





from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
 import torch

if is_tf_available():
 import tensorflow as tf


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  ['''image_processor''']
     __UpperCamelCase            =  '''SamImageProcessor'''



     def __init__(				self							:Union[str, Any]      ,   snake_case							:Dict	):





        '''simple docstring'''




        super().__init__(snake_case	)
        A_       :					Optional[Any]        =      self.image_processor
        A_       :					Optional[Any]        =      -10
        A_       :					List[Any]        =      self.image_processor.size["longest_edge"]



     def __call__(				self							:Tuple      ,   snake_case							:Union[str, Any]=None      ,   snake_case							:int=None      ,   snake_case							:Union[str, Any]=None      ,   snake_case							:List[Any]=None      ,   snake_case							:Optional[Union[str, TensorType]] = None      ,   **snake_case							:Any      ,   ):





        '''simple docstring'''




        A_       :					Optional[int]        =      self.image_processor(
            snake_case      ,   return_tensors=snake_case      ,   **snake_case      ,   )

        # pop arguments that are not used in the foward but used nevertheless
        A_       :					Dict        =      encoding_image_processor["original_sizes"]

        if hasattr(snake_case      ,   "numpy"	):  # Checks if Torch or TF tensor
           A_       :					int        =      original_sizes.numpy()

        A_   ,    A_   ,    A_       :					Tuple        =      self._check_and_preprocess_points(
            input_points=snake_case      ,   input_labels=snake_case      ,   input_boxes=snake_case      ,   )

        A_       :					Tuple        =      self._normalize_and_convert(
            snake_case      ,   snake_case      ,   input_points=snake_case      ,   input_labels=snake_case      ,   input_boxes=snake_case      ,   return_tensors=snake_case      ,   )

        return encoding_image_processor



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]      ,   snake_case							:List[str]      ,   snake_case							:int      ,   snake_case							:int=None      ,   snake_case							:Dict=None      ,   snake_case							:Union[str, Any]=None      ,   snake_case							:List[Any]="pt"      ,   ):





        '''simple docstring'''




        if input_points is not None:
           if len(snake_case	) != len(snake_case	):
              A_       :					Union[str, Any]        =      [
                  self._normalize_coordinates(self.target_size      ,   snake_case      ,   original_sizes[0]	) for point in input_points
              ]
           else:
              A_       :					Tuple        =      [
                  self._normalize_coordinates(self.target_size      ,   snake_case      ,   snake_case	)
                  for point, original_size in zip(snake_case      ,   snake_case	)
              ]
           # check that all arrays have the same shape
           if not all(point.shape == input_points[0].shape for point in input_points	):
              if input_labels is not None:
                 A_   ,    A_       :					List[str]        =      self._pad_points_and_labels(snake_case      ,   snake_case	)

           A_       :					Tuple        =      np.array(snake_case	)

        if input_labels is not None:
           A_       :					Optional[Any]        =      np.array(snake_case	)

        if input_boxes is not None:
           if len(snake_case	) != len(snake_case	):
              A_       :					int        =      [
                  self._normalize_coordinates(self.target_size      ,   snake_case      ,   original_sizes[0]      ,   is_bounding_box=snake_case	)
                  for box in input_boxes
              ]
           else:
              A_       :					Optional[Any]        =      [
                  self._normalize_coordinates(self.target_size      ,   snake_case      ,   snake_case      ,   is_bounding_box=snake_case	)
                  for box, original_size in zip(snake_case      ,   snake_case	)
              ]
           A_       :					Union[str, Any]        =      np.array(snake_case	)

        if input_boxes is not None:
           if return_tensors == "pt":
              A_       :					int        =      torch.from_numpy(snake_case	)
              # boxes batch size of 1 by default
              A_       :					Any        =      input_boxes.unsqueeze(1	) if len(input_boxes.shape	) != 3 else input_boxes
           elif return_tensors == "tf":
              A_       :					List[Any]        =      tf.convert_to_tensor(snake_case	)
              # boxes batch size of 1 by default
              A_       :					Union[str, Any]        =      tf.expand_dims(snake_case      ,   1	) if len(input_boxes.shape	) != 3 else input_boxes
           encoding_image_processor.update({"input_boxes": input_boxes}	)
        if input_points is not None:
           if return_tensors == "pt":
              A_       :					Tuple        =      torch.from_numpy(snake_case	)
              # point batch size of 1 by default
              A_       :					Optional[int]        =      input_points.unsqueeze(1	) if len(input_points.shape	) != 4 else input_points
           elif return_tensors == "tf":
              A_       :					Optional[Any]        =      tf.convert_to_tensor(snake_case	)
              # point batch size of 1 by default
              A_       :					List[str]        =      tf.expand_dims(snake_case      ,   1	) if len(input_points.shape	) != 4 else input_points
           encoding_image_processor.update({"input_points": input_points}	)
        if input_labels is not None:
           if return_tensors == "pt":
              A_       :					Any        =      torch.from_numpy(snake_case	)
              # point batch size of 1 by default
              A_       :					List[str]        =      input_labels.unsqueeze(1	) if len(input_labels.shape	) != 3 else input_labels
           elif return_tensors == "tf":
              A_       :					List[Any]        =      tf.convert_to_tensor(snake_case	)
              # point batch size of 1 by default
              A_       :					List[str]        =      tf.expand_dims(snake_case      ,   1	) if len(input_labels.shape	) != 3 else input_labels
           encoding_image_processor.update({"input_labels": input_labels}	)

        return encoding_image_processor



     def     SCREAMING_SNAKE_CASE		(				self							:Any      ,   snake_case							:Tuple      ,   snake_case							:Optional[int]	):





        '''simple docstring'''




        A_       :					Tuple        =      max([point.shape[0] for point in input_points]	)
        A_       :					List[Any]        =      []
        for i, point in enumerate(snake_case	):
           if point.shape[0] != expected_nb_points:
              A_       :					List[str]        =      np.concatenate(
                  [point, np.zeros((expected_nb_points - point.shape[0], 2)	) + self.point_pad_value]      ,   axis=0	)
              A_       :					List[Any]        =      np.append(input_labels[i]      ,   [self.point_pad_value]	)
           processed_input_points.append(snake_case	)
        A_       :					List[Any]        =      processed_input_points
        return input_points, input_labels



     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:int      ,   snake_case							:np.ndarray      ,   snake_case							:Tuple      ,   snake_case							:int=False	):





        '''simple docstring'''




        A_   ,    A_       :					Tuple        =      original_size
        A_   ,    A_       :					int        =      self.image_processor._get_preprocess_shape(snake_case      ,   longest_edge=snake_case	)
        A_       :					List[Any]        =      deepcopy(snake_case	).astype(snake_case	)

        if is_bounding_box:
           A_       :					Optional[int]        =      coords.reshape(-1      ,   2      ,   2	)

        A_       :					Dict        =      coords[..., 0] * (new_w / old_w)
        A_       :					Tuple        =      coords[..., 1] * (new_h / old_h)

        if is_bounding_box:
           A_       :					str        =      coords.reshape(-1      ,   4	)

        return coords



     def     SCREAMING_SNAKE_CASE		(				self							:Any      ,   snake_case							:Optional[Any]=None      ,   snake_case							:int=None      ,   snake_case							:Union[str, Any]=None      ,   ):





        '''simple docstring'''




        if input_points is not None:
           if hasattr(snake_case      ,   "numpy"	):  # Checks for TF or Torch tensor
              A_       :					str        =      input_points.numpy().tolist()

           if not isinstance(snake_case      ,   snake_case	) or not isinstance(input_points[0]      ,   snake_case	):
              raise ValueError("Input points must be a list of list of floating points."	)
           A_       :					Tuple        =      [np.array(snake_case	) for input_point in input_points]
        else:
           A_       :					str        =      None

        if input_labels is not None:
           if hasattr(snake_case      ,   "numpy"	):
              A_       :					List[Any]        =      input_labels.numpy().tolist()

           if not isinstance(snake_case      ,   snake_case	) or not isinstance(input_labels[0]      ,   snake_case	):
              raise ValueError("Input labels must be a list of list integers."	)
           A_       :					Dict        =      [np.array(snake_case	) for label in input_labels]
        else:
           A_       :					Any        =      None

        if input_boxes is not None:
           if hasattr(snake_case      ,   "numpy"	):
              A_       :					List[Any]        =      input_boxes.numpy().tolist()

           if (
               not isinstance(snake_case      ,   snake_case	)
               or not isinstance(input_boxes[0]      ,   snake_case	)
               or not isinstance(input_boxes[0][0]      ,   snake_case	)
           ):
              raise ValueError("Input boxes must be a list of list of list of floating points."	)
           A_       :					List[Any]        =      [np.array(snake_case	).astype(np.floataa	) for box in input_boxes]
        else:
           A_       :					str        =      None

        return input_points, input_labels, input_boxes



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        A_       :					Dict        =      self.image_processor.model_input_names
        return list(dict.fromkeys(snake_case	)	)





     def     SCREAMING_SNAKE_CASE		(				self							:str      ,   *snake_case							:Optional[int]      ,   **snake_case							:Any	):





        '''simple docstring'''




        return self.image_processor.post_process_masks(*snake_case      ,   **snake_case	)







import numpy as np


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:List[str]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      (0, 0)
        A_       :					Dict        =      None
        A_       :					Optional[Any]        =      0
        A_       :					Optional[Any]        =      0
        A_       :					List[Any]        =      0



     def __eq__(				self							:int      ,   snake_case							:str	):





        '''simple docstring'''




        return self.position == cell.position





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        print(self.position	)




class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:Dict      ,   snake_case							:Optional[Any]=(5, 5)	):





        '''simple docstring'''




        A_       :					Optional[int]        =      np.zeros(snake_case	)
        A_       :					Tuple        =      world_size[0]
        A_       :					Optional[Any]        =      world_size[1]



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        print(self.w	)





     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:int	):





        '''simple docstring'''




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




def   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			str						)			->    str:
   A_       :					Optional[Any]        =      []
   A_       :					Tuple        =      []
   _open.append(_lowerCAmelCase						)

   while _open:
      A_       :					List[Any]        =      np.argmin([n.f for n in _open]						)
      A_       :					Optional[Any]        =      _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
         A_       :					int        =      current.g + 1
         A_   ,    A_       :					List[str]        =      n.position
         A_   ,    A_       :					Any        =      goal.position
         A_       :					Optional[Any]        =      (ya - ya) ** 2 + (xa - xa) ** 2
         A_       :					Optional[Any]        =      n.h + n.g

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


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