

import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


A_	:			Optional[Any]  	=      logging.getLogger(__name__)



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	'''sequence-classification'''






					def __init__(			self   ,	A__      ):
						if type(A__      ) == dict:
							A__							:   Optional[int]					       =		Namespace(**A__      )
						A__							:   int					       =		glue_output_modes[hparams.task]
						A__							:   Optional[int]					       =		glue_tasks_num_labels[hparams.task]

						super().__init__(A__   ,	A__   ,	self.mode      )






					def   __A      (			self   ,	**A__      ):
						return self.model(**A__      )






					def   __A      (			self   ,	A__   ,	A__      ):
						A__							:   List[Any]					       =		{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

						if self.config.model_type not in ["distilbert", "bart"]:
							A__							:   Any					       =		batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None

						A__							:   Union[str, Any]					       =		self(**A__      )
						A__							:   List[Any]					       =		outputs[0]

						A__							:   Any					       =		self.trainer.lr_schedulers[0]["""scheduler"""]
						A__							:   Tuple					       =		{"""loss""": loss, """rate""": lr_scheduler.get_last_lr()[-1]}
						return {"loss": loss, "log": tensorboard_logs}






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.hparams
						A__							:   List[str]					       =		processors[args.task]()
						A__							:   Optional[int]					       =		processor.get_labels()

						for mode in ["train", "dev"]:
							A__							:   Optional[int]					       =		self._feature_file(A__      )
							if os.path.exists(A__      ) and not args.overwrite_cache:
								logger.info("""Loading features from cached file %s"""   ,	A__      )
							else:
								logger.info("""Creating features from dataset file at %s"""   ,	args.data_dir      )
								A__							:   str					       =		(
								    processor.get_dev_examples(args.data_dir      )
								    if mode == """dev"""
								    else processor.get_train_examples(args.data_dir      )
								)
								A__							:   Any					       =		convert_examples_to_features(
								    A__   ,	self.tokenizer   ,	max_length=args.max_seq_length   ,	label_list=self.labels   ,	output_mode=args.glue_output_mode   ,	)
								logger.info("""Saving features into cached file %s"""   ,	A__      )
								torch.save(A__   ,	A__      )






					def   __A      (			self   ,	A__   ,	A__   ,	A__ = False      ):
						A__							:   Any					       =		"""dev""" if mode == """test""" else mode

						A__							:   List[Any]					       =		self._feature_file(A__      )
						logger.info("""Loading features from cached file %s"""   ,	A__      )
						A__							:   Any					       =		torch.load(A__      )
						A__							:   Optional[int]					       =		torch.tensor([f.input_ids for f in features]   ,	dtype=torch.long      )
						A__							:   Tuple					       =		torch.tensor([f.attention_mask for f in features]   ,	dtype=torch.long      )
						A__							:   Tuple					       =		torch.tensor([f.token_type_ids for f in features]   ,	dtype=torch.long      )
						if self.hparams.glue_output_mode == "classification":
							A__							:   List[str]					       =		torch.tensor([f.label for f in features]   ,	dtype=torch.long      )
						elif self.hparams.glue_output_mode == "regression":
							A__							:   Tuple					       =		torch.tensor([f.label for f in features]   ,	dtype=torch.float      )

						return DataLoader(
						    TensorDataset(A__   ,	A__   ,	A__   ,	A__      )   ,	batch_size=A__   ,	shuffle=A__   ,	)






					def   __A      (			self   ,	A__   ,	A__      ):
						A__							:   Dict					       =		{"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

						if self.config.model_type not in ["distilbert", "bart"]:
							A__							:   str					       =		batch[2] if self.config.model_type in ["""bert""", """xlnet""", """albert"""] else None

						A__							:   Tuple					       =		self(**A__      )
						A__       ,       A__							:   Tuple					       =		outputs[:2]
						A__							:   Dict					       =		logits.detach().cpu().numpy()
						A__							:   List[Any]					       =		inputs["""labels"""].detach().cpu().numpy()

						return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






					def   __A      (			self   ,	A__      ):
						A__							:   List[Any]					       =		torch.stack([x["""val_loss"""] for x in outputs]      ).mean().detach().cpu().item()
						A__							:   List[str]					       =		np.concatenate([x["""pred"""] for x in outputs]   ,	axis=0      )

						if self.hparams.glue_output_mode == "classification":
							A__							:   List[Any]					       =		np.argmax(A__   ,	axis=1      )
						elif self.hparams.glue_output_mode == "regression":
							A__							:   str					       =		np.squeeze(A__      )

