


from __future__ import annotations

__a     =			list[tuple[int, int]]

__a     =			[
    [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],
]

__a     =			([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class   __lowercase				:





      def __init__(		self						:					Union[str, Any]					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					float					,       __lowerCamelCase						:					Node | None					,       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     pos_x
         UpperCAmelCase							=     pos_y
         UpperCAmelCase							=     (pos_y, pos_x)
         UpperCAmelCase							=     goal_x
         UpperCAmelCase							=     goal_y
         UpperCAmelCase							=     g_cost
         UpperCAmelCase							=     parent
         UpperCAmelCase							=     self.calculate_heuristic()





      def        _lowercase				(		self						:					List[str]       )    ->   float:






         """simple docstring"""







         UpperCAmelCase							=     abs(self.pos_x - self.goal_x       )
         UpperCAmelCase							=     abs(self.pos_y - self.goal_y       )
         return dx + dy





      def __lt__(		self						:					Union[str, Any]					,       __lowerCamelCase						:					Dict       )    ->   bool:






         """simple docstring"""







         return self.f_cost < other.f_cost






class   __lowercase				:





      def __init__(		self						:					str					,       __lowerCamelCase						:					tuple[int, int]					,       __lowerCamelCase						:					tuple[int, int]       )    ->   str:






         """simple docstring"""







         UpperCAmelCase							=     Node(start[1]					,       start[0]					,       goal[1]					,       goal[0]					,       0					,       __lowerCamelCase       )
         UpperCAmelCase							=     Node(goal[1]					,       goal[0]					,       goal[1]					,       goal[0]					,       9_9_9_9_9					,       __lowerCamelCase       )

         UpperCAmelCase							=     [self.start]
         UpperCAmelCase							=     []

         UpperCAmelCase							=     False





      def        _lowercase				(		self						:					List[Any]       )    ->   Path | None:






         """simple docstring"""







         while self.open_nodes:
            # Open Nodes are sorted using __lt__
            self.open_nodes.sort()
            UpperCAmelCase							=     self.open_nodes.pop(0       )

            if current_node.pos == self.target.pos:
               UpperCAmelCase							=     True
               return self.retrace_path(__lowerCamelCase       )

            self.closed_nodes.append(__lowerCamelCase       )
            UpperCAmelCase							=     self.get_successors(__lowerCamelCase       )

            for child_node in successors:
               if child_node in self.closed_nodes:
                  continue

               if child_node not in self.open_nodes:
                  self.open_nodes.append(__lowerCamelCase       )
               else:
                  # retrieve the best current path
                  UpperCAmelCase							=     self.open_nodes.pop(self.open_nodes.index(__lowerCamelCase       )       )

                  if child_node.g_cost < better_node.g_cost:
                     self.open_nodes.append(__lowerCamelCase       )
                  else:
                     self.open_nodes.append(__lowerCamelCase       )

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





      def        _lowercase				(		self						:					Union[str, Any]					,       __lowerCamelCase						:					Node       )    ->   list[Node]:






         """simple docstring"""







         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(__lowerCamelCase       ) - 1):
               continue

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

            successors.append(
                Node(
                    __lowerCamelCase					,       __lowerCamelCase					,       self.target.pos_y					,       self.target.pos_x					,       parent.g_cost + 1					,       __lowerCamelCase					,       )       )
         return successors





      def        _lowercase				(		self						:					Optional[Any]					,       __lowerCamelCase						:					Node | None       )    ->   Path:






         """simple docstring"""







         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


if __name__ == "__main__":
 __a     =			(0, 0)
 __a     =			(len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 print("""------""")

 __a     =			GreedyBestFirst(init, goal)
 __a     =			greedy_bf.search()
 if path:
  for pos_x, pos_y in path:
   __a     =			2

  for elem in grid:
   print(elem)







from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


__a     =			logging.get_logger(__name__)
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Tuple:
   try:
      with open(lowerCAmelCase_  ,						"""rb"""   ) as flax_state_f:
         UpperCAmelCase							=     from_bytes(lowerCAmelCase_  ,						flax_state_f.read()   )
   except UnpicklingError as e:
      try:
         with open(lowerCAmelCase_   ) as f:
            if f.read().startswith("""version"""   ):
               raise OSError(
                   """You seem to have cloned a repository without having git-lfs installed. Please"""
                   """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
                   """ folder you cloned."""   )
            else:
               raise ValueError from e
      except (UnicodeDecodeError, ValueError):
         raise EnvironmentError(F"""Unable to convert {model_file} to Flax deserializable object. """   )

   return load_flax_weights_in_pytorch_model(lowerCAmelCase_  ,						lowerCAmelCase_   )







def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Dict:

   try:
      import torch  # noqa: F401
   except ImportError:
      logger.error(
          """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
          """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
          """ instructions."""   )
      raise

