


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							(    _lowercase):
	_a  :  Optional[int]				   =							'''token-classification'''




	def __init__( self						:		str		, _SCREAMING_SNAKE_CASE						:		int		)->	Optional[Any]:
			if type(_SCREAMING_SNAKE_CASE		) == dict:
					lowerCAmelCase__       :      Optional[int]   					= Namespace(**_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Any   					= import_module('''tasks'''		)
			try:
					lowerCAmelCase__       :      Optional[int]   					= getattr(_SCREAMING_SNAKE_CASE		, hparams.task_type		)
					lowerCAmelCase__       :      TokenClassificationTask   					= 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__       :      List[Any]   					= self.token_classification_task.get_labels(hparams.labels		)
			lowerCAmelCase__       :      Tuple   					= CrossEntropyLoss().ignore_index
			super().__init__(_SCREAMING_SNAKE_CASE		, len(self.labels		)		, self.mode		)




	def 							UpperCAmelCase__( self						:		List[str]		, **_SCREAMING_SNAKE_CASE						:		Dict		)->	List[str]:
			return self.model(**_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		str		)->	Tuple:
			lowerCAmelCase__       :      Tuple   					= {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
			if self.config.model_type != "distilbert":
					lowerCAmelCase__       :      Any   					= (
					    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
					)  # XLM and RoBERTa don"t use token_type_ids

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




	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	List[Any]:
			lowerCAmelCase__       :      Tuple   					= self.hparams
			for mode in ["train", "dev", "test"]:
					lowerCAmelCase__       :      int   					= self._feature_file(_SCREAMING_SNAKE_CASE		)
					if os.path.exists(_SCREAMING_SNAKE_CASE		) and not args.overwrite_cache:
							logger.info('''Loading features from cached file %s'''		, _SCREAMING_SNAKE_CASE		)
							lowerCAmelCase__       :      Optional[int]   					= torch.load(_SCREAMING_SNAKE_CASE		)
					else:
							logger.info('''Creating features from dataset file at %s'''		, args.data_dir		)
							lowerCAmelCase__       :      str   					= self.token_classification_task.read_examples_from_file(args.data_dir		, _SCREAMING_SNAKE_CASE		)
							lowerCAmelCase__       :      Optional[Any]   					= self.token_classification_task.convert_examples_to_features(
							    _SCREAMING_SNAKE_CASE		, 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=_SCREAMING_SNAKE_CASE		, 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'''		, _SCREAMING_SNAKE_CASE		)
							torch.save(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		bool = False		)->	DataLoader:
			lowerCAmelCase__       :      List[str]   					= self._feature_file(_SCREAMING_SNAKE_CASE		)
			logger.info('''Loading features from cached file %s'''		, _SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[Any]   					= torch.load(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Dict   					= torch.tensor([f.input_ids for f in features]		, dtype=torch.long		)
			lowerCAmelCase__       :      Union[str, Any]   					= torch.tensor([f.attention_mask for f in features]		, dtype=torch.long		)
			if features[0].token_type_ids is not None:
					lowerCAmelCase__       :      str   					= torch.tensor([f.token_type_ids for f in features]		, dtype=torch.long		)
			else:
					lowerCAmelCase__       :      Any   					= torch.tensor([0 for f in features]		, dtype=torch.long		)
					# HACK(we will not use this anymore soon)
			lowerCAmelCase__       :      str   					= torch.tensor([f.label_ids for f in features]		, dtype=torch.long		)
			return DataLoader(
			    TensorDataset(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)		, batch_size=_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Dict		)->	Optional[int]:
			"""Compute validation""" ""
			lowerCAmelCase__       :      Optional[int]   					= {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
			if self.config.model_type != "distilbert":
					lowerCAmelCase__       :      List[Any]   					= (
					    batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
					)  # XLM and RoBERTa don"t use token_type_ids
			lowerCAmelCase__       :      Dict   					= self(**_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__					,     lowerCAmelCase__       :      int   					= outputs[:2]
			lowerCAmelCase__       :      List[str]   					= logits.detach().cpu().numpy()
			lowerCAmelCase__       :      Optional[Any]   					= inputs['''labels'''].detach().cpu().numpy()
			return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	str:
			lowerCAmelCase__       :      Dict   					= torch.stack([x['''val_loss'''] for x in outputs]		).mean()
			lowerCAmelCase__       :      List[str]   					= np.concatenate([x['''pred'''] for x in outputs]		, axis=0		)
			lowerCAmelCase__       :      Any   					= np.argmax(_SCREAMING_SNAKE_CASE		, axis=2		)
			lowerCAmelCase__       :      List[Any]   					= np.concatenate([x['''target'''] for x in outputs]		, axis=0		)

