



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
				import jax
				import jaxlib

UpperCamelCase__   : Optional[int]					  = get_logger()

UpperCamelCase__   : int					  = None







class   _UpperCamelCase  (       TensorFormatter[Mapping, 'jax.Array', Mapping]  ):







					'''simple docstring'''

					def __init__(						self    :  Dict ,					__lowercase    :  List[str]=None ,					__lowercase    :  int=None ,					**__lowercase    :  Optional[int]		):






						'''simple docstring'''







						super().__init__(features=SCREAMING_SNAKE_CASE_		)
						import jax
						from jaxlib.xla_client import Device

						if isinstance(SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_		):
							raise ValueError(
							    F"""Expected {device} to be a `str` not {type(SCREAMING_SNAKE_CASE_		)}, as `jaxlib.xla_extension.Device` """
							    """is not serializable neither with `pickle` nor with `dill`. Instead you can surround """
							    """the device with `str()` to get its string identifier that will be internally mapped """
							    """to the actual `jaxlib.xla_extension.Device`."""		)
						UpperCAmelCase_								=     device if isinstance(SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_		) else str(jax.devices()[0]		)
						# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
						# with `pickle` nor with `dill`, so we need to use a global variable instead
						global DEVICE_MAPPING
						if DEVICE_MAPPING is None:
							UpperCAmelCase_								=     self._map_devices_to_str()
						if self.device not in list(DEVICE_MAPPING.keys()		):
							logger.warning(
							    F"""Device with string identifier {self.device} not listed among the available """
							    F"""devices: {list(DEVICE_MAPPING.keys()		)}, so falling back to the default """
							    F"""device: {str(jax.devices()[0]		)}."""		)
							UpperCAmelCase_								=     str(jax.devices()[0]		)
						UpperCAmelCase_								=     jnp_array_kwargs





					@staticmethod
					def        SCREAMING_SNAKE_CASE							(						):






						'''simple docstring'''







						import jax

						return {str(SCREAMING_SNAKE_CASE_		): device for device in jax.devices()}





					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  Union[str, Any]		):






						'''simple docstring'''







						import jax
						import jax.numpy as jnp

						if isinstance(SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_		) and column:
							if all(
							    isinstance(SCREAMING_SNAKE_CASE_ ,					jax.Array		) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column		):
								return jnp.stack(SCREAMING_SNAKE_CASE_ ,					axis=0		)
						return column





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  List[Any]		):






						'''simple docstring'''







						import jax
						import jax.numpy as jnp

						if isinstance(SCREAMING_SNAKE_CASE_ ,					(str, bytes, type(SCREAMING_SNAKE_CASE_		))		):
							return value
						elif isinstance(SCREAMING_SNAKE_CASE_ ,					(np.character, np.ndarray)		) and np.issubdtype(value.dtype ,					np.character		):
							return value.tolist()

						UpperCAmelCase_								=     {}

						if isinstance(SCREAMING_SNAKE_CASE_ ,					(np.number, np.ndarray)		) and np.issubdtype(value.dtype ,					np.integer		):
							# the default int precision depends on the jax config
							# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
							if jax.config.jax_enable_xaa:
								UpperCAmelCase_								=     {"""dtype""": jnp.intaa}
							else:
								UpperCAmelCase_								=     {"""dtype""": jnp.intaa}
						elif isinstance(SCREAMING_SNAKE_CASE_ ,					(np.number, np.ndarray)		) and np.issubdtype(value.dtype ,					np.floating		):
							UpperCAmelCase_								=     {"""dtype""": jnp.floataa}
						elif config.PIL_AVAILABLE and "PIL" in sys.modules:
							import PIL.Image

							if isinstance(SCREAMING_SNAKE_CASE_ ,					PIL.Image.Image		):
								UpperCAmelCase_								=     np.asarray(SCREAMING_SNAKE_CASE_		)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
						global DEVICE_MAPPING
						if DEVICE_MAPPING is None:
							UpperCAmelCase_								=     self._map_devices_to_str()

						with jax.default_device(DEVICE_MAPPING[self.device]		):
							# calling jnp.array on a np.ndarray does copy the data
							# see https://github.com/google/jax/issues/4486
							return jnp.array(SCREAMING_SNAKE_CASE_ ,					**{**default_dtype, **self.jnp_array_kwargs}		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  Any		):






