






'''simple docstring'''







from __future__ import annotations

UpperCamelCase     :   Dict	      =		[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]
def     A__      (					__lowerCAmelCase		:      list[list[int]] ,     __lowerCAmelCase		:      list[int] ,     __lowerCAmelCase		:      list[int] ,     __lowerCAmelCase		:      int ,     __lowerCAmelCase		:      list[list[int]] ,     ):
						lowerCamelCase__								=	[
						    [0 for col in range(len(grid[0]    )    )] for row in range(len(__lowerCAmelCase    )    )
						]  # the reference grid
						lowerCamelCase__								=	1
						lowerCamelCase__								=	[
						    [0 for col in range(len(grid[0]    )    )] for row in range(len(__lowerCAmelCase    )    )
						]  # the action grid

						lowerCamelCase__								=	init[0]
						lowerCamelCase__								=	init[1]
						lowerCamelCase__								=	0
						lowerCamelCase__								=	g + heuristic[x][y]  # cost from starting cell to destination cell
						lowerCamelCase__								=	[[f, g, x, y]]

						lowerCamelCase__								=	False  # flag that is set when search is complete
						lowerCamelCase__								=	False  # flag set if we can't find expand

						while not found and not resign:
												if len(__lowerCAmelCase    ) == 0:
																		raise ValueError("""Algorithm is unable to find solution"""    )
												else:  # to choose the least costliest action so as to move closer to the goal
																		cell.sort()
																		cell.reverse()
																		lowerCamelCase__								=	cell.pop()
																		lowerCamelCase__								=	next_cell[2]
																		lowerCamelCase__								=	next_cell[3]
																		lowerCamelCase__								=	next_cell[1]

																		if x == goal[0] and y == goal[1]:
																								lowerCamelCase__								=	True
																		else:
																								for i in range(len(__lowerCAmelCase    )    ):  # to try out different valid actions
																														lowerCamelCase__								=	x + DIRECTIONS[i][0]
																														lowerCamelCase__								=	y + DIRECTIONS[i][1]
																														if xa >= 0 and xa < len(__lowerCAmelCase    ) and ya >= 0 and ya < len(grid[0]    ):
																																				if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																																										lowerCamelCase__								=	g + cost
																																										lowerCamelCase__								=	ga + heuristic[xa][ya]
																																										cell.append([fa, ga, xa, ya]    )
																																										lowerCamelCase__								=	1
																																										lowerCamelCase__								=	i
						lowerCamelCase__								=	[]
						lowerCamelCase__								=	goal[0]
						lowerCamelCase__								=	goal[1]
						invpath.append([x, y]    )  # we get the reverse path from here
						while x != init[0] or y != init[1]:
												lowerCamelCase__								=	x - DIRECTIONS[action[x][y]][0]
												lowerCamelCase__								=	y - DIRECTIONS[action[x][y]][1]
												lowerCamelCase__								=	xa
												lowerCamelCase__								=	ya
												invpath.append([x, y]    )

						lowerCamelCase__								=	[]
						for i in range(len(__lowerCAmelCase    )    ):
												path.append(invpath[len(__lowerCAmelCase    ) - 1 - i]    )
						return path, action


if __name__ == "__main__":
							UpperCamelCase     :   List[Any]	      =		[
							    [0, 1, 0, 0, 0, 0],
							    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
							    [0, 1, 0, 0, 0, 0],
							    [0, 1, 0, 0, 1, 0],
							    [0, 0, 0, 0, 1, 0],
							]

							UpperCamelCase     :   List[Any]	      =		[0, 0]
							# all coordinates are given in format [y,x]
							UpperCamelCase     :   int	      =		[len(grid) - 1, len(grid[0]) - 1]
							UpperCamelCase     :   Dict	      =		1

							# the cost map which pushes the path closer to the goal
							UpperCamelCase     :   Tuple	      =		[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
							for i in range(len(grid)):
														for j in range(len(grid[0])):
																					UpperCamelCase     :   str	      =		abs(i - goal[0]) + abs(j - goal[1])
																					if grid[i][j] == 1:
																												# added extra penalty in the heuristic map
																												UpperCamelCase     :   Any	      =		99

							UpperCamelCase      ,    UpperCamelCase     :   int	      =		search(grid, init, goal, cost, heuristic)

							print('ACTION MAP')
							for i in range(len(action)):
														print(action[i])

							for i in range(len(path)):
														print(path[i])





'''simple docstring'''






from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 import tensorflow as tf

 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowercase				=logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase							)
class    __magic_name__				(						lowerCAmelCase							):






       def __init__(     self    ,				*snake_case    ,				**snake_case)		->    Dict:




          '''simple docstring'''
          super().__init__(*snake_case    ,				**snake_case)
          requires_backends(self    ,				'vision')
          self.check_model_type(
              TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
              if self.framework == 'tf'
              else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)






       def      lowerCAmelCase						(     self    ,				snake_case=None)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       ={}
          if top_k is not None:
             _UpperCAmelCase  :  Optional[int]				       =top_k
          return {}, {}, postprocess_params






       def __call__(     self    ,				snake_case    ,				**snake_case)		->    List[str]:




          '''simple docstring'''
          return super().__call__(snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case)		->    List[str]:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =load_image(snake_case)
          _UpperCAmelCase  :  Tuple				       =self.image_processor(images=snake_case    ,				return_tensors=self.framework)
          return model_inputs






       def      lowerCAmelCase						(     self    ,				snake_case)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =self.model(**snake_case)
          return model_outputs






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case=5)		->    Any:




          '''simple docstring'''
          if top_k > self.model.config.num_labels:
             _UpperCAmelCase  :  Optional[Any]				       =self.model.config.num_labels

          if self.framework == "pt":
             _UpperCAmelCase  :  List[Any]				       =model_outputs.logits.softmax(-1)[0]
             _UpperCAmelCase       ,			_UpperCAmelCase  :  Optional[Any]				       =probs.topk(snake_case)
          elif self.framework == "tf":
             _UpperCAmelCase  :  int				       =stable_softmax(model_outputs.logits    ,				axis=-1)[0]
             _UpperCAmelCase  :  List[Any]				       =tf.math.top_k(snake_case    ,				k=snake_case)
             _UpperCAmelCase       ,			_UpperCAmelCase  :  List[str]				       =topk.values.numpy(), topk.indices.numpy()
          else:
             raise ValueError(f"Unsupported framework: {self.framework}")

          _UpperCAmelCase  :  int				       =scores.tolist()
          _UpperCAmelCase  :  Tuple				       =ids.tolist()
          return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case    ,				snake_case)]

