






from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
__A      =   0

__A      =   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

__A      =   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

__A      =   tuple[int, int]





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   Union[str, Any]		,     __A:   int		,     __A:   int		,     __A:   int		,     __A:   int		,     __A:   int		,     __A:   Node | None		,     )    ->				None:
												_A								= pos_x
												_A								= pos_y
												_A								= (pos_y, pos_x)
												_A								= goal_x
												_A								= goal_y
												_A								= g_cost
												_A								= parent
												_A								= self.calculate_heuristic()
												_A								= self.g_cost + self.h_cost

							def       __A (  self:   int   )    ->				float:
												_A								= self.pos_x - self.goal_x
												_A								= self.pos_y - self.goal_y
												if HEURISTIC == 1:
																	return abs(__A   ) + abs(__A   )
												else:
																	return sqrt(dy**2 + dx**2   )



							def __lt__(  self:   List[str]		,     __A:   Node   )    ->				bool:
												return self.f_cost < other.f_cost





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   Optional[int]		,     __A:   TPosition		,     __A:   TPosition   )    ->				str:
												_A								= Node(start[1]		,     start[0]		,     goal[1]		,     goal[0]		,     0		,     __A   )
												_A								= Node(goal[1]		,     goal[0]		,     goal[1]		,     goal[0]		,     9_99_99		,     __A   )

												_A								= [self.start]
												_A								= []

												_A								= False

							def       __A (  self:   Optional[Any]   )    ->				list[TPosition]:
												while self.open_nodes:
																	# Open Nodes are sorted using __lt__
																	self.open_nodes.sort()
																	_A								= self.open_nodes.pop(0   )

																	if current_node.pos == self.target.pos:
																						return self.retrace_path(__A   )

																	self.closed_nodes.append(__A   )
																	_A								= self.get_successors(__A   )

																	for child_node in successors:
																						if child_node in self.closed_nodes:
																											continue

																						if child_node not in self.open_nodes:
																											self.open_nodes.append(__A   )
																						else:
																											# retrieve the best current path
																											_A								= self.open_nodes.pop(self.open_nodes.index(__A   )   )

																											if child_node.g_cost < better_node.g_cost:
																																self.open_nodes.append(__A   )
																											else:
																																self.open_nodes.append(__A   )

												return [self.start.pos]

							def       __A (  self:   str		,     __A:   Node   )    ->				list[Node]:
												_A								= []
												for action in delta:
																	_A								= parent.pos_x + action[1]
																	_A								= parent.pos_y + action[0]
																	if not (0 <= pos_x <= len(grid[0]   ) - 1 and 0 <= pos_y <= len(__A   ) - 1):
																						continue

																	if grid[pos_y][pos_x] != 0:
																						continue

																	successors.append(
																	    Node(
																	        __A		,     __A		,     self.target.pos_y		,     self.target.pos_x		,     parent.g_cost + 1		,     __A		,     )   )
												return successors



							def       __A (  self:   str		,     __A:   Node | None   )    ->				list[TPosition]:
												_A								= node
												_A								= []
												while current_node is not None:
																	path.append((current_node.pos_y, current_node.pos_x)   )
																	_A								= current_node.parent
												path.reverse()
												return path





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   Union[str, Any]		,     __A:   TPosition		,     __A:   TPosition   )    ->				None:
												_A								= AStar(__A		,     __A   )
												_A								= AStar(__A		,     __A   )
												_A								= False

							def       __A (  self:   Tuple   )    ->				list[TPosition]:
												while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
																	self.fwd_astar.open_nodes.sort()
																	self.bwd_astar.open_nodes.sort()
																	_A								= self.fwd_astar.open_nodes.pop(0   )
																	_A								= self.bwd_astar.open_nodes.pop(0   )

																	if current_bwd_node.pos == current_fwd_node.pos:
																						return self.retrace_bidirectional_path(
																						    __A		,     __A   )

																	self.fwd_astar.closed_nodes.append(__A   )
																	self.bwd_astar.closed_nodes.append(__A   )

