




import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
     from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm



def 							_A       (						lowerCAmelCase_					: Optional[int]      ):
     """simple docstring"""

     if is_torch_version("<"       ,    "2.0.0"      ) or not hasattr(lowerCAmelCase_       ,    "_dynamo"      ):
          return False
     return isinstance(lowerCAmelCase_       ,    torch._dynamo.eval_frame.OptimizedModule      )



def 							_A       (						lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: bool = True      ):
     """simple docstring"""

     lowerCAmelCase__          =			(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

     lowerCAmelCase__          =			is_compiled_module(lowerCAmelCase_      )
     if is_compiled:
          lowerCAmelCase__          =			model
          lowerCAmelCase__          =			model._orig_mod

     if is_deepspeed_available():
          options += (DeepSpeedEngine,)

     while isinstance(lowerCAmelCase_       ,    lowerCAmelCase_      ):
          lowerCAmelCase__          =			model.module

     if not keep_fpaa_wrapper:
          lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "forward"      )
          lowerCAmelCase__          =			model.__dict__.pop("_original_forward"       ,    lowerCAmelCase_      )
          if original_forward is not None:
               while hasattr(lowerCAmelCase_       ,    "__wrapped__"      ):
                    lowerCAmelCase__          =			forward.__wrapped__
                    if forward == original_forward:
                         break
               lowerCAmelCase__          =			forward
          if getattr(lowerCAmelCase_       ,    "_converted_to_transformer_engine"       ,    lowerCAmelCase_      ):
               convert_model(lowerCAmelCase_       ,    to_transformer_engine=lowerCAmelCase_      )

     if is_compiled:
          lowerCAmelCase__          =			model
          lowerCAmelCase__          =			compiled_model

     return model



def 							_A       (						):
     """simple docstring"""

     PartialState().wait_for_everyone()



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Optional[Any]      ):
     """simple docstring"""

     if PartialState().distributed_type == DistributedType.TPU:
          xm.save(lowerCAmelCase_       ,    lowerCAmelCase_      )
     elif PartialState().local_process_index == 0:
          torch.save(lowerCAmelCase_       ,    lowerCAmelCase_      )



@contextmanager
def 							_A       (						**lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     for key, value in kwargs.items():
          lowerCAmelCase__          =			str(lowerCAmelCase_      )

     yield

     for key in kwargs:
          if key.upper() in os.environ:
               del os.environ[key.upper()]



def 							_A       (						lowerCAmelCase_					: Optional[Any]      ):
     """simple docstring"""

     if not hasattr(lowerCAmelCase_       ,    "__qualname__"      ) and not hasattr(lowerCAmelCase_       ,    "__name__"      ):
          lowerCAmelCase__          =			getattr(lowerCAmelCase_       ,    "__class__"       ,    lowerCAmelCase_      )
     if hasattr(lowerCAmelCase_       ,    "__qualname__"      ):
          return obj.__qualname__
     if hasattr(lowerCAmelCase_       ,    "__name__"      ):
          return obj.__name__
     return str(lowerCAmelCase_      )



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     for key, value in source.items():
          if isinstance(lowerCAmelCase_       ,    lowerCAmelCase_      ):
               lowerCAmelCase__          =			destination.setdefault(lowerCAmelCase_       ,    {}      )
               merge_dicts(lowerCAmelCase_       ,    lowerCAmelCase_      )
          else:
               lowerCAmelCase__          =			value

     return destination






def 							_A       (						lowerCAmelCase_					: int = None      ):
     """simple docstring"""

     if port is None:
          lowerCAmelCase__          =			2_9500
     with socket.socket(socket.AF_INET       ,    socket.SOCK_STREAM      ) as s:
          return s.connect_ex(("localhost", port)      ) == 0






from __future__ import annotations

UpperCamelCase								=					[
    [-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]]       ,    ):
     """simple docstring"""

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

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

     UpperCamelCase						,  UpperCamelCase								=					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])