			lowerCAmelCase__       :      Union[str, Any]   					= dict(enumerate(self.labels		)		)
			lowerCAmelCase__       :      str   					= [[] for _ in range(out_label_ids.shape[0]		)]
			lowerCAmelCase__       :      Dict   					= [[] 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__       :      int   					= {
			    '''val_loss''': val_loss_mean,
			    '''accuracy_score''': accuracy_score(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		),
			    '''precision''': precision_score(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		),
			    '''recall''': recall_score(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		),
			    '''f1''': fa_score(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		),
			}

			lowerCAmelCase__       :      Optional[int]   					= dict(results.items()		)
			lowerCAmelCase__       :      List[str]   					= results
			return ret, preds_list, out_label_list




	def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	Tuple:
			# when stable
			lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      Dict   					= self._eval_end(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      List[str]   					= ret['''log''']
			return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	int:
			# updating to test_epoch_end instead of deprecated test_end
			lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      List[Any]   					= self._eval_end(_SCREAMING_SNAKE_CASE		)

			# Converting to the dict required by pl
			# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
			# pytorch_lightning/trainer/logging.py#L139
			lowerCAmelCase__       :      Optional[int]   					= 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 							UpperCAmelCase__( _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Optional[Any]:
			# Add NER specific options
			BaseTransformer.add_model_specific_args(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
			parser.add_argument(
			    '''--task_type'''		, default='''NER'''		, type=_SCREAMING_SNAKE_CASE		, help='''Task type to fine tune in training (e.g. NER, POS, etc)'''		)
			parser.add_argument(
			    '''--max_seq_length'''		, default=128		, type=_SCREAMING_SNAKE_CASE		, 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=_SCREAMING_SNAKE_CASE		, help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''		, )
			parser.add_argument(
			    '''--gpus'''		, default=0		, type=_SCREAMING_SNAKE_CASE		, 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)








import math





class  _a							:




	def __init__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Any=0		)->	Optional[Any]:  # a graph with Node 0,1,...,N-1
			lowerCAmelCase__       :      Optional[int]   					= n
			lowerCAmelCase__       :      List[Any]   					= [
			    [math.inf for j in range(0		, _SCREAMING_SNAKE_CASE		)] for i in range(0		, _SCREAMING_SNAKE_CASE		)
			]  # adjacency matrix for weight
			lowerCAmelCase__       :      str   					= [
			    [math.inf for j in range(0		, _SCREAMING_SNAKE_CASE		)] for i in range(0		, _SCREAMING_SNAKE_CASE		)
			]  # dp[i][j] stores minimum distance from i to j




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		List[str]		, _SCREAMING_SNAKE_CASE						:		str		)->	List[str]:
			lowerCAmelCase__       :      Optional[int]   					= w




	def 							UpperCAmelCase__( self						:		List[Any]		)->	Optional[int]:
			for k in range(0		, self.n		):
					for i in range(0		, self.n		):
							for j in range(0		, self.n		):
									lowerCAmelCase__       :      Dict   					= min(self.dp[i][j]		, self.dp[i][k] + self.dp[k][j]		)




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		str		)->	str:
			return self.dp[u][v]


if __name__ == "__main__":
		lowerCamelCase						=		Graph(5)
		graph.add_edge(0, 2, 9)
		graph.add_edge(0, 4, 10)
		graph.add_edge(1, 3, 5)
		graph.add_edge(2, 3, 7)
		graph.add_edge(3, 0, 10)
		graph.add_edge(3, 1, 2)
		graph.add_edge(3, 2, 1)
		graph.add_edge(3, 4, 6)
		graph.add_edge(4, 1, 3)
		graph.add_edge(4, 2, 4)
		graph.add_edge(4, 3, 9)
		graph.floyd_warshall()
		graph.show_min(1, 4)
		graph.show_min(0, 3)




