



'''simple docstring'''

from __future__ import annotations

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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :list[list[int]]    ,	_SCREAMING_SNAKE_CASE      :list[int]    ,	_SCREAMING_SNAKE_CASE      :list[int]    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :list[list[int]]    ,	):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [
				    [0 for col in range(len(grid[0]       )       )] for row in range(len(_SCREAMING_SNAKE_CASE       )       )
				]  # the reference grid
				SCREAMING_SNAKE_CASE  :						Dict      					= 1
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [
				    [0 for col in range(len(grid[0]       )       )] for row in range(len(_SCREAMING_SNAKE_CASE       )       )
				]  # the action grid

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= init[0]
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= init[1]
				SCREAMING_SNAKE_CASE  :						Any      					= 0
				SCREAMING_SNAKE_CASE  :						List[Any]      					= g + heuristic[x][y]  # cost from starting cell to destination cell
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= [[f, g, x, y]]

				SCREAMING_SNAKE_CASE  :						List[str]      					= False  # flag that is set when search is complete
				SCREAMING_SNAKE_CASE  :						Any      					= 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  :						List[str]      					= cell.pop()
												SCREAMING_SNAKE_CASE  :						List[str]      					= next_cell[2]
												SCREAMING_SNAKE_CASE  :						Dict      					= next_cell[3]
												SCREAMING_SNAKE_CASE  :						str      					= next_cell[1]

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

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


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

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

	snake_case_			,				snake_case_			=     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])









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_			=     {
    """configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_			=     [
	    """TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimesformerModel""",
	    """TimesformerForVideoClassification""",
	    """TimesformerPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_timesformer import (
		    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimesformerForVideoClassification,
		    TimesformerModel,
		    TimesformerPreTrainedModel,
		)

else:
	import sys

	snake_case_			=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




