from __future__ import annotations

__UpperCamelCase	: Union[str, Any]    		=  [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]
def    A			(					_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						_lowercase	,						):
						SCREAMING_SNAKE_CASE							:					Any           =							[
						    [0 for col in range(len(grid[0]		)		)] for row in range(len(_lowercase		)		)
						]  # the reference grid
						SCREAMING_SNAKE_CASE							:					int           =							1
						SCREAMING_SNAKE_CASE							:					int           =							[
						    [0 for col in range(len(grid[0]		)		)] for row in range(len(_lowercase		)		)
						]  # the action grid

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

						SCREAMING_SNAKE_CASE							:					List[Any]           =							False  # flag that is set when search is complete
						SCREAMING_SNAKE_CASE							:					Tuple           =							False  # flag set if we can't find expand

						while not found and not resign:
												if len(_lowercase		) == 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							:					Tuple           =							next_cell[2]
																		SCREAMING_SNAKE_CASE							:					Dict           =							next_cell[3]
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							next_cell[1]

																		if x == goal[0] and y == goal[1]:
																								SCREAMING_SNAKE_CASE							:					Tuple           =							True
																		else:
																								for i in range(len(_lowercase		)		):  # to try out different valid actions
																														SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							x + DIRECTIONS[i][0]
																														SCREAMING_SNAKE_CASE							:					Tuple           =							y + DIRECTIONS[i][1]
																														if xa >= 0 and xa < len(_lowercase		) and ya >= 0 and ya < len(grid[0]		):
																																				if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																																										SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							g + cost
																																										SCREAMING_SNAKE_CASE							:					Any           =							ga + heuristic[xa][ya]
																																										cell.append([fa, ga, xa, ya]		)
																																										SCREAMING_SNAKE_CASE							:					Any           =							1
																																										SCREAMING_SNAKE_CASE							:					Optional[int]           =							i
						SCREAMING_SNAKE_CASE							:					str           =							[]
						SCREAMING_SNAKE_CASE							:					List[str]           =							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							:					Dict           =							x - DIRECTIONS[action[x][y]][0]
												SCREAMING_SNAKE_CASE							:					List[str]           =							y - DIRECTIONS[action[x][y]][1]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							xa
												SCREAMING_SNAKE_CASE							:					Dict           =							ya
												invpath.append([x, y]		)

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


if __name__ == "__main__":
					__UpperCamelCase	: 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],
					]

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

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

					__UpperCamelCase		,    __UpperCamelCase	: List[Any]    		=  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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase	: Tuple    		=  {
    'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
    'tokenization_ctrl': ['CTRLTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Dict    		=  [
					    'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'CTRLForSequenceClassification',
					    'CTRLLMHeadModel',
					    'CTRLModel',
					    'CTRLPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Any    		=  [
					    'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFCTRLForSequenceClassification',
					    'TFCTRLLMHeadModel',
					    'TFCTRLModel',
					    'TFCTRLPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
					from .tokenization_ctrl import CTRLTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ctrl import (
										    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CTRLForSequenceClassification,
										    CTRLLMHeadModel,
										    CTRLModel,
										    CTRLPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ctrl import (
										    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCTRLForSequenceClassification,
										    TFCTRLLMHeadModel,
										    TFCTRLModel,
										    TFCTRLPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase	: Optional[Any]    		=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





