args(_a					,						_a							)
												parser.add_argument(
												    '--task_type'					,						default='NER'					,						type=_a					,						help='Task type to fine tune in training (e.g. NER, POS, etc)'							)
												parser.add_argument(
												    '--max_seq_length'					,						default=1_2_8					,						type=_a					,						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=_a					,						help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'					,						)
												parser.add_argument(
												    '--gpus'					,						default=0					,						type=_a					,						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)









def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					assert x is not None
					assert y is not None

					__lowerCAmelCase					=							len(UpperCAmelCase__      )
					__lowerCAmelCase					=							len(UpperCAmelCase__      )

					# declaring the array for storing the dp values
					__lowerCAmelCase					=							[[0] * (n + 1) for _ in range(m + 1      )]  # noqa: E741

					for i in range(1		,					m + 1      ):
										for j in range(1		,					n + 1      ):
															__lowerCAmelCase					=							1 if x[i - 1] == y[j - 1] else 0

															__lowerCAmelCase					=							max(l[i - 1][j]		,					l[i][j - 1]		,					l[i - 1][j - 1] + match      )

					__lowerCAmelCase					=							''
					__lowerCAmelCase,	__lowerCAmelCase					=							m, n
					while i > 0 and j > 0:
										__lowerCAmelCase					=							1 if x[i - 1] == y[j - 1] else 0

										if l[i][j] == l[i - 1][j - 1] + match:
															if match == 1:
																				__lowerCAmelCase					=							x[i - 1] + seq
															i -= 1
															j -= 1
										elif l[i][j] == l[i - 1][j]:
															i -= 1
										else:
															j -= 1

					return l[m][n], seq


if __name__ == "__main__":
		lowerCamelCase      				=     '''AGGTAB'''
		lowerCamelCase      				=     '''GXTXAYB'''
		lowerCamelCase      				=     4
		lowerCamelCase      				=     '''GTAB'''

		lowerCamelCase				,				lowerCamelCase      				=     longest_common_subsequence(a, b)
		print('''len =''', ln, ''', sub-sequence =''', subseq)
		import doctest

		doctest.testmod()






