





from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
lowercase				:	Any      						=  0

lowercase				:	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],
]

lowercase				:	Optional[Any]      						=  [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

lowercase				:	int      						=  tuple[int, int]





class 			A__   :




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  )      ->						None:







          '''simple docstring'''

          a__						:     Optional[Any]			=    pos_x
          a__						:     Dict			=    pos_y
          a__						:     Any			=    (pos_y, pos_x)
          a__						:     Optional[int]			=    goal_x
          a__						:     Union[str, Any]			=    goal_y
          a__						:     str			=    g_cost
          a__						:     Union[str, Any]			=    parent
          a__						:     Tuple			=    self.calculate_heuristic()
          a__						:     Union[str, Any]			=    self.g_cost + self.h_cost

   def 							__lowercase		( self)      ->						float:







          '''simple docstring'''

          a__						:     Tuple			=    self.pos_x - self.goal_x
          a__						:     Optional[Any]			=    self.pos_y - self.goal_y
          if HEURISTIC == 1:
                 return abs(lowercase) + abs(lowercase)
          else:
                 return sqrt(dy**2 + dx**2)

   def __lt__( self ,  lowercase)      ->						bool:







          '''simple docstring'''

          return self.f_cost < other.f_cost





class 			A__   :




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase)      ->						Tuple:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    Node(start[1] ,  start[0] ,  goal[1] ,  goal[0] ,  0 ,  lowercase)
          a__						:     Dict			=    Node(goal[1] ,  goal[0] ,  goal[1] ,  goal[0] ,  9_9999 ,  lowercase)

          a__						:     List[str]			=    [self.start]
          a__						:     list[Node]			=    []

          a__						:     Dict			=    False

   def 							__lowercase		( self)      ->						list[TPosition]:







          '''simple docstring'''

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

                 if current_node.pos == self.target.pos:
                        return self.retrace_path(lowercase)

                 self.closed_nodes.append(lowercase)
                 a__						:     Any			=    self.get_successors(lowercase)

                 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(lowercase)
                        else:
                               # retrieve the best current path
                               a__						:     Optional[int]			=    self.open_nodes.pop(self.open_nodes.index(lowercase))

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

          return [self.start.pos]

   def 							__lowercase		( self ,  lowercase)      ->						list[Node]:







          '''simple docstring'''

          a__						:     str			=    []
          for action in delta:
                 a__						:     Optional[Any]			=    parent.pos_x + action[1]
                 a__						:     int			=    parent.pos_y + action[0]
                 if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(lowercase) - 1):
                        continue

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

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

   def 							__lowercase		( self ,  lowercase)      ->						list[TPosition]:







          '''simple docstring'''

          a__						:     Optional[Any]			=    node
          a__						:     Optional[Any]			=    []
          while current_node is not None:
                 path.append((current_node.pos_y, current_node.pos_x))
                 a__						:     List[str]			=    current_node.parent
          path.reverse()
          return path





class 			A__   :




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase)      ->						None:







          '''simple docstring'''

          a__						:     List[Any]			=    AStar(lowercase ,  lowercase)
          a__						:     Optional[int]			=    AStar(lowercase ,  lowercase)
          a__						:     int			=    False

   def 							__lowercase		( self)      ->						list[TPosition]:







          '''simple docstring'''

          while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
                 self.fwd_astar.open_nodes.sort()
                 self.bwd_astar.open_nodes.sort()
                 a__						:     Any			=    self.fwd_astar.open_nodes.pop(0)
                 a__						:     int			=    self.bwd_astar.open_nodes.pop(0)

                 if current_bwd_node.pos == current_fwd_node.pos:
                        return self.retrace_bidirectional_path(
                            lowercase ,  lowercase)

                 self.fwd_astar.closed_nodes.append(lowercase)
                 self.bwd_astar.closed_nodes.append(lowercase)

                 a__						:     Tuple			=    current_bwd_node
                 a__						:     Optional[int]			=    current_fwd_node

                 a__						:     Tuple			=    {
                     self.fwd_astar: self.fwd_astar.get_successors(lowercase),
                     self.bwd_astar: self.bwd_astar.get_successors(lowercase),
                 }

                 for astar in [self.fwd_astar, self.bwd_astar]:
                        for child_node in successors[astar]:
                               if child_node in astar.closed_nodes:
                                      continue

                               if child_node not in astar.open_nodes:
                                      astar.open_nodes.append(lowercase)
                               else:
                                      # retrieve the best current path
                                      a__						:     Tuple			=    astar.open_nodes.pop(
                                          astar.open_nodes.index(lowercase))

