





'''simple docstring'''




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__					:			Optional[int]          =     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 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       *UpperCamelCase_      ,       UpperCamelCase_=None      ,       UpperCamelCase_=None      ,       UpperCamelCase_=None      ,       **UpperCamelCase_		)      ->		Dict:
		super().__init__(*UpperCamelCase_      ,       **UpperCamelCase_		)
		snake_case__	       =   eval_examples
		snake_case__	       =   post_process_function
		snake_case__	       =   quant_trainer_args
		snake_case__	       =   128  # default number of calibration samples





	def   _snake_case   (		self      ,       UpperCamelCase_=None		)      ->		Optional[Any]:
		if calib_dataset is None and self.calib_dataset is None:
			raise ValueError('Trainer: calibration requires an calib_dataset.'		)
		snake_case__	       =   calib_dataset if calib_dataset is not None else self.calib_dataset

		snake_case__	       =   self._remove_unused_columns(UpperCamelCase_      ,       description='Calibration'		)

		return DataLoader(
		    UpperCamelCase_      ,       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=UpperCamelCase_      ,       )





	def   _snake_case   (		self      ,       UpperCamelCase_=None		)      ->		Any:
		snake_case__	       =   self.train_dataset if calib_dataset is None else calib_dataset
		snake_case__	       =   self.get_calib_dataloader(UpperCamelCase_		)

		snake_case__	       =   self.model
		quant_trainer.configure_model(UpperCamelCase_      ,       self.quant_trainer_args      ,       calib=UpperCamelCase_		)
		model.eval()
		quant_trainer.enable_calibration(UpperCamelCase_		)

		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(UpperCamelCase_		):
			# Prediction step
			snake_case__       ,  snake_case__       ,  snake_case__	       =   self.prediction_step(UpperCamelCase_      ,       UpperCamelCase_      ,       prediction_loss_only=UpperCamelCase_		)
			if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
				break

		quant_trainer.finish_calibration(UpperCamelCase_      ,       self.quant_trainer_args		)
		snake_case__	       =   model





	def   _snake_case   (		self      ,       UpperCamelCase_=None      ,       UpperCamelCase_=None      ,       UpperCamelCase_=None      ,       UpperCamelCase_ = "eval"		)      ->		List[Any]:
		snake_case__	       =   self.eval_dataset if eval_dataset is None else eval_dataset
		snake_case__	       =   self.get_eval_dataloader(UpperCamelCase_		)
		snake_case__	       =   self.eval_examples if eval_examples is None else eval_examples

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

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

			# Prefix all keys with metric_key_prefix + '_'
			for key in list(metrics.keys()		):
				if not key.startswith(F'''{metric_key_prefix}_'''		):
					snake_case__	       =   metrics.pop(UpperCamelCase_		)

			self.log(UpperCamelCase_		)
		else:
			snake_case__	       =   {}

		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()		)

		snake_case__	       =   self.callback_handler.on_evaluate(self.args      ,       self.state      ,       self.control      ,       UpperCamelCase_		)
		return metrics





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_=None      ,       UpperCamelCase_ = "test"		)      ->		Tuple:
		snake_case__	       =   self.get_test_dataloader(UpperCamelCase_		)

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

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

		snake_case__	       =   self.post_process_function(UpperCamelCase_      ,       UpperCamelCase_      ,       output.predictions      ,       'predict'		)
		snake_case__	       =   self.compute_metrics(UpperCamelCase_		)

		# Prefix all keys with metric_key_prefix + '_'
		for key in list(metrics.keys()		):
			if not key.startswith(F'''{metric_key_prefix}_'''		):
				snake_case__	       =   metrics.pop(UpperCamelCase_		)

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





	def   _snake_case   (		self      ,       UpperCamelCase_="./"		)      ->		Union[str, Any]:
		snake_case__	       =   self.eval_dataset
		snake_case__	       =   self.get_eval_dataloader(UpperCamelCase_		)

		snake_case__	       =   next(iter(UpperCamelCase_		)		)