																	_A								= current_bwd_node
																	_A								= current_fwd_node

																	_A								= {
																	    self.fwd_astar: self.fwd_astar.get_successors(__A   ),
																	    self.bwd_astar: self.bwd_astar.get_successors(__A   ),
																	}

																	for astar in [self.fwd_astar, self.bwd_astar]:
																						for child_node in successors[astar]:
																											if child_node in astar.closed_nodes:
																																continue

																											if child_node not in astar.open_nodes:
																																astar.open_nodes.append(__A   )
																											else:
																																# retrieve the best current path
																																_A								= astar.open_nodes.pop(
																																    astar.open_nodes.index(__A   )   )

																																if child_node.g_cost < better_node.g_cost:
																																					astar.open_nodes.append(__A   )
																																else:
																																					astar.open_nodes.append(__A   )

												return [self.fwd_astar.start.pos]



							def       __A (  self:   List[str]		,     __A:   Node		,     __A:   Node   )    ->				list[TPosition]:
												_A								= self.fwd_astar.retrace_path(__A   )
												_A								= self.bwd_astar.retrace_path(__A   )
												bwd_path.pop()
												bwd_path.reverse()
												_A								= fwd_path + bwd_path
												return path


if __name__ == "__main__":
				# all coordinates are given in format [y,x]
				__A      =   (0, 0)
				__A      =   (len(grid) - 1, len(grid[0]) - 1)
				for elem in grid:
								print(elem)

				__A      =   time.time()
				__A      =   AStar(init, goal)
				__A      =   a_star.search()
				__A      =   time.time() - start_time
				print(f'AStar execution time = {end_time:f} seconds')

				__A      =   time.time()
				__A      =   BidirectionalAStar(init, goal)
				__A      =   time.time() - bd_start_time
				print(f'BidirectionalAStar execution time = {bd_end_time:f} seconds')











import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


__A      =				logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm
				import torch_xla.debug.metrics as met





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							def __init__(  self:   int		,     *__A:   str		,     __A:   List[Any]=None		,     __A:   Union[str, Any]=None		,     __A:   List[Any]=None		,     **__A:   int   )    ->				List[Any]:
												super().__init__(*__A		,     **__A   )
												_A								= eval_examples
												_A								= post_process_function
												_A								= quant_trainer_args
												_A								= 1_28  # default number of calibration samples

							def       __A (  self:   Union[str, Any]		,     __A:   List[Any]=None   )    ->				Optional[Any]:
												if calib_dataset is None and self.calib_dataset is None:
																	raise ValueError('''Trainer: calibration requires an calib_dataset.'''   )
												_A								= calib_dataset if calib_dataset is not None else self.calib_dataset

												_A								= self._remove_unused_columns(__A		,     description='''Calibration'''   )

												return DataLoader(
												    __A		,     batch_size=self.args.eval_batch_size		,     collate_fn=self.data_collator		,     drop_last=self.args.dataloader_drop_last		,     num_workers=self.args.dataloader_num_workers		,     pin_memory=self.args.dataloader_pin_memory		,     shuffle=__A		,     )

							def       __A (  self:   List[Any]		,     __A:   Any=None   )    ->				Optional[int]:
												_A								= self.train_dataset if calib_dataset is None else calib_dataset
												_A								= self.get_calib_dataloader(__A   )

												_A								= self.model
												quant_trainer.configure_model(__A		,     self.quant_trainer_args		,     calib=__A   )
												model.eval()
												quant_trainer.enable_calibration(__A   )

												logger.info('''***** Running calibration *****'''   )
												logger.info(f"""  Num examples = {self.calib_num}"""   )
												logger.info(f"""  Batch size = {calib_dataloader.batch_size}"""   )

												for step, inputs in enumerate(__A   ):
																	# Prediction step
																	_A							,_A							,_A								= self.prediction_step(__A		,     __A		,     prediction_loss_only=__A   )
																	if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
																						break

												quant_trainer.finish_calibration(__A		,     self.quant_trainer_args   )
												_A								= model

							def       __A (  self:   Any		,     __A:   Dict=None		,     __A:   Tuple=None		,     __A:   List[Any]=None		,     __A:   str = "eval"   )    ->				int:
												_A								= self.eval_dataset if eval_dataset is None else eval_dataset
												_A								= self.get_eval_dataloader(__A   )
												_A								= self.eval_examples if eval_examples is None else eval_examples

