

'''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_		:      Tuple       	=					n.position
			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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}



class 					lowercase						(  a_,					a_ ):
							_lowerCamelCase		:      Optional[int]=   "swin"

							_lowerCamelCase		:      Dict=   {
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(			self    ,			_snake_case=224    ,			_snake_case=4    ,			_snake_case=3    ,			_snake_case=96    ,			_snake_case=[2, 2, 6, 2]    ,			_snake_case=[3, 6, 12, 24]    ,			_snake_case=7    ,			_snake_case=4.0    ,			_snake_case=True    ,			_snake_case=0.0    ,			_snake_case=0.0    ,			_snake_case=0.1    ,			_snake_case="gelu"    ,			_snake_case=False    ,			_snake_case=0.02    ,			_snake_case=1e-5    ,			_snake_case=32    ,			_snake_case=None    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Tuple:
								super().__init__(**_snake_case)

								UpperCAmelCase_		:      Any       	=					image_size
								UpperCAmelCase_		:      Dict       	=					patch_size
								UpperCAmelCase_		:      int       	=					num_channels
								UpperCAmelCase_		:      Tuple       	=					embed_dim
								UpperCAmelCase_		:      Dict       	=					depths
								UpperCAmelCase_		:      Dict       	=					len(_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					num_heads
								UpperCAmelCase_		:      Optional[int]       	=					window_size
								UpperCAmelCase_		:      Any       	=					mlp_ratio
								UpperCAmelCase_		:      Any       	=					qkv_bias
								UpperCAmelCase_		:      str       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Optional[int]       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      Dict       	=					drop_path_rate
								UpperCAmelCase_		:      int       	=					hidden_act
								UpperCAmelCase_		:      Tuple       	=					use_absolute_embeddings
								UpperCAmelCase_		:      Optional[int]       	=					layer_norm_eps
								UpperCAmelCase_		:      Optional[int]       	=					initializer_range
								UpperCAmelCase_		:      Optional[Any]       	=					encoder_stride
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								UpperCAmelCase_		:      Union[str, Any]       	=					int(embed_dim * 2 ** (len(_snake_case) - 1))
								UpperCAmelCase_		:      Tuple       	=					['stem'] + [F"""stage{idx}""" for idx in range(1    ,			len(_snake_case) + 1)]
								UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					get_aligned_output_features_output_indices(
								    out_features=_snake_case    ,			out_indices=_snake_case    ,			stage_names=self.stage_names)



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Tuple=   version.parse("1.11" )


							@property
							def 						_snake_case							(			self)							->				Mapping[str, Mapping[int, str]]:
								return OrderedDict(
								    [
								        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
								    ])





							@property
							def 						_snake_case							(			self)							->				float:
								return 1e-4






