



import numpy as np



class 	lowerCamelCase      :







					'''simple docstring'''







					def __init__(			self     ):
							UpperCAmelCase_						=				(0, 0)
							UpperCAmelCase_						=				None
							UpperCAmelCase_						=				0
							UpperCAmelCase_						=				0
							UpperCAmelCase_						=				0



					def __eq__(			self    ,	lowerCAmelCase     ):
							return self.position == cell.position



					def    A__		(			self     ):
							print(self.position     )





class 	lowerCamelCase      :







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase=(5, 5)     ):
							UpperCAmelCase_						=				np.zeros(lowerCAmelCase     )
							UpperCAmelCase_						=				world_size[0]
							UpperCAmelCase_						=				world_size[1]



					def    A__		(			self     ):
							print(self.w     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				[
							    (-1, -1),
							    (-1, 0),
							    (-1, 1),
							    (0, -1),
							    (0, 1),
							    (1, -1),
							    (1, 0),
							    (1, 1),
							]
							UpperCAmelCase_						=				cell.position[0]
							UpperCAmelCase_						=				cell.position[1]
							UpperCAmelCase_						=				[]
							for n in neughbour_cord:
									UpperCAmelCase_						=				current_x + n[0]
									UpperCAmelCase_						=				current_y + n[1]
									if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
											UpperCAmelCase_						=				Cell()
											UpperCAmelCase_						=				(x, y)
											UpperCAmelCase_						=				cell
											neighbours.append(lowerCAmelCase     )
							return neighbours







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Optional[int]:
		UpperCAmelCase_						=				[]
		UpperCAmelCase_						=				[]
		_open.append(__SCREAMING_SNAKE_CASE )

		while _open:
				UpperCAmelCase_						=				np.argmin([n.f for n in _open] )
				UpperCAmelCase_						=				_open[min_f]
				_closed.append(_open.pop(__SCREAMING_SNAKE_CASE ) )
				if current == goal:
						break
				for n in world.get_neigbours(__SCREAMING_SNAKE_CASE ):
						for c in _closed:
								if c == n:
										continue
						UpperCAmelCase_						=				current.g + 1
						UpperCAmelCase_					,						UpperCAmelCase_						=				n.position
						UpperCAmelCase_					,						UpperCAmelCase_						=				goal.position
						UpperCAmelCase_						=				(ya - ya) ** 2 + (xa - xa) ** 2
						UpperCAmelCase_						=				n.h + n.g

						for c in _open:
								if c == n and c.f < n.f:
										continue
						_open.append(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				[]
		while current.parent is not None:
				path.append(current.position )
				UpperCAmelCase_						=				current.parent
		path.append(current.position )
		return path[::-1]


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE     				=						Gridworld()
			# Start position and goal
			SCREAMING_SNAKE_CASE     				=						Cell()
			SCREAMING_SNAKE_CASE     				=						(0, 0)
			SCREAMING_SNAKE_CASE     				=						Cell()
			SCREAMING_SNAKE_CASE     				=						(4, 4)
			print(f'''path from {start.position} to {goal.position}''')
			SCREAMING_SNAKE_CASE     				=						astar(world, start, goal)
			#   Just for visual reasons.
			for i in s:
						SCREAMING_SNAKE_CASE     				=						1
			print(world.w)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE     				=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTBigCodeForSequenceClassification",
			    "GPTBigCodeForTokenClassification",
			    "GPTBigCodeForCausalLM",
			    "GPTBigCodeModel",
			    "GPTBigCodePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_bigcode import (
						    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTBigCodeForCausalLM,
						    GPTBigCodeForSequenceClassification,
						    GPTBigCodeForTokenClassification,
						    GPTBigCodeModel,
						    GPTBigCodePreTrainedModel,
						)


else:
			import sys

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

