






'''simple docstring'''





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


__UpperCamelCase      :      Dict     						=						logging.getLogger(__name__)


class        __UpperCamelCase    (     _lowercase   ):
	__snake_case	:List[Any]   =       '''sequence-classification'''







	def __init__(  self      :							Any       ,				_lowerCAmelCase      :							Union[str, Any]			)		-> List[Any]:
		"""simple docstring"""







		if type(A_			) == dict:
			__lowercase             =  Namespace(**A_			)
		__lowercase             =  glue_output_modes[hparams.task]
		__lowercase             =  glue_tasks_num_labels[hparams.task]

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







	def  _a   (  self      :							Dict       ,				**_lowerCAmelCase      :							Dict			)		-> str:
		"""simple docstring"""







		return self.model(**A_			)







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							List[Any]			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

		__lowercase             =  self(**A_			)
		__lowercase             =  outputs[0]

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







	def  _a   (  self      :							Optional[int]			)		-> int:
		"""simple docstring"""







		__lowercase             =  self.hparams
		__lowercase             =  processors[args.task]()
		__lowercase             =  processor.get_labels()

		for mode in ["train", "dev"]:
			__lowercase             =  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			)
				__lowercase             =  (
				    processor.get_dev_examples(args.data_dir			)
				    if mode == """dev"""
				    else processor.get_train_examples(args.data_dir			)
				)
				__lowercase             =  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      :							Optional[int]       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							bool = False			)		-> DataLoader:
		"""simple docstring"""







		__lowercase             =  """dev""" if mode == """test""" else mode

		__lowercase             =  self._feature_file(A_			)
		logger.info("""Loading features from cached file %s"""       ,				A_			)
		__lowercase             =  torch.load(A_			)
		__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			)
		__lowercase             =  torch.tensor([f.token_type_ids for f in features]       ,				dtype=torch.long			)
		if self.hparams.glue_output_mode == "classification":
			__lowercase             =  torch.tensor([f.label for f in features]       ,				dtype=torch.long			)
		elif self.hparams.glue_output_mode == "regression":
			__lowercase             =  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      :							Optional[Any]       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

		__lowercase             =  self(**A_			)
		__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  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							str			)		-> tuple:
		"""simple docstring"""







		__lowercase             =  torch.stack([x["""val_loss"""] for x in outputs]			).mean().detach().cpu().item()
		__lowercase             =  np.concatenate([x["""pred"""] for x in outputs]       ,				axis=0			)

		if self.hparams.glue_output_mode == "classification":
			__lowercase             =  np.argmax(A_       ,				axis=1			)
		elif self.hparams.glue_output_mode == "regression":
			__lowercase             =  np.squeeze(A_			)

		__lowercase             =  np.concatenate([x["""target"""] for x in outputs]       ,				axis=0			)
		__lowercase             =  [[] for _ in range(out_label_ids.shape[0]			)]
		__lowercase             =  [[] for _ in range(out_label_ids.shape[0]			)]

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

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







	def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							list			)		-> dict:
		"""simple docstring"""







		__lowercase       ,						__lowercase       ,						__lowercase             =  self._eval_end(A_			)
		__lowercase             =  ret["""log"""]
		return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







	def  _a   (  self      :							Dict       ,				_lowerCAmelCase      :							Dict			)		-> dict:
		"""simple docstring"""







		__lowercase       ,						__lowercase       ,						__lowercase             =  self._eval_end(A_			)
		__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  _a   (  _lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							Optional[int]			)		-> int:
		"""simple docstring"""







		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 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  argparse.ArgumentParser()
	add_generic_args(snake_case__				,  os.getcwd()					)
	__lowercase             =  GLUETransformer.add_model_specific_args(snake_case__				,  os.getcwd()					)
	__lowercase             =  parser.parse_args()

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

	__lowercase             =  GLUETransformer(snake_case__					)
	__lowercase             =  generic_train(snake_case__				,  snake_case__					)