												# Temporarily disable metric computation, we will do it in the loop here.
												_A								= self.compute_metrics
												_A								= None
												_A								= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
												try:
																	_A								= eval_loop(
																	    __A		,     description='''Evaluation'''		,     prediction_loss_only=True if compute_metrics is None else None		,     ignore_keys=__A		,     )
												finally:
																	_A								= compute_metrics

												if self.post_process_function is not None and self.compute_metrics is not None:
																	_A								= self.post_process_function(__A		,     __A		,     output.predictions   )
																	_A								= self.compute_metrics(__A   )

																	# Prefix all keys with metric_key_prefix + '_'
																	for key in list(metrics.keys()   ):
																						if not key.startswith(f"""{metric_key_prefix}_"""   ):
																											_A								= metrics.pop(__A   )

																	self.log(__A   )
												else:
																	_A								= {}

												if self.args.tpu_metrics_debug or self.args.debug:
																	# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
																	xm.master_print(met.metrics_report()   )

												_A								= self.callback_handler.on_evaluate(self.args		,     self.state		,     self.control		,     __A   )
												return metrics

							def       __A (  self:   Union[str, Any]		,     __A:   Optional[int]		,     __A:   int		,     __A:   List[Any]=None		,     __A:   str = "test"   )    ->				Union[str, Any]:
												_A								= self.get_test_dataloader(__A   )

												# Temporarily disable metric computation, we will do it in the loop here.
												_A								= self.compute_metrics
												_A								= None
												_A								= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
												try:
																	_A								= eval_loop(
																	    __A		,     description='''Prediction'''		,     prediction_loss_only=True if compute_metrics is None else None		,     ignore_keys=__A		,     )
												finally:
																	_A								= compute_metrics

												if self.post_process_function is None or self.compute_metrics is None:
																	return output

												_A								= self.post_process_function(__A		,     __A		,     output.predictions		,     '''predict'''   )
												_A								= self.compute_metrics(__A   )

												# Prefix all keys with metric_key_prefix + '_'
												for key in list(metrics.keys()   ):
																	if not key.startswith(f"""{metric_key_prefix}_"""   ):
																						_A								= metrics.pop(__A   )

												return PredictionOutput(predictions=predictions.predictions		,     label_ids=predictions.label_ids		,     metrics=__A   )



							def       __A (  self:   Tuple		,     __A:   Optional[Any]="./"   )    ->				List[str]:
												_A								= self.eval_dataset
												_A								= self.get_eval_dataloader(__A   )

												_A								= next(iter(__A   )   )

												# saving device - to make it consistent
												_A								= torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''   )

												# convert to tuple
												_A								= tuple(v.to(__A   ) for k, v in batch.items()   )

												logger.info('''Converting model to be onnx compatible'''   )
												from pytorch_quantization.nn import TensorQuantizer

												_A								= True

												_A								= self.model.to(__A   )

												model.eval()
												model.float()

												_A								= model.module if hasattr(__A		,     '''module'''   ) else model
												quant_trainer.configure_model(__A		,     self.quant_trainer_args   )

												_A								= os.path.join(__A		,     '''model.onnx'''   )
												logger.info(f"""exporting model to {output_model_file}"""   )

												_A								= {0: '''batch_size''', 1: '''seq_len'''}

												torch.onnx.export(
												    __A		,     __A		,     __A		,     export_params=__A		,     opset_version=13		,     do_constant_folding=__A		,     input_names=['''input_ids''', '''attention_mask''', '''token_type_ids''']		,     output_names=['''output_start_logits''', '''output_end_logits''']		,     dynamic_axes={
												        '''input_ids''': axes,
												        '''attention_mask''': axes,
												        '''token_type_ids''': axes,
												        '''output_start_logits''': axes,
												        '''output_end_logits''': axes,
												    }		,     verbose=__A		,     )
												logger.info('''onnx export finished'''   )



