



from __future__ import annotations

__snake_case						:str      		=     [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , ):
    __a						       =				[
        [0 for col in range(len(grid[0]				)				)] for row in range(len(_UpperCAmelCase				)				)
    ]  # the reference grid
    __a						       =				1
    __a						       =				[
        [0 for col in range(len(grid[0]				)				)] for row in range(len(_UpperCAmelCase				)				)
    ]  # the action grid

    __a						       =				init[0]
    __a						       =				init[1]
    __a						       =				0
    __a						       =				g + heuristic[x][y]  # cost from starting cell to destination cell
    __a						       =				[[f, g, x, y]]

    __a						       =				False  # flag that is set when search is complete
    __a						       =				False  # flag set if we can't find expand

    while not found and not resign:
        if len(_UpperCAmelCase				) == 0:
            raise ValueError('''Algorithm is unable to find solution'''				)
        else:  # to choose the least costliest action so as to move closer to the goal
            cell.sort()
            cell.reverse()
            __a						       =				cell.pop()
            __a						       =				next_cell[2]
            __a						       =				next_cell[3]
            __a						       =				next_cell[1]

            if x == goal[0] and y == goal[1]:
                __a						       =				True
            else:
                for i in range(len(_UpperCAmelCase				)				):  # to try out different valid actions
                    __a						       =				x + DIRECTIONS[i][0]
                    __a						       =				y + DIRECTIONS[i][1]
                    if xa >= 0 and xa < len(_UpperCAmelCase				) and ya >= 0 and ya < len(grid[0]				):
                        if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                            __a						       =				g + cost
                            __a						       =				ga + heuristic[xa][ya]
                            cell.append([fa, ga, xa, ya]				)
                            __a						       =				1
                            __a						       =				i
    __a						       =				[]
    __a						       =				goal[0]
    __a						       =				goal[1]
    invpath.append([x, y]				)  # we get the reverse path from here
    while x != init[0] or y != init[1]:
        __a						       =				x - DIRECTIONS[action[x][y]][0]
        __a						       =				y - DIRECTIONS[action[x][y]][1]
        __a						       =				xa
        __a						       =				ya
        invpath.append([x, y]				)

    __a						       =				[]
    for i in range(len(_UpperCAmelCase				)				):
        path.append(invpath[len(_UpperCAmelCase				) - 1 - i]				)
    return path, action


if __name__ == "__main__":
 __snake_case						:Dict      		=     [
     [0, 1, 0, 0, 0, 0],
     [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
     [0, 1, 0, 0, 0, 0],
     [0, 1, 0, 0, 1, 0],
     [0, 0, 0, 0, 1, 0],
 ]

 __snake_case						:List[Any]      		=     [0, 0]
 # all coordinates are given in format [y,x]
 __snake_case						:Tuple      		=     [len(grid) - 1, len(grid[0]) - 1]
 __snake_case						:Any      		=     1

 # the cost map which pushes the path closer to the goal
 __snake_case						:Optional[int]      		=     [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
 for i in range(len(grid)):
  for j in range(len(grid[0])):
   __snake_case						:Union[str, Any]      		=     abs(i - goal[0]) + abs(j - goal[1])
   if grid[i][j] == 1:
    # added extra penalty in the heuristic map
    __snake_case						:int      		=     99

 __snake_case		,__snake_case						:int      		=     search(grid, init, goal, cost, heuristic)

 print('''ACTION MAP''')
 for i in range(len(action)):
  print(action[i])

 for i in range(len(path)):
  print(path[i])










import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


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

class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     List[Any]     ,		*__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           super().__init__(*__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           __a						       =				eval_examples
           __a						       =				post_process_function



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Optional[Dataset] = None     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=None     ,		__SCREAMING_SNAKE_CASE							:     Optional[List[str]] = None     ,		__SCREAMING_SNAKE_CASE							:     str = "eval"     ,		**__SCREAMING_SNAKE_CASE							:     Any     ,		):


           '''simple docstring'''

           __a						       =				gen_kwargs.copy()
           __a						       =				(
               gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length''') is not None else self.args.generation_max_length
           )
           __a						       =				(
               gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams''') is not None else self.args.generation_num_beams
           )
           __a						       =				gen_kwargs

           __a						       =				self.eval_dataset if eval_dataset is None else eval_dataset
           __a						       =				self.get_eval_dataloader(__SCREAMING_SNAKE_CASE)
           __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						       =				time.time()
           __a						       =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
               __a						       =				eval_loop(
                   __SCREAMING_SNAKE_CASE     ,		description='''Evaluation'''     ,		prediction_loss_only=True if compute_metrics is None else None     ,		ignore_keys=__SCREAMING_SNAKE_CASE     ,		metric_key_prefix=__SCREAMING_SNAKE_CASE     ,		)
           finally:
               __a						       =				compute_metrics
           __a						       =				self.args.eval_batch_size * self.args.world_size
           if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
               start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
           output.metrics.update(
               speed_metrics(
                   __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		num_samples=output.num_samples     ,		num_steps=math.ceil(output.num_samples / total_batch_size)     ,		))

           if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
               # Only the main node write the results by default
               __a						       =				self.post_process_function(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
               __a						       =				self.compute_metrics(__SCREAMING_SNAKE_CASE)

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

               metrics.update(output.metrics)
           else:
               __a						       =				output.metrics

           if self.args.should_log:
               # Only the main node log the results by default
               self.log(__SCREAMING_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())

           __a						       =				self.callback_handler.on_evaluate(self.args     ,		self.state     ,		self.control     ,		__SCREAMING_SNAKE_CASE)
           return metrics





       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		__SCREAMING_SNAKE_CASE							:     str = "test"     ,		**__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           __a						       =				gen_kwargs.copy()

           __a						       =				self.get_test_dataloader(__SCREAMING_SNAKE_CASE)

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

           __a						       =				self.args.eval_batch_size * self.args.world_size
           if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
               start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
           output.metrics.update(
               speed_metrics(
                   __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		num_samples=output.num_samples     ,		num_steps=math.ceil(output.num_samples / total_batch_size)     ,		))
           if self.post_process_function is None or self.compute_metrics is None:
               return output

           __a						       =				self.post_process_function(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		'''predict''')
           __a						       =				self.compute_metrics(__SCREAMING_SNAKE_CASE)

           # Prefix all keys with metric_key_prefix + '_'
           for key in list(metrics.keys()):
               if not key.startswith(F'{metric_key_prefix}_'):
                   __a						       =				metrics.pop(__SCREAMING_SNAKE_CASE)
           metrics.update(output.metrics)
           return PredictionOutput(predictions=predictions.predictions     ,		label_ids=predictions.label_ids     ,		metrics=__SCREAMING_SNAKE_CASE)