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


if __name__ == "__main__":
						main()









import heapq
import sys

import numpy as np

__UpperCamelCase      :      List[str]     						=						tuple[int, int]


class        __UpperCamelCase    :







	def __init__(  self      :							Optional[int]			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  []
		__lowercase             =  set()







	def  _a   (  self      :							int			)		-> List[Any]:
		"""simple docstring"""







		if not self.empty():
			return self.elements[0][0]
		else:
			return float("""inf"""			)







	def  _a   (  self      :							Dict			)		-> Optional[Any]:
		"""simple docstring"""







		return len(self.elements			) == 0







	def  _a   (  self      :							str       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Dict:
		"""simple docstring"""







		if item not in self.set:
			heapq.heappush(self.elements       ,				(priority, item)			)
			self.set.add(_lowerCAmelCase			)
		else:
			# update
			# print("update", item)
			__lowercase             =  []
			((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			while x != item:
				temp.append((pri, x)			)
				((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			temp.append((priority, item)			)
			for pro, xxx in temp:
				heapq.heappush(self.elements       ,				(pro, xxx)			)







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Dict:
		"""simple docstring"""







		if item in self.set:
			self.set.remove(_lowerCAmelCase			)
			__lowercase             =  []
			((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			while x != item:
				temp.append((pro, x)			)
				((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			for prito, yyy in temp:
				heapq.heappush(self.elements       ,				(prito, yyy)			)







	def  _a   (  self      :							Any			)		-> List[Any]:
		"""simple docstring"""







		return self.elements[0][1]







	def  _a   (  self      :							Optional[int]			)		-> List[Any]:
		"""simple docstring"""







		((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
		self.set.remove(_lowerCAmelCase			)
		return (priority, item)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  np.array(lowerCamelCase					)
	__lowercase             =  np.array(lowerCamelCase					)
	return np.linalg.norm(a - b					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	return consistent_heuristic(lowerCamelCase				,  lowerCamelCase					) // t





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	return abs(p[0] - goal[0]					) + abs(p[1] - goal[1]					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  g_function[start] + Wa * heuristics[i](lowerCamelCase				,  lowerCamelCase					)
	return ans





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  np.chararray((n, n)					)
	for i in range(lowerCamelCase					):
		for j in range(lowerCamelCase					):
			__lowercase             =  """*"""

	for i in range(lowerCamelCase					):
		for j in range(lowerCamelCase					):
			if (j, (n - 1) - i) in blocks:
				__lowercase             =  """#"""

	__lowercase             =  """-"""
	__lowercase             =  back_pointer[goal]
	while x != start:
		((__lowercase)       ,						(__lowercase))             =  x
		# print(x)
		__lowercase             =  """-"""
		__lowercase             =  back_pointer[x]
	__lowercase             =  """-"""

	for i in range(lowerCamelCase					):
		for j in range(lowerCamelCase					):
			if (i, j) == (0, n - 1):
				print(grid[i][j]				,  end=""" """					)
				print("""<-- End position"""				,  end=""" """					)
			else:
				print(grid[i][j]				,  end=""" """					)
		print()
	print("""^"""					)
	print("""Start position"""					)
	print()
	print("""# is an obstacle"""					)
	print("""- is the path taken by algorithm"""					)
	print("""PATH TAKEN BY THE ALGORITHM IS:-"""					)
	__lowercase             =  back_pointer[goal]
	while x != start:
		print(lowerCamelCase				,  end=""" """					)
		__lowercase             =  back_pointer[x]
	print(lowerCamelCase					)
	sys.exit()





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if p[0] < 0 or p[0] > n - 1:
		return False
	if p[1] < 0 or p[1] > n - 1:
		return False
	return True





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  ):




	'''simple docstring'''







	for itera in range(lowerCamelCase					):
		open_list[itera].remove_element(lowerCamelCase					)
	# print("s", s)
	# print("j", j)
	((__lowercase)       ,						(__lowercase))             =  s
	__lowercase             =  (x - 1, y)
	__lowercase             =  (x + 1, y)
	__lowercase             =  (x, y + 1)
	__lowercase             =  (x, y - 1)

	for neighbours in [left, right, up, down]:
		if neighbours not in blocks:
			if valid(lowerCamelCase					) and neighbours not in visited:
				# print("neighbour", neighbours)
				visited.add(lowerCamelCase					)
				__lowercase             =  -1
				__lowercase             =  float("""inf"""					)