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

		# convert to tuple
		snake_case__	       =   tuple(v.to(UpperCamelCase_		) for k, v in batch.items()		)

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

		snake_case__	       =   True

		snake_case__	       =   self.model.to(UpperCamelCase_		)

		model.eval()
		model.float()

		snake_case__	       =   model.module if hasattr(UpperCamelCase_      ,       'module'		) else model
		quant_trainer.configure_model(UpperCamelCase_      ,       self.quant_trainer_args		)

		snake_case__	       =   os.path.join(UpperCamelCase_      ,       'model.onnx'		)
		logger.info(F'''exporting model to {output_model_file}'''		)

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

		torch.onnx.export(
		    UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       export_params=UpperCamelCase_      ,       opset_version=13      ,       do_constant_folding=UpperCamelCase_      ,       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=UpperCamelCase_      ,       )
		logger.info('onnx export finished'		)










'''simple docstring'''




from __future__ import annotations

a__					:			Optional[int]          =     list[tuple[int, int]]

a__					:			List[Any]          =     [
    [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__					:			Optional[int]          =     ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       )      ->		Any:
		snake_case__	       =   pos_x
		snake_case__	       =   pos_y
		snake_case__	       =   (pos_y, pos_x)
		snake_case__	       =   goal_x
		snake_case__	       =   goal_y
		snake_case__	       =   g_cost
		snake_case__	       =   parent
		snake_case__	       =   self.calculate_heuristic()





	def   _snake_case   (		self		)      ->		float:
		snake_case__	       =   abs(self.pos_x - self.goal_x		)
		snake_case__	       =   abs(self.pos_y - self.goal_y		)
		return dx + dy





	def __lt__(		self      ,       UpperCamelCase_		)      ->		bool:
		return self.f_cost < other.f_cost






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Any:
		snake_case__	       =   Node(start[1]      ,       start[0]      ,       goal[1]      ,       goal[0]      ,       0      ,       UpperCamelCase_		)
		snake_case__	       =   Node(goal[1]      ,       goal[0]      ,       goal[1]      ,       goal[0]      ,       9_9999      ,       UpperCamelCase_		)

		snake_case__	       =   [self.start]
		snake_case__	       =   []

		snake_case__	       =   False





	def   _snake_case   (		self		)      ->		Path | None:
		while self.open_nodes:
			# Open Nodes are sorted using __lt__
			self.open_nodes.sort()
			snake_case__	       =   self.open_nodes.pop(0		)

			if current_node.pos == self.target.pos:
				snake_case__	       =   True
				return self.retrace_path(UpperCamelCase_		)

			self.closed_nodes.append(UpperCamelCase_		)
			snake_case__	       =   self.get_successors(UpperCamelCase_		)

			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(UpperCamelCase_		)
				else:
					# retrieve the best current path
					snake_case__	       =   self.open_nodes.pop(self.open_nodes.index(UpperCamelCase_		)		)

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

		if not self.reached:
			return [self.start.pos]
		return None





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		list[Node]:
		snake_case__	       =   []
		for action in delta:
			snake_case__	       =   parent.pos_x + action[1]
			snake_case__	       =   parent.pos_y + action[0]

			if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(UpperCamelCase_		) - 1):
				continue

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

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





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Path:
		snake_case__	       =   node
		snake_case__	       =   []
		while current_node is not None:
			path.append((current_node.pos_y, current_node.pos_x)		)
			snake_case__	       =   current_node.parent
		path.reverse()
		return path


if __name__ == "__main__":
			a__					:			List[str]          =     (0, 0)
			a__					:			Dict          =     (len(grid) - 1, len(grid[0]) - 1)
			for elem in grid:
						print(elem)

			print('''------''')

			a__					:			Optional[int]          =     GreedyBestFirst(init, goal)
			a__					:			Optional[int]          =     greedy_bf.search()
			if path:
						for pos_x, pos_y in path:
									a__					:			Tuple          =     2

						for elem in grid:
									print(elem)



