import numpy as np
class     a_			:






 '''simple docstring'''






 def __init__(      self :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__					     = (0, 0)
        lowerCAmelCase__					     = None
        lowerCAmelCase__					     = 0
        lowerCAmelCase__					     = 0
        lowerCAmelCase__					     = 0




 def __eq__(      self :  List[Any]      ,   lowercase__ :  str):







        '''simple docstring'''



        return self.position == cell.position




 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        print(self.position)






class     a_			:






 '''simple docstring'''






 def __init__(      self :  Optional[int]      ,   lowercase__ :  Union[str, Any]=(5, 5)):







        '''simple docstring'''



        lowerCAmelCase__					     = np.zeros(lowercase__)
        lowerCAmelCase__					     = world_size[0]
        lowerCAmelCase__					     = world_size[1]




 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        print(self.w)




 def 		__snake_case						(      self :  Dict      ,   lowercase__ :  int):







        '''simple docstring'''



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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = []
       lowerCAmelCase__					     = []
       _open.append(lowerCAmelCase__     )

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

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

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


lowerCAmelCase__     						=			logging.get_logger(__name__)
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= ['pixel_values']




 def __init__(      self :  Tuple      ,   lowercase__ :  bool = True      ,   lowercase__ :  Dict[str, int] = None      ,   lowercase__ :  PILImageResampling = PILImageResampling.BICUBIC      ,   lowercase__ :  bool = True      ,   lowercase__ :  Union[int, float] = 1 / 255      ,   lowercase__ :  bool = True      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  bool = True      ,   **lowercase__ :  List[Any]      ,   ):







        '''simple docstring'''



        super().__init__(**lowercase__)
        lowerCAmelCase__					     = size if size is not None else {'height': 384, 'width': 384}
        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)

        lowerCAmelCase__					     = do_resize
        lowerCAmelCase__					     = size
        lowerCAmelCase__					     = resample
        lowerCAmelCase__					     = do_rescale
        lowerCAmelCase__					     = rescale_factor
        lowerCAmelCase__					     = do_normalize
        lowerCAmelCase__					     = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        lowerCAmelCase__					     = image_std if image_std is not None else OPENAI_CLIP_STD
        lowerCAmelCase__					     = do_convert_rgb




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Dict[str, int]      ,   lowercase__ :  PILImageResampling = PILImageResampling.BICUBIC      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)
        if "height" not in size or "width" not in size:
               raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""")
        lowerCAmelCase__					     = (size['height'], size['width'])
        return resize(lowercase__      ,   size=lowercase__      ,   resample=lowercase__      ,   data_format=lowercase__      ,   **lowercase__)




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Union[int, float]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Union[str, Any]      ,   ):







        '''simple docstring'''



        return rescale(lowercase__      ,   scale=lowercase__      ,   data_format=lowercase__      ,   **lowercase__)




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Union[float, List[float]]      ,   lowercase__ :  Union[float, List[float]]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Any      ,   ):







        '''simple docstring'''



        return normalize(lowercase__      ,   mean=lowercase__      ,   std=lowercase__      ,   data_format=lowercase__      ,   **lowercase__)




 def 		__snake_case						(      self :  Any      ,   lowercase__ :  ImageInput      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[Dict[str, int]] = None      ,   lowercase__ :  PILImageResampling = None      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[float] = None      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  ChannelDimension = ChannelDimension.FIRST      ,   **lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase__					     = resample if resample is not None else self.resample
        lowerCAmelCase__					     = do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase__					     = rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase__					     = do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase__					     = image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase__					     = image_std if image_std is not None else self.image_std
        lowerCAmelCase__					     = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowerCAmelCase__					     = size if size is not None else self.size
        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)

        lowerCAmelCase__					     = make_list_of_images(lowercase__)

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

        if do_resize and size is None or resample is None:
               raise ValueError('Size and resample must be specified if do_resize is True.')

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

        if do_normalize and (image_mean is None or image_std is None):
               raise ValueError('Image mean and std must be specified if do_normalize is True.')

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
               lowerCAmelCase__					     = [convert_to_rgb(lowercase__) for image in images]

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

        if do_resize:
               lowerCAmelCase__					     = [self.resize(image=lowercase__      ,   size=lowercase__      ,   resample=lowercase__) for image in images]

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

        if do_normalize:
               lowerCAmelCase__					     = [self.normalize(image=lowercase__      ,   mean=lowercase__      ,   std=lowercase__) for image in images]

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

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

        return encoded_outputs