			if valid(lowerCamelCase					) and g_function[neighbours] > g_function[s] + 1:
				__lowercase             =  g_function[s] + 1
				__lowercase             =  s
				if neighbours not in close_list_anchor:
					open_list[0].put(lowerCamelCase				,  key(lowerCamelCase				,  0				,  lowerCamelCase				,  lowerCamelCase					)					)
					if neighbours not in close_list_inad:
						for var in range(1				,  lowerCamelCase					):
							if key(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					) <= Wa * key(
							    lowerCamelCase				,  0				,  lowerCamelCase				,  lowerCamelCase					):
								open_list[j].put(
								    lowerCamelCase				,  key(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)					)





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  []
	for x in range(1				,  5					):
		for y in range(1				,  6					):
			some_list.append((x, y)					)

	for x in range(15				,  20					):
		some_list.append((x, 17)					)

	for x in range(10				,  19					):
		for y in range(1				,  15					):
			some_list.append((x, y)					)

    # L block
	for x in range(1				,  4					):
		for y in range(12				,  19					):
			some_list.append((x, y)					)
	for x in range(3				,  13					):
		for y in range(16				,  19					):
			some_list.append((x, y)					)
	return some_list


__UpperCamelCase      :      Optional[int]     						=						{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

__UpperCamelCase      :      Optional[Any]     						=						[
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
__UpperCamelCase      :      Optional[Any]     						=						make_common_ground()


__UpperCamelCase      :      Dict     						=						blocks_blk
# hyper parameters
__UpperCamelCase      :      Union[str, Any]     						=						1
__UpperCamelCase      :      Union[str, Any]     						=						1
__UpperCamelCase      :      Optional[int]     						=						20
__UpperCamelCase      :      List[str]     						=						3  # one consistent and two other inconsistent

# start and end destination
__UpperCamelCase      :      str     						=						(0, 0)
__UpperCamelCase      :      str     						=						(n - 1, n - 1)

__UpperCamelCase      :      Optional[Any]     						=						1





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  {start: 0, goal: float("""inf"""					)}
	__lowercase             =  {start: -1, goal: -1}
	__lowercase             =  []
	__lowercase             =  set()

	for i in range(lowerCamelCase					):
		open_list.append(PriorityQueue()					)
		open_list[i].put(lowerCamelCase				,  key(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)					)

	__lowercase             =  []
	__lowercase             =  []
	while open_list[0].minkey() < float("""inf"""					):
		for i in range(1				,  lowerCamelCase					):
			# print(open_list[0].minkey(), open_list[i].minkey())
			if open_list[i].minkey() <= Wa * open_list[0].minkey():
				global t
				t += 1
				if g_function[goal] <= open_list[i].minkey():
					if g_function[goal] < float("""inf"""					):
						do_something(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
				else:
					__lowercase       ,						__lowercase             =  open_list[i].top_show()
					visited.add(lowerCamelCase					)
					expand_state(
					    lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  )
					close_list_inad.append(lowerCamelCase					)
			else:
				if g_function[goal] <= open_list[0].minkey():
					if g_function[goal] < float("""inf"""					):
						do_something(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
				else:
					__lowercase             =  open_list[0].top_show()
					visited.add(lowerCamelCase					)
					expand_state(
					    lowerCamelCase				,  0				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  )
					close_list_anchor.append(lowerCamelCase					)
	print("""No path found to goal"""					)
	print()
	for i in range(n - 1				,  -1				,  -1					):
		for j in range(lowerCamelCase					):
			if (j, i) in blocks:
				print("""#"""				,  end=""" """					)
			elif (j, i) in back_pointer:
				if (j, i) == (n - 1, n - 1):
					print("""*"""				,  end=""" """					)
				else:
					print("""-"""				,  end=""" """					)
			else:
				print("""*"""				,  end=""" """					)
			if (j, i) == (n - 1, n - 1):
				print("""<-- End position"""				,  end=""" """					)
		print()
	print("""^"""					)
	print("""Start position"""					)
	print()
	print("""# is an obstacle"""					)
	print("""- is the path taken by algorithm"""					)


if __name__ == "__main__":
						multi_a_star(start, goal, n_heuristic)

