'''simple docstring'''





from __future__ import annotations

import time

SCREAMING_SNAKE_CASE_:					Optional[Any]  =list[tuple[int, int]]

SCREAMING_SNAKE_CASE_:					int  =[
    [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],
]

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




class 	__A				:



    def __init__(self   :   int       ,      __a   :   int       ,      __a   :   int       ,      __a   :   int       ,      __a   :   int       ,      __a   :   Node | None			):
         UpperCAmelCase_	   =       pos_x
         UpperCAmelCase_	   =       pos_y
         UpperCAmelCase_	   =       (pos_y, pos_x)
         UpperCAmelCase_	   =       goal_x
         UpperCAmelCase_	   =       goal_y
         UpperCAmelCase_	   =       parent




class 	__A				:



    def __init__(self   :   Dict       ,      __a   :   tuple[int, int]       ,      __a   :   tuple[int, int]			):
         UpperCAmelCase_	   =       Node(start[1]       ,      start[0]       ,      goal[1]       ,      goal[0]       ,      __A			)
         UpperCAmelCase_	   =       Node(goal[1]       ,      goal[0]       ,      goal[1]       ,      goal[0]       ,      __A			)

         UpperCAmelCase_	   =       [self.start]
         UpperCAmelCase_	   =       False



    def  _lowercase						(self   :   List[Any]			):
         while self.node_queue:
              UpperCAmelCase_	   =       self.node_queue.pop(0			)

              if current_node.pos == self.target.pos:
                   UpperCAmelCase_	   =       True
                   return self.retrace_path(__A			)

              UpperCAmelCase_	   =       self.get_successors(__A			)

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

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



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   Node			):
         UpperCAmelCase_	   =       []
         for action in delta:
              UpperCAmelCase_	   =       parent.pos_x + action[1]
              UpperCAmelCase_	   =       parent.pos_y + action[0]
              if not (0 <= pos_x <= len(grid[0]			) - 1 and 0 <= pos_y <= len(__A			) - 1):
                   continue

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

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






    def  _lowercase						(self   :   int       ,      __a   :   Node | None			):
         UpperCAmelCase_	   =       node
         UpperCAmelCase_	   =       []
         while current_node is not None:
              path.append((current_node.pos_y, current_node.pos_x)			)
              UpperCAmelCase_	   =       current_node.parent
         path.reverse()
         return path




class 	__A				:



    def __init__(self   :   Dict       ,      __a   :   List[str]       ,      __a   :   Dict			):
         UpperCAmelCase_	   =       BreadthFirstSearch(__A       ,      __A			)
         UpperCAmelCase_	   =       BreadthFirstSearch(__A       ,      __A			)
         UpperCAmelCase_	   =       False



    def  _lowercase						(self   :   Dict			):
         while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
              UpperCAmelCase_	   =       self.fwd_bfs.node_queue.pop(0			)
              UpperCAmelCase_	   =       self.bwd_bfs.node_queue.pop(0			)

              if current_bwd_node.pos == current_fwd_node.pos:
                   UpperCAmelCase_	   =       True
                   return self.retrace_bidirectional_path(
                       __A       ,      __A			)

              UpperCAmelCase_	   =       current_bwd_node
              UpperCAmelCase_	   =       current_fwd_node

              UpperCAmelCase_	   =       {
                  self.fwd_bfs: self.fwd_bfs.get_successors(__A			),
                  self.bwd_bfs: self.bwd_bfs.get_successors(__A			),
              }

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

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






    def  _lowercase						(self   :   Any       ,      __a   :   Node       ,      __a   :   Node			):
         UpperCAmelCase_	   =       self.fwd_bfs.retrace_path(__A			)
         UpperCAmelCase_	   =       self.bwd_bfs.retrace_path(__A			)
         bwd_path.pop()
         bwd_path.reverse()
         UpperCAmelCase_	   =       fwd_path + bwd_path
         return path


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

       doctest.testmod()
       SCREAMING_SNAKE_CASE_:					List[Any]  =(0, 0)
       SCREAMING_SNAKE_CASE_:					Union[str, Any]  =(len(grid) - 1, len(grid[0]) - 1)
       for elem in grid:
              print(elem)

       SCREAMING_SNAKE_CASE_:					Dict  =time.time()
       SCREAMING_SNAKE_CASE_:					Any  =BreadthFirstSearch(init, goal)
       SCREAMING_SNAKE_CASE_:					int  =bfs.search()
       SCREAMING_SNAKE_CASE_:					List[str]  =time.time() - start_bfs_time

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

       SCREAMING_SNAKE_CASE_:					Optional[int]  =time.time()
       SCREAMING_SNAKE_CASE_:					str  =BidirectionalBreadthFirstSearch(init, goal)
       SCREAMING_SNAKE_CASE_:					Optional[int]  =bd_bfs.search()
       SCREAMING_SNAKE_CASE_:					Optional[int]  =time.time() - start_bd_bfs_time

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





'''simple docstring'''





# 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

SCREAMING_SNAKE_CASE_:					List[str]  =get_logger()

SCREAMING_SNAKE_CASE_:					Optional[dict]  =None




class 	__A				(			TensorFormatter[Mapping, """jax.Array""", Mapping]     ):



    def __init__(self   :   List[Any]       ,      __a   :   Optional[int]=None       ,      __a   :   Any=None       ,      **__a   :   Dict			):
         super().__init__(features=__a			)
         import jax
         from jaxlib.xla_client import Device

         if isinstance(__a       ,      __a			):
              raise ValueError(
                  f"""Expected {device} to be a `str` not {type(__a			)}, 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(__a       ,      __a			) 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  _lowercase						():
         import jax

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



    def  _lowercase						(self   :   str       ,      __a   :   Tuple			):
         import jax
         import jax.numpy as jnp

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



    def  _lowercase						(self   :   Any       ,      __a   :   Optional[int]			):
         import jax
         import jax.numpy as jnp

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

         UpperCAmelCase_	   =       {}

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

        # 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(__a       ,      **{**default_dtype, **self.jnp_array_kwargs}			)



    def  _lowercase						(self   :   int       ,      __a   :   Any			):
         import jax

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

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



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   dict			):
         return map_nested(self._recursive_tensorize       ,      __a       ,      map_list=__a			)



    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_row(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_row(__a			)
         return self.recursive_tensorize(__a			)



    def  _lowercase						(self   :   Tuple       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_column(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_column(__a       ,      pa_table.column_names[0]			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         UpperCAmelCase_	   =       self._consolidate(__a			)
         return column






    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_batch(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_batch(__a			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         for column_name in batch:
              UpperCAmelCase_	   =       self._consolidate(batch[column_name]			)
         return batch




