




from __future__ import annotations

SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    )			->							tuple[list[list[int]], list[list[int]]]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[
			    [0 for col in range(len(grid[0] ) )] for row in range(len(SCREAMING_SNAKE_CASE ) )
			]  # the reference grid
			SCREAMING_SNAKE_CASE_								=						1
			SCREAMING_SNAKE_CASE_								=						[
			    [0 for col in range(len(grid[0] ) )] for row in range(len(SCREAMING_SNAKE_CASE ) )
			]  # the action grid

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

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

			while not found and not resign:
						if len(SCREAMING_SNAKE_CASE ) == 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()
									SCREAMING_SNAKE_CASE_								=						cell.pop()
									SCREAMING_SNAKE_CASE_								=						next_cell[2]
									SCREAMING_SNAKE_CASE_								=						next_cell[3]
									SCREAMING_SNAKE_CASE_								=						next_cell[1]

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

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


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     List[str]		    =       [
				    [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],
				]

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

				# the cost map which pushes the path closer to the goal
				SCREAMING_SNAKE_CASE__     :     Dict		    =       [[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])):
												SCREAMING_SNAKE_CASE__     :     str		    =       abs(i - goal[0]) + abs(j - goal[1])
												if grid[i][j] == 1:
																# added extra penalty in the heuristic map
																SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       99

				SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__     :     List[str]		    =       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])






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :     str		    =       {
    "google/mobilenet_v2_1.4_224": "https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json",
    "google/mobilenet_v2_1.0_224": "https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json",
    "google/mobilenet_v2_0.75_160": "https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json",
    "google/mobilenet_v2_0.35_96": "https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}



class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''mobilenet_v2'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=3      ,     SCREAMING_SNAKE_CASE=224      ,     SCREAMING_SNAKE_CASE=1.0      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=6      ,     SCREAMING_SNAKE_CASE=32      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE="relu6"      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=0.8      ,     SCREAMING_SNAKE_CASE=0.0_2      ,     SCREAMING_SNAKE_CASE=0.0_0_1      ,     SCREAMING_SNAKE_CASE=255      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					Dict:




				"""simple docstring"""



				super().__init__(**SCREAMING_SNAKE_CASE      )

				if depth_multiplier <= 0:
							raise ValueError('depth_multiplier must be greater than zero.'      )

				SCREAMING_SNAKE_CASE_								=						num_channels
				SCREAMING_SNAKE_CASE_								=						image_size
				SCREAMING_SNAKE_CASE_								=						depth_multiplier
				SCREAMING_SNAKE_CASE_								=						depth_divisible_by
				SCREAMING_SNAKE_CASE_								=						min_depth
				SCREAMING_SNAKE_CASE_								=						expand_ratio
				SCREAMING_SNAKE_CASE_								=						output_stride
				SCREAMING_SNAKE_CASE_								=						first_layer_is_expansion
				SCREAMING_SNAKE_CASE_								=						finegrained_output
				SCREAMING_SNAKE_CASE_								=						hidden_act
				SCREAMING_SNAKE_CASE_								=						tf_padding
				SCREAMING_SNAKE_CASE_								=						classifier_dropout_prob
				SCREAMING_SNAKE_CASE_								=						initializer_range
				SCREAMING_SNAKE_CASE_								=						layer_norm_eps
				SCREAMING_SNAKE_CASE_								=						semantic_loss_ignore_index




class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= version.parse('''1.11'''     )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				return OrderedDict([('pixel_values', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				if self.task == "image-classification":
							return OrderedDict([('logits', {0: 'batch'})]      )
				else:
							return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					float:




				"""simple docstring"""



				return 1e-4