                                      if child_node.g_cost < better_node.g_cost:
                                             astar.open_nodes.append(lowercase)
                                      else:
                                             astar.open_nodes.append(lowercase)

          return [self.fwd_astar.start.pos]

   def 							__lowercase		( self ,  lowercase ,  lowercase)      ->						list[TPosition]:







          '''simple docstring'''

          a__						:     Tuple			=    self.fwd_astar.retrace_path(lowercase)
          a__						:     List[Any]			=    self.bwd_astar.retrace_path(lowercase)
          bwd_path.pop()
          bwd_path.reverse()
          a__						:     Optional[Any]			=    fwd_path + bwd_path
          return path


if __name__ == "__main__":
    # all coordinates are given in format [y,x]
    lowercase				:	str      						=  (0, 0)
    lowercase				:	Optional[Any]      						=  (len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    lowercase				:	Any      						=  time.time()
    lowercase				:	Optional[int]      						=  AStar(init, goal)
    lowercase				:	Union[str, Any]      						=  a_star.search()
    lowercase				:	Any      						=  time.time() - start_time
    print(F"""AStar execution time = {end_time:f} seconds""")

    lowercase				:	List[str]      						=  time.time()
    lowercase				:	str      						=  BidirectionalAStar(init, goal)
    lowercase				:	int      						=  time.time() - bd_start_time
    print(F"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")









import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowercase				:	Union[str, Any]      						=  data_utils.TransfoXLTokenizer
lowercase				:	Optional[int]      						=  data_utils.TransfoXLCorpus
lowercase				:	List[Any]      						=  data_utils
lowercase				:	Tuple      						=  data_utils



def A_     (		A__		,				A__		,				A__		,				A__    )  ->		Optional[Any]:
       if transfo_xl_dataset_file:
              # Convert a pre-processed corpus (see original TensorFlow repo)
              with open(A__		,				'rb'    ) as fp:
                     a__						:     int			=    pickle.load(A__		,				encoding='latin1'    )
              # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
              a__						:     int			=    pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
              print(F'Save vocabulary to {pytorch_vocab_dump_path}'    )
              a__						:     List[Any]			=    corpus.vocab.__dict__
              torch.save(A__		,				A__    )

              a__						:     Dict			=    corpus.__dict__
              corpus_dict_no_vocab.pop('vocab'		,				A__    )
              a__						:     Optional[int]			=    pytorch_dump_folder_path + '/' + CORPUS_NAME
              print(F'Save dataset to {pytorch_dataset_dump_path}'    )
              torch.save(A__		,				A__    )

       if tf_checkpoint_path:
              # Convert a pre-trained TensorFlow model
              a__						:     Union[str, Any]			=    os.path.abspath(A__    )
              a__						:     Optional[Any]			=    os.path.abspath(A__    )

              print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'    )
              # Initialise PyTorch model
              if transfo_xl_config_file == "":
                     a__						:     Dict			=    TransfoXLConfig()
              else:
                     a__						:     Dict			=    TransfoXLConfig.from_json_file(A__    )
              print(F'Building PyTorch model from configuration: {config}'    )
              a__						:     Optional[int]			=    TransfoXLLMHeadModel(A__    )

              a__						:     int			=    load_tf_weights_in_transfo_xl(A__		,				A__		,				A__    )
              # Save pytorch-model
              a__						:     Any			=    os.path.join(A__		,				A__    )
              a__						:     Dict			=    os.path.join(A__		,				A__    )
              print(F'Save PyTorch model to {os.path.abspath(A__    )}'    )
              torch.save(model.state_dict()		,				A__    )
              print(F'Save configuration file to {os.path.abspath(A__    )}'    )
              with open(A__		,				'w'		,				encoding='utf-8'    ) as f:
                     f.write(config.to_json_string()    )


if __name__ == "__main__":
    lowercase				:	List[Any]      						=  argparse.ArgumentParser()
    parser.add_argument(
        """--pytorch_dump_folder_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
    )
    parser.add_argument(
        """--tf_checkpoint_path""",
        default="""""",
        type=str,
        help="""An optional path to a TensorFlow checkpoint path to be converted.""",
    )
    parser.add_argument(
        """--transfo_xl_config_file""",
        default="""""",
        type=str,
        help=(
            """An optional config json file corresponding to the pre-trained BERT model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument(
        """--transfo_xl_dataset_file""",
        default="""""",
        type=str,
        help="""An optional dataset file to be converted in a vocabulary.""",
    )
    lowercase				:	Any      						=  parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )


