


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


UpperCAmelCase      =   logging.get_logger(__name__)







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  ["pixel_values"]



 def __init__(     self		,				_UpperCAmelCase = True		,				_UpperCAmelCase = None		,				_UpperCAmelCase = PILImageResampling.BICUBIC		,				_UpperCAmelCase = True		,				_UpperCAmelCase = 1 / 2_55		,				_UpperCAmelCase = True		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = True		,				**_UpperCAmelCase		,				):
       super().__init__(**_UpperCAmelCase )
       snake_case_	=				size if size is not None else {'''height''': 3_84, '''width''': 3_84}
       snake_case_	=				get_size_dict(_UpperCAmelCase		,				default_to_square=_UpperCAmelCase )

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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase = PILImageResampling.BICUBIC		,				_UpperCAmelCase = None		,				**_UpperCAmelCase		,				):
       snake_case_	=				get_size_dict(_UpperCAmelCase		,				default_to_square=_UpperCAmelCase )
       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()}''' )
       snake_case_	=				(size['''height'''], size['''width'''])
       return resize(_UpperCAmelCase		,				size=_UpperCAmelCase		,				resample=_UpperCAmelCase		,				data_format=_UpperCAmelCase		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase = None		,				**_UpperCAmelCase		,				):
       return rescale(_UpperCAmelCase		,				scale=_UpperCAmelCase		,				data_format=_UpperCAmelCase		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase = None		,				**_UpperCAmelCase		,				):
       return normalize(_UpperCAmelCase		,				mean=_UpperCAmelCase		,				std=_UpperCAmelCase		,				data_format=_UpperCAmelCase		,				**_UpperCAmelCase )





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = ChannelDimension.FIRST		,				**_UpperCAmelCase		,				):
       snake_case_	=				do_resize if do_resize is not None else self.do_resize
       snake_case_	=				resample if resample is not None else self.resample
       snake_case_	=				do_rescale if do_rescale is not None else self.do_rescale
       snake_case_	=				rescale_factor if rescale_factor is not None else self.rescale_factor
       snake_case_	=				do_normalize if do_normalize is not None else self.do_normalize
       snake_case_	=				image_mean if image_mean is not None else self.image_mean
       snake_case_	=				image_std if image_std is not None else self.image_std
       snake_case_	=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       snake_case_	=				size if size is not None else self.size
       snake_case_	=				get_size_dict(_UpperCAmelCase		,				default_to_square=_UpperCAmelCase )

       snake_case_	=				make_list_of_images(_UpperCAmelCase )

       if not valid_images(_UpperCAmelCase ):
             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:
             snake_case_	=				[convert_to_rgb(_UpperCAmelCase ) for image in images]

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

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

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

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

       snake_case_	=				[to_channel_dimension_format(_UpperCAmelCase		,				_UpperCAmelCase ) for image in images]

       snake_case_	=				BatchFeature(data={'''pixel_values''': images}		,				tensor_type=_UpperCAmelCase )

       return encoded_outputs



from __future__ import annotations

import time

UpperCAmelCase      =   list[tuple[int, int]]

UpperCAmelCase      =   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

UpperCAmelCase      =   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right







class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				pos_x
       snake_case_	=				pos_y
       snake_case_	=				(pos_y, pos_x)
       snake_case_	=				goal_x
       snake_case_	=				goal_y
       snake_case_	=				parent







class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				Node(start[1]		,				start[0]		,				goal[1]		,				goal[0]		,				_UpperCAmelCase )
       snake_case_	=				Node(goal[1]		,				goal[0]		,				goal[1]		,				goal[0]		,				_UpperCAmelCase )

       snake_case_	=				[self.start]
       snake_case_	=				False



 def 						UpperCamelCase__					(     self ):
       while self.node_queue:
             snake_case_	=				self.node_queue.pop(0 )

             if current_node.pos == self.target.pos:
                   snake_case_	=				True
                   return self.retrace_path(_UpperCAmelCase )

             snake_case_	=				self.get_successors(_UpperCAmelCase )

             for node in successors:
                   self.node_queue.append(_UpperCAmelCase )

       if not self.reached:
             return [self.start.pos]
       return None



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				[]
       for action in delta:
             snake_case_	=				parent.pos_x + action[1]
             snake_case_	=				parent.pos_y + action[0]
             if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(_UpperCAmelCase ) - 1):
                   continue

             if grid[pos_y][pos_x] != 0:
                   continue

             successors.append(
                 Node(_UpperCAmelCase		,				_UpperCAmelCase		,				self.target.pos_y		,				self.target.pos_x		,				_UpperCAmelCase ) )
       return successors





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				node
       snake_case_	=				[]
       while current_node is not None:
             path.append((current_node.pos_y, current_node.pos_x) )
             snake_case_	=				current_node.parent
       path.reverse()
       return path







class lowerCAmelCase_						:

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				BreadthFirstSearch(_UpperCAmelCase		,				_UpperCAmelCase )
       snake_case_	=				BreadthFirstSearch(_UpperCAmelCase		,				_UpperCAmelCase )
       snake_case_	=				False



 def 						UpperCamelCase__					(     self ):
       while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
             snake_case_	=				self.fwd_bfs.node_queue.pop(0 )
             snake_case_	=				self.bwd_bfs.node_queue.pop(0 )

             if current_bwd_node.pos == current_fwd_node.pos:
                   snake_case_	=				True
                   return self.retrace_bidirectional_path(
                       _UpperCAmelCase		,				_UpperCAmelCase )

             snake_case_	=				current_bwd_node
             snake_case_	=				current_fwd_node

             snake_case_	=				{
                 self.fwd_bfs: self.fwd_bfs.get_successors(_UpperCAmelCase ),
                 self.bwd_bfs: self.bwd_bfs.get_successors(_UpperCAmelCase ),
             }

             for bfs in [self.fwd_bfs, self.bwd_bfs]:
                   for node in successors[bfs]:
                         bfs.node_queue.append(_UpperCAmelCase )

       if not self.reached:
             return [self.fwd_bfs.start.pos]
       return None





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				self.fwd_bfs.retrace_path(_UpperCAmelCase )
       snake_case_	=				self.bwd_bfs.retrace_path(_UpperCAmelCase )
       bwd_path.pop()
       bwd_path.reverse()
       snake_case_	=				fwd_path + bwd_path
       return path


if __name__ == "__main__":
   # all coordinates are given in format [y,x]
   import doctest

   doctest.testmod()
   UpperCAmelCase      =   (0, 0)
   UpperCAmelCase      =   (len(grid) - 1, len(grid[0]) - 1)
   for elem in grid:
      print(elem)

   UpperCAmelCase      =   time.time()
   UpperCAmelCase      =   BreadthFirstSearch(init, goal)
   UpperCAmelCase      =   bfs.search()
   UpperCAmelCase      =   time.time() - start_bfs_time

   print("""Unidirectional BFS computation time : """, bfs_time)

   UpperCAmelCase      =   time.time()
   UpperCAmelCase      =   BidirectionalBreadthFirstSearch(init, goal)
   UpperCAmelCase      =   bd_bfs.search()
   UpperCAmelCase      =   time.time() - start_bd_bfs_time

   print("""Bidirectional BFS computation time : """, bd_bfs_time)