						A__							:   Tuple					       =		np.concatenate([x["""target"""] for x in outputs]   ,	axis=0      )
						A__							:   Tuple					       =		[[] for _ in range(out_label_ids.shape[0]      )]
						A__							:   List[str]					       =		[[] for _ in range(out_label_ids.shape[0]      )]

						A__							:   int					       =		{**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task   ,	A__   ,	A__      )}

						A__							:   str					       =		dict(results.items()      )
						A__							:   Dict					       =		results
						return ret, preds_list, out_label_list






					def   __A      (			self   ,	A__      ):
						A__       ,       A__       ,       A__							:   Optional[int]					       =		self._eval_end(A__      )
						A__							:   List[str]					       =		ret["""log"""]
						return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






					def   __A      (			self   ,	A__      ):
						A__       ,       A__       ,       A__							:   Optional[Any]					       =		self._eval_end(A__      )
						A__							:   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   __A      (			A__   ,	A__      ):
						BaseTransformer.add_model_specific_args(A__   ,	A__      )
						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(
						    """--task"""   ,	default=""""""   ,	type=A__   ,	required=A__   ,	help="""The GLUE task to run"""   ,	)
						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



def 						UpperCamelCase ()					->				int:
	A__							:   Optional[Any]					       =		argparse.ArgumentParser()
	add_generic_args(lowercase_   ,				os.getcwd() )
	A__							:   Tuple					       =		GLUETransformer.add_model_specific_args(lowercase_   ,				os.getcwd() )
	A__							:   int					       =		parser.parse_args()

	# If output_dir not provided, a folder will be generated in pwd
	if args.output_dir is None:
		A__							:   int					       =		os.path.join(
		    """./results"""   ,				f"""{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}"""   ,				)
		os.makedirs(args.output_dir )

	A__							:   List[str]					       =		GLUETransformer(lowercase_ )
	A__							:   List[Any]					       =		generic_train(lowercase_   ,				lowercase_ )

	# Optionally, predict on dev set and write to output_dir
	if args.do_predict:
		A__							:   List[Any]					       =		sorted(glob.glob(os.path.join(args.output_dir   ,				"""checkpoint-epoch=*.ckpt""" )   ,				recursive=lowercase_ ) )
		A__							:   Tuple					       =		model.load_from_checkpoint(checkpoints[-1] )
		return trainer.test(lowercase_ )


if __name__ == "__main__":
	main()






from queue import PriorityQueue
from typing import Any

import numpy as np

def 						UpperCamelCase (lowercase_:			dict   ,				lowercase_:			str   ,				lowercase_:			set   ,				lowercase_:			set   ,				lowercase_:			dict   ,				lowercase_:			dict   ,				lowercase_:			PriorityQueue   ,				lowercase_:			dict   ,				lowercase_:			float | int   ,				)					->				float | int:
	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		A__							:   Any					       =		cst_fwd.get(lowercase_   ,				np.inf )
		A__							:   List[Any]					       =		cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt) )
			A__							:   Tuple					       =		new_cost_f
			A__							:   Any					       =		v
		if nxt in visited_backward:
			if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
				A__							:   Optional[int]					       =		cst_fwd[v] + d + cst_bwd[nxt]
	return shortest_distance

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str   ,				lowercase_:			dict   ,				lowercase_:			dict )					->				int:
	A__							:   Dict					       =		-1

	A__							:   List[Any]					       =		set()
	A__							:   Union[str, Any]					       =		set()
	A__							:   Optional[Any]					       =		{source: 0}
	A__							:   int					       =		{destination: 0}
	A__							:   Optional[Any]					       =		{source: None}
	A__							:   Union[str, Any]					       =		{destination: None}
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()

	A__							:   List[Any]					       =		np.inf

	queue_forward.put((0, source) )
	queue_backward.put((0, destination) )

	if source == destination:
		return 0

	while not queue_forward.empty() and not queue_backward.empty():
		A__       ,       A__							:   Tuple					       =		queue_forward.get()
		visited_forward.add(lowercase_ )

		A__       ,       A__							:   Optional[Any]					       =		queue_backward.get()
		visited_backward.add(lowercase_ )

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
			break

	if shortest_distance != np.inf:
		A__							:   int					       =		shortest_distance
	return shortest_path_distance


A_	:			List[Any]  	=      {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A_	:			Optional[int]  	=      {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
	import doctest

	doctest.testmod()



