from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowercase									=				logging.get_logger(__name__)





class 	__lowercase    (			__a       ):




				'''simple docstring'''


				_A      :		Union[str, Any]   						=			['''pixel_values''']





				def __init__(		self    :		Optional[Any]      ,							_a    :		bool = True      ,							_a    :		Union[int, float] = 1 / 255      ,							_a    :		bool = True      ,							_a    :		int = 8      ,							**_a    :		Optional[Any]      ,							):
								super().__init__(**lowerCAmelCase__				)

								UpperCamelCase__					=				do_rescale
								UpperCamelCase__					=				rescale_factor
								UpperCamelCase__					=				do_pad
								UpperCamelCase__					=				pad_size





				def      A_							(		self    :		Optional[Any]      ,							_a    :		np.ndarray      ,							_a    :		float      ,							_a    :		Optional[Union[str, ChannelDimension]] = None      ,							**_a    :		str				):
								return rescale(lowerCAmelCase__      ,							scale=lowerCAmelCase__      ,							data_format=lowerCAmelCase__      ,							**lowerCAmelCase__				)





				def      A_							(		self    :		int      ,							_a    :		np.ndarray      ,							_a    :		int      ,							_a    :		Optional[Union[str, ChannelDimension]] = None				):
								UpperCamelCase__		, UpperCamelCase__					=				get_image_size(lowerCAmelCase__				)
								UpperCamelCase__					=				(old_height // size + 1) * size - old_height
								UpperCamelCase__					=				(old_width // size + 1) * size - old_width

								return pad(lowerCAmelCase__      ,							((0, pad_height), (0, pad_width))      ,							mode='''symmetric'''      ,							data_format=lowerCAmelCase__				)





				def      A_							(		self    :		Any      ,							_a    :		ImageInput      ,							_a    :		Optional[bool] = None      ,							_a    :		Optional[float] = None      ,							_a    :		Optional[bool] = None      ,							_a    :		Optional[int] = None      ,							_a    :		Optional[Union[str, TensorType]] = None      ,							_a    :		Union[str, ChannelDimension] = ChannelDimension.FIRST      ,							**_a    :		int      ,							):
								UpperCamelCase__					=				do_rescale if do_rescale is not None else self.do_rescale
								UpperCamelCase__					=				rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCamelCase__					=				do_pad if do_pad is not None else self.do_pad
								UpperCamelCase__					=				pad_size if pad_size is not None else self.pad_size

								UpperCamelCase__					=				make_list_of_images(lowerCAmelCase__				)

								if not valid_images(lowerCAmelCase__				):
												raise ValueError(
												    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
												    '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

								if do_rescale and rescale_factor is None:
												raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

								# All transformations expect numpy arrays.
								UpperCamelCase__					=				[to_numpy_array(lowerCAmelCase__				) for image in images]

								if do_rescale:
												UpperCamelCase__					=				[self.rescale(image=lowerCAmelCase__      ,							scale=lowerCAmelCase__				) for image in images]

								if do_pad:
												UpperCamelCase__					=				[self.pad(lowerCAmelCase__      ,							size=lowerCAmelCase__				) for image in images]

								UpperCamelCase__					=				[to_channel_dimension_format(lowerCAmelCase__      ,							lowerCAmelCase__				) for image in images]

								UpperCamelCase__					=				{'''pixel_values''': images}
								return BatchFeature(data=lowerCAmelCase__      ,							tensor_type=lowerCAmelCase__				)





import numpy as np





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:List[Any]     )  ->Any:
										lowercase         =      (0, 0)
										lowercase         =      None
										lowercase         =      0
										lowercase         =      0
										lowercase         =      0





						def __eq__(			self							:Optional[int]		,    lowerCAmelCase__							:Any     )  ->Optional[int]:
										return self.position == cell.position





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Optional[Any]:
										print(self.position     )





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:Optional[Any]		,    lowerCAmelCase__							:Tuple=(5, 5)     )  ->str:
										lowercase         =      np.zeros(lowerCAmelCase__     )
										lowercase         =      world_size[0]
										lowercase         =      world_size[1]





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Optional[int]:
										print(self.w     )





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




def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      []
				lowercase         =      []
				_open.append(__magic_name__						)

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

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


if __name__ == "__main__":
						_snake_case      :							Optional[int]     	=    Gridworld()
						# Start position and goal
						_snake_case      :							Dict     	=    Cell()
						_snake_case      :							List[str]     	=    (0, 0)
						_snake_case      :							Union[str, Any]     	=    Cell()
						_snake_case      :							List[Any]     	=    (4, 4)
						print(F"path from {start.position} to {goal.position}")
						_snake_case      :							str     	=    astar(world, start, goal)
						#   Just for visual reasons.
						for i in s:
												_snake_case      :							List[Any]     	=    1
						print(world.w)