						'''simple docstring'''







						import jax

						# support for torch, tf, jax etc.
						if config.TORCH_AVAILABLE and "torch" in sys.modules:
							import torch

							if isinstance(SCREAMING_SNAKE_CASE_ ,					torch.Tensor		):
								return self._tensorize(data_struct.detach().cpu().numpy()[()]		)
						if hasattr(SCREAMING_SNAKE_CASE_ ,					"""__array__"""		) and not isinstance(SCREAMING_SNAKE_CASE_ ,					jax.Array		):
							UpperCAmelCase_								=     data_struct.__array__()
						# support for nested types like struct of list of struct
						if isinstance(SCREAMING_SNAKE_CASE_ ,					np.ndarray		):
							if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
								return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE_		) for substruct in data_struct]		)
						elif isinstance(SCREAMING_SNAKE_CASE_ ,					(list, tuple)		):
							return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE_		) for substruct in data_struct]		)
						return self._tensorize(SCREAMING_SNAKE_CASE_		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any] ,					__lowercase    :  Dict		):






						'''simple docstring'''







						return map_nested(self._recursive_tensorize ,					SCREAMING_SNAKE_CASE_ ,					map_list=SCREAMING_SNAKE_CASE_		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict ,					__lowercase    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.numpy_arrow_extractor().extract_row(SCREAMING_SNAKE_CASE_		)
						UpperCAmelCase_								=     self.python_features_decoder.decode_row(SCREAMING_SNAKE_CASE_		)
						return self.recursive_tensorize(SCREAMING_SNAKE_CASE_		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.numpy_arrow_extractor().extract_column(SCREAMING_SNAKE_CASE_		)
						UpperCAmelCase_								=     self.python_features_decoder.decode_column(SCREAMING_SNAKE_CASE_ ,					pa_table.column_names[0]		)
						UpperCAmelCase_								=     self.recursive_tensorize(SCREAMING_SNAKE_CASE_		)
						UpperCAmelCase_								=     self._consolidate(SCREAMING_SNAKE_CASE_		)
						return column





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






						'''simple docstring'''







						UpperCAmelCase_								=     self.numpy_arrow_extractor().extract_batch(SCREAMING_SNAKE_CASE_		)
						UpperCAmelCase_								=     self.python_features_decoder.decode_batch(SCREAMING_SNAKE_CASE_		)
						UpperCAmelCase_								=     self.recursive_tensorize(SCREAMING_SNAKE_CASE_		)
						for column_name in batch:
							UpperCAmelCase_								=     self._consolidate(batch[column_name]		)
						return batch









from __future__ import annotations

UpperCamelCase__   : str					  = [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]




def    A_(			A				,      A				,      A				,      A				,      A				,      ):
	UpperCAmelCase_								=     [
	    [0 for col in range(len(grid[0]   )   )] for row in range(len(A   )   )
	]  # the reference grid
	UpperCAmelCase_								=     1
	UpperCAmelCase_								=     [
	    [0 for col in range(len(grid[0]   )   )] for row in range(len(A   )   )
	]  # 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(A   ) == 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(A   )   ):  # to try out different valid actions
					UpperCAmelCase_								=     x + DIRECTIONS[i][0]
					UpperCAmelCase_								=     y + DIRECTIONS[i][1]
					if xa >= 0 and xa < len(A   ) 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(A   )   ):
		path.append(invpath[len(A   ) - 1 - i]   )
	return path, action


if __name__ == "__main__":
				UpperCamelCase__   : Tuple					  = [
				    [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__   : Dict					  = [0, 0]
				# all coordinates are given in format [y,x]
				UpperCamelCase__   : Optional[int]					  = [len(grid) - 1, len(grid[0]) - 1]
				UpperCamelCase__   : Optional[Any]					  = 1

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

				UpperCamelCase__  ,			UpperCamelCase__   : Dict					  = 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])


