

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


a_						     =    logging.get_logger(__name__)

a_						     =    {
    'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
    'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}



class _UpperCamelCase							(							lowercase__   ):
			'''simple docstring'''







			lowerCamelCase__           ="""mobilenet_v1"""
			def __init__(			self							: Optional[int]      ,      a							: Tuple=3      ,      a							: Optional[int]=224      ,      a							: str=1.0      ,      a							: List[Any]=8      ,      a							: List[str]="relu6"      ,      a							: Optional[int]=True      ,      a							: Optional[int]=0.999      ,      a							: Any=0.02      ,      a							: int=0.001      ,      **a							: Optional[Any]      ,      )							->       List[str]:

				"""simple docstring"""

				super().__init__(**__lowercase	)

				if depth_multiplier <= 0:
					raise ValueError("depth_multiplier must be greater than zero."	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       num_channels
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       image_size
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       depth_multiplier
				SCREAMING_SNAKE_CASE   :      Tuple				     =       min_depth
				SCREAMING_SNAKE_CASE   :      str				     =       hidden_act
				SCREAMING_SNAKE_CASE   :      Any				     =       tf_padding
				SCREAMING_SNAKE_CASE   :      Tuple				     =       classifier_dropout_prob
				SCREAMING_SNAKE_CASE   :      int				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       layer_norm_eps



class _UpperCamelCase							(							lowercase__   ):
			'''simple docstring'''







			lowerCamelCase__           =version.parse('1.11'   )
			@property
			def 							__UpperCamelCase (			self							: List[Any]	)							->       List[str]:

				"""simple docstring"""

				return OrderedDict([("pixel_values", {0: "batch"})]	)
			@property
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Union[str, Any]:

				"""simple docstring"""

				if self.task == "image-classification":
					return OrderedDict([("logits", {0: "batch"})]	)
				else:
					return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]	)






			@property
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       Union[str, Any]:

				"""simple docstring"""

				return 1e-4


import numpy as np






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Dict	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       (0, 0)
				SCREAMING_SNAKE_CASE   :      Any				     =       None
				SCREAMING_SNAKE_CASE   :      Any				     =       0
				SCREAMING_SNAKE_CASE   :      List[str]				     =       0
				SCREAMING_SNAKE_CASE   :      Any				     =       0
			def __eq__(			self							: List[str]      ,      a							: Any	)							->       Tuple:

				"""simple docstring"""

				return self.position == cell.position






			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       str:

				"""simple docstring"""

				print(self.position	)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: List[Any]      ,      a							: List[Any]=(5, 5)	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.zeros(a	)
				SCREAMING_SNAKE_CASE   :      str				     =       world_size[0]
				SCREAMING_SNAKE_CASE   :      int				     =       world_size[1]
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[int]:

				"""simple docstring"""

				print(self.w	)






			def 							__UpperCamelCase (			self							: Dict      ,      a							: Dict	)							->       Tuple:

				"""simple docstring"""

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

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	_open.append(_a)

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

			for c in _open:
				if c == n and c.f < n.f:
					continue
			_open.append(_a)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	while current.parent is not None:
		path.append(current.position)
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       current.parent
	path.append(current.position)
	return path[::-1]


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