



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__		:     Any       						=logging.getLogger(__name__)

class    UpperCAmelCase_  (   snake_case_       ):

						'''simple docstring'''




						UpperCamelCase__			: Union[str, Any]				  =  '''token-classification'''






						def __init__(    self      ,							_A		):







													'''simple docstring'''

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






						def        _A	(    self      ,							**_A		):







													'''simple docstring'''

													return self.model(**_A		)






						def        _A	(    self      ,							_A      ,							_A		):







													'''simple docstring'''

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

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






						def        _A	(    self		):







													'''simple docstring'''

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







													'''simple docstring'''

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







													'''simple docstring'''

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






						def        _A	(    self      ,							_A		):







													'''simple docstring'''

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

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

													__SCREAMING_SNAKE_CASE									=					{
													    '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		),
													}

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






						def        _A	(    self      ,							_A		):







													'''simple docstring'''

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






						def        _A	(    self      ,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE							,       __SCREAMING_SNAKE_CASE							,       __SCREAMING_SNAKE_CASE									=					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
													__SCREAMING_SNAKE_CASE									=					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		):







													'''simple docstring'''

													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=128      ,							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__		:     int       						=argparse.ArgumentParser()
			add_generic_args(parser, os.getcwd())
			lowerCAmelCase__		:     Dict       						=NERTransformer.add_model_specific_args(parser, os.getcwd())
			lowerCAmelCase__		:     Dict       						=parser.parse_args()
			lowerCAmelCase__		:     Tuple       						=NERTransformer(args)
			lowerCAmelCase__		:     Tuple       						=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__		:     Tuple       						=sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
						lowerCAmelCase__		:     List[Any]       						=model.load_from_checkpoint(checkpoints[-1])
						trainer.test(model)








from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst





def 			lowerCamelCase_ (				):
				lowerCamelCase_	,    lowerCamelCase_						=			9, 1_4  # noqa: F841
				lowerCamelCase_						=			[
				    [0, 1, 4],
				    [0, 7, 8],
				    [1, 2, 8],
				    [7, 8, 7],
				    [7, 6, 1],
				    [2, 8, 2],
				    [8, 6, 6],
				    [2, 3, 7],
				    [2, 5, 4],
				    [6, 5, 2],
				    [3, 5, 1_4],
				    [3, 4, 9],
				    [5, 4, 1_0],
				    [1, 7, 1_1],
				]

				lowerCamelCase_						=			defaultdict(lowerCamelCase__    )
				for nodea, nodea, cost in edges:
								adjancency[nodea].append([nodea, cost]    )
								adjancency[nodea].append([nodea, cost]    )

				lowerCamelCase_						=			mst(lowerCamelCase__    )

				lowerCamelCase_						=			[
				    [7, 6, 1],
				    [2, 8, 2],
				    [6, 5, 2],
				    [0, 1, 4],
				    [2, 5, 4],
				    [2, 3, 7],
				    [0, 7, 8],
				    [3, 4, 9],
				]

				for answer in expected:
								lowerCamelCase_						=			tuple(answer[:2]    )
								lowerCamelCase_						=			tuple(edge[::-1]    )
								assert edge in result or reverse in result