   # check if we have bf16 weights
   UpperCAmelCase							=     flatten_dict(jax.tree_util.tree_map(lambda lowerCAmelCase_   : x.dtype == jnp.bfloataa  ,						lowerCAmelCase_   )   ).values()
   if any(lowerCAmelCase_   ):
      # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

      # and bf16 is not fully supported in PT yet.
      logger.warning(
          """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
          """before loading those in PyTorch model."""   )
      UpperCAmelCase							=     jax.tree_util.tree_map(
          lambda lowerCAmelCase_   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params  ,						lowerCAmelCase_   )

   UpperCAmelCase							=     """"""

   UpperCAmelCase							=     flatten_dict(lowerCAmelCase_  ,						sep="""."""   )
   UpperCAmelCase							=     pt_model.state_dict()

   # keep track of unexpected & missing keys
   UpperCAmelCase							=     []
   UpperCAmelCase							=     set(pt_model_dict.keys()   )

   for flax_key_tuple, flax_tensor in flax_state_dict.items():
      UpperCAmelCase							=     flax_key_tuple.split("""."""   )

      if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
         UpperCAmelCase							=     flax_key_tuple_array[:-1] + ["""weight"""]
         UpperCAmelCase							=     jnp.transpose(lowerCAmelCase_  ,						(3, 2, 0, 1)   )
      elif flax_key_tuple_array[-1] == "kernel":
         UpperCAmelCase							=     flax_key_tuple_array[:-1] + ["""weight"""]
         UpperCAmelCase							=     flax_tensor.T
      elif flax_key_tuple_array[-1] == "scale":
         UpperCAmelCase							=     flax_key_tuple_array[:-1] + ["""weight"""]

      if "time_embedding" not in flax_key_tuple_array:
         for i, flax_key_tuple_string in enumerate(lowerCAmelCase_   ):
            UpperCAmelCase							=     (
                flax_key_tuple_string.replace("""_0"""  ,						""".0"""   )
                .replace("""_1"""  ,						""".1"""   )
                .replace("""_2"""  ,						""".2"""   )
                .replace("""_3"""  ,						""".3"""   )
                .replace("""_4"""  ,						""".4"""   )
                .replace("""_5"""  ,						""".5"""   )
                .replace("""_6"""  ,						""".6"""   )
                .replace("""_7"""  ,						""".7"""   )
                .replace("""_8"""  ,						""".8"""   )
                .replace("""_9"""  ,						""".9"""   )
            )

      UpperCAmelCase							=     """.""".join(lowerCAmelCase_   )

      if flax_key in pt_model_dict:
         if flax_tensor.shape != pt_model_dict[flax_key].shape:
            raise ValueError(
                F"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
                F"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""   )
         else:
            # add weight to pytorch dict
            UpperCAmelCase							=     np.asarray(lowerCAmelCase_   ) if not isinstance(lowerCAmelCase_  ,						np.ndarray   ) else flax_tensor
            UpperCAmelCase							=     torch.from_numpy(lowerCAmelCase_   )
            # remove from missing keys
            missing_keys.remove(lowerCAmelCase_   )
      else:
         # weight is not expected by PyTorch model
         unexpected_keys.append(lowerCAmelCase_   )

   pt_model.load_state_dict(lowerCAmelCase_   )

   # re-transform missing_keys to list
   UpperCAmelCase							=     list(lowerCAmelCase_   )

   if len(lowerCAmelCase_   ) > 0:
      logger.warning(
          """Some weights of the Flax model were not used when initializing the PyTorch model"""
          F""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
          F""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
          """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
          F""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
          """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
          """ FlaxBertForSequenceClassification model)."""   )
   if len(lowerCAmelCase_   ) > 0:
      logger.warning(
          F"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
          F""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
          """ use it for predictions and inference."""   )

   return pt_model



