

'''simple docstring'''


import numpy as np



class 					lowercase						:


							def __init__(			self)							->				List[Any]:
								UpperCAmelCase_		:      str       	=					(0, 0)
								UpperCAmelCase_		:      Union[str, Any]       	=					None
								UpperCAmelCase_		:      Union[str, Any]       	=					0
								UpperCAmelCase_		:      int       	=					0
								UpperCAmelCase_		:      Optional[int]       	=					0


							def __eq__(			self    ,			_snake_case)							->				List[str]:
								return self.position == cell.position





							def 						_snake_case							(			self)							->				List[Any]:
								print(self.position)



class 					lowercase						:


							def __init__(			self    ,			_snake_case=(5, 5))							->				str:
								UpperCAmelCase_		:      List[Any]       	=					np.zeros(_snake_case)
								UpperCAmelCase_		:      Any       	=					world_size[0]
								UpperCAmelCase_		:      Dict       	=					world_size[1]


							def 						_snake_case							(			self)							->				Tuple:
								print(self.w)





							def 						_snake_case							(			self    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      str       	=					[
								    (-1, -1),
								    (-1, 0),
								    (-1, 1),
								    (0, -1),
								    (0, 1),
								    (1, -1),
								    (1, 0),
								    (1, 1),
								]
								UpperCAmelCase_		:      Optional[int]       	=					cell.position[0]
								UpperCAmelCase_		:      Any       	=					cell.position[1]
								UpperCAmelCase_		:      Optional[int]       	=					[]
								for n in neughbour_cord:
									UpperCAmelCase_		:      str       	=					current_x + n[0]
									UpperCAmelCase_		:      Any       	=					current_y + n[1]
									if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
										UpperCAmelCase_		:      List[str]       	=					Cell()
										UpperCAmelCase_		:      str       	=					(x, y)
										UpperCAmelCase_		:      Optional[Any]       	=					cell
										neighbours.append(_snake_case)
								return neighbours







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Dict:
	UpperCAmelCase_		:      List[str]       	=					[]
	UpperCAmelCase_		:      List[str]       	=					[]
	_open.append(UpperCamelCase  )

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

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


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









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__										= {
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__										= [
						    "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ClapModel",
						    "ClapPreTrainedModel",
						    "ClapTextModel",
						    "ClapTextModelWithProjection",
						    "ClapAudioModel",
						    "ClapAudioModelWithProjection",
						]
						lowerCAmelCase__										= ["ClapFeatureExtractor"]

if TYPE_CHECKING:
						from .configuration_clap import (
						    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ClapAudioConfig,
						    ClapConfig,
						    ClapTextConfig,
						)
						from .processing_clap import ClapProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_clap import ClapFeatureExtractor
												from .modeling_clap import (
												    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ClapAudioModel,
												    ClapAudioModelWithProjection,
												    ClapModel,
												    ClapPreTrainedModel,
												    ClapTextModel,
												    ClapTextModelWithProjection,
												)


else:
						import sys

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






