'''simple docstring'''


from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
snake_case__					:      Optional[int]				      =  0

snake_case__					:      Optional[int]				      =  [
    [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],
]

snake_case__					:      Any				      =  [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

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






class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				pos_x
       UpperCAmelCase_							:       Optional[int]       =				pos_y
       UpperCAmelCase_							:       int       =				(pos_y, pos_x)
       UpperCAmelCase_							:       List[Any]       =				goal_x
       UpperCAmelCase_							:       Dict       =				goal_y
       UpperCAmelCase_							:       List[str]       =				g_cost
       UpperCAmelCase_							:       Union[str, Any]       =				parent
       UpperCAmelCase_							:       str       =				self.calculate_heuristic()
       UpperCAmelCase_							:       str       =				self.g_cost + self.h_cost


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				self.pos_x - self.goal_x
       UpperCAmelCase_							:       List[str]       =				self.pos_y - self.goal_y
       if HEURISTIC == 1:
            return abs(snake_case_						) + abs(snake_case_						)
       else:
            return sqrt(dy**2 + dx**2						)






  def __lt__(			self				,		snake_case_						):




       '''simple docstring'''

       return self.f_cost < other.f_cost







class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				Node(start[1]				,		start[0]				,		goal[1]				,		goal[0]				,		0				,		snake_case_						)
       UpperCAmelCase_							:       Union[str, Any]       =				Node(goal[1]				,		goal[0]				,		goal[1]				,		goal[0]				,		9_9_9_9_9				,		snake_case_						)

       UpperCAmelCase_							:       str       =				[self.start]
       UpperCAmelCase_							:       list[Node]       =				[]

       UpperCAmelCase_							:       Dict       =				False


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       while self.open_nodes:
            # Open Nodes are sorted using __lt__
            self.open_nodes.sort()
            UpperCAmelCase_							:       List[Any]       =				self.open_nodes.pop(0						)

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

            self.closed_nodes.append(snake_case_						)
            UpperCAmelCase_							:       int       =				self.get_successors(snake_case_						)

            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(snake_case_						)
                 else:
                      # retrieve the best current path
                      UpperCAmelCase_							:       Dict       =				self.open_nodes.pop(self.open_nodes.index(snake_case_						)						)

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

       return [self.start.pos]


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				[]
       for action in delta:
            UpperCAmelCase_							:       Optional[int]       =				parent.pos_x + action[1]
            UpperCAmelCase_							:       Any       =				parent.pos_y + action[0]
            if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(snake_case_						) - 1):
                 continue

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

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






  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				node
       UpperCAmelCase_							:       Dict       =				[]
       while current_node is not None:
            path.append((current_node.pos_y, current_node.pos_x)						)
            UpperCAmelCase_							:       Tuple       =				current_node.parent
       path.reverse()
       return path







class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				AStar(snake_case_				,		snake_case_						)
       UpperCAmelCase_							:       Dict       =				AStar(snake_case_				,		snake_case_						)
       UpperCAmelCase_							:       List[str]       =				False


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
            self.fwd_astar.open_nodes.sort()
            self.bwd_astar.open_nodes.sort()
            UpperCAmelCase_							:       List[str]       =				self.fwd_astar.open_nodes.pop(0						)
            UpperCAmelCase_							:       Any       =				self.bwd_astar.open_nodes.pop(0						)

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

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

            UpperCAmelCase_							:       Union[str, Any]       =				current_bwd_node
            UpperCAmelCase_							:       Tuple       =				current_fwd_node

            UpperCAmelCase_							:       Tuple       =				{
                self.fwd_astar: self.fwd_astar.get_successors(snake_case_						),
                self.bwd_astar: self.bwd_astar.get_successors(snake_case_						),
            }

            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(snake_case_						)
                      else:
                           # retrieve the best current path
                           UpperCAmelCase_							:       int       =				astar.open_nodes.pop(
                               astar.open_nodes.index(snake_case_						)						)

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

       return [self.fwd_astar.start.pos]






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				self.fwd_astar.retrace_path(snake_case_						)
       UpperCAmelCase_							:       Tuple       =				self.bwd_astar.retrace_path(snake_case_						)
       bwd_path.pop()
       bwd_path.reverse()
       UpperCAmelCase_							:       List[Any]       =				fwd_path + bwd_path
       return path


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

 snake_case__					:      int				      =  time.time()
 snake_case__					:      str				      =  AStar(init, goal)
 snake_case__					:      Tuple				      =  a_star.search()
 snake_case__					:      List[str]				      =  time.time() - start_time
 print(f'''AStar execution time = {end_time:f} seconds''')

 snake_case__					:      Optional[int]				      =  time.time()
 snake_case__					:      Optional[Any]				      =  BidirectionalAStar(init, goal)
 snake_case__					:      List[str]				      =  time.time() - bd_start_time
 print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')







'''simple docstring'''


import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image
else:






 class 					__SCREAMING_SNAKE_CASE						:







   '''simple docstring'''






   @staticmethod
   def _UpperCamelCase					(			*snake_case_				,		**snake_case_						):




        '''simple docstring'''

        pass


def  _lowerCamelCase   (				lowerCamelCase_ :							Image       ):



     """simple docstring"""





     UpperCAmelCase_							:       Dict       =				hashlib.mda(image.tobytes()       )
     return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[int]						   =		MODEL_FOR_DEPTH_ESTIMATION_MAPPING


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				DepthEstimationPipeline(model=snake_case_				,		image_processor=snake_case_						)
       return depth_estimator, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
       self.assertEqual({'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)}				,		snake_case_						)
       import datasets

       UpperCAmelCase_							:       Optional[int]       =				datasets.load_dataset('hf-internal-testing/fixtures_image_utils'				,		'image'				,		split='test'						)
       UpperCAmelCase_							:       Union[str, Any]       =				depth_estimator(
           [
               Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						),
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               # RGBA
               dataset[0]['file'],
               # LA
               dataset[1]['file'],
               # L
               dataset[2]['file'],
           ]						)
       self.assertEqual(
           [
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
               {'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)},
           ]				,		snake_case_				,		)


  @require_tf
  @unittest.skip('Depth estimation is not implemented in TF'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  @slow
  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				'Intel/dpt-large'
       UpperCAmelCase_							:       Dict       =				pipeline('depth-estimation'				,		model=snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'						)
       UpperCAmelCase_							:       Union[str, Any]       =				hashimage(outputs['depth']						)

       # This seems flaky.
       # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
       self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()						)				,		29.3_04						)
       self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()						)				,		2.6_62						)






  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'						)






