




"""simple docstring"""







from __future__ import annotations

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





def      __snake_case (				_lowercase			,_lowercase			,_lowercase			,_lowercase			,_lowercase			,):


    """simple docstring"""







    UpperCamelCase              =	[
        [0 for col in range(len(grid[0]    )    )] for row in range(len(_lowercase    )    )
    ]  # the reference grid
    UpperCamelCase              =	1
    UpperCamelCase              =	[
        [0 for col in range(len(grid[0]    )    )] for row in range(len(_lowercase    )    )
    ]  # the action grid

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

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

    while not found and not resign:
        if len(_lowercase    ) == 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()
            UpperCamelCase              =	cell.pop()
            UpperCamelCase              =	next_cell[2]
            UpperCamelCase              =	next_cell[3]
            UpperCamelCase              =	next_cell[1]

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

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_	     =       [
      [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],
  ]

  SCREAMING_SNAKE_CASE_	     =       [0, 0]
  # all coordinates are given in format [y,x]
  SCREAMING_SNAKE_CASE_	     =       [len(grid) - 1, len(grid[0]) - 1]
  SCREAMING_SNAKE_CASE_	     =       1

  # the cost map which pushes the path closer to the goal
  SCREAMING_SNAKE_CASE_	     =       [[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])):
      SCREAMING_SNAKE_CASE_	     =       abs(i - goal[0]) + abs(j - goal[1])
      if grid[i][j] == 1:
        # added extra penalty in the heuristic map
        SCREAMING_SNAKE_CASE_	     =       99

  SCREAMING_SNAKE_CASE_     ,    SCREAMING_SNAKE_CASE_	     =       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])


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowercase_     )
class 					lowercase							(			lowercase_     ):
				# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
				__SCREAMING_SNAKE_CASE   :					str			     =     field(default='''text-classification'''      ,    metadata={'''include_in_asdict_even_if_is_default''': True}     )
				__SCREAMING_SNAKE_CASE   :					ClassVar[Features]			     =     Features({'''text''': Value('''string'''     )}     )
				__SCREAMING_SNAKE_CASE   :					ClassVar[Features]			     =     Features({'''labels''': ClassLabel}     )
				__SCREAMING_SNAKE_CASE   :					str			     =     "text"
				__SCREAMING_SNAKE_CASE   :					str			     =     "labels"




				def        a				(   self ,  snake_case			):
											if self.label_column not in features:
																		raise ValueError(F'''Column {self.label_column} is not present in features.'''			)
											if not isinstance(features[self.label_column] ,  snake_case			):
																		raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''			)
											snake_case_     							=       copy.deepcopy(self			)
											snake_case_     							=       self.label_schema.copy()
											snake_case_     							=       features[self.label_column]
											snake_case_     							=       label_schema
											return task_template




				@property
				def        a				(   self			):
											return {
											    self.text_column: "text",
											    self.label_column: "labels",
											}




