




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Union[str, Any]       =			('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

     lowercase :       Optional[Any]       =			(
         ('''layer.''', '''layer_'''),
         ('''word_embeddings.weight''', '''word_embeddings'''),
         ('''position_embeddings.weight''', '''position_embeddings'''),
         ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
         ('''.''', '''/'''),
         ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
         ('''LayerNorm/bias''', '''LayerNorm/beta'''),
         ('''weight''', '''kernel'''),
     )

     if not os.path.isdir(__magic_name__   ):
          os.makedirs(__magic_name__   )

     lowercase :       Optional[Any]       =			model.state_dict()

     def to_tf_var_name(__magic_name__   ):
          for patt, repl in iter(__magic_name__   ):
               lowercase :       str       =			name.replace(__magic_name__			,		__magic_name__   )
          return F"""bert/{name}"""

     def create_tf_var(__magic_name__			,		__magic_name__			,		__magic_name__   ):
          lowercase :       int       =			tf.dtypes.as_dtype(tensor.dtype   )
          lowercase :       int       =			tf.get_variable(dtype=__magic_name__			,		shape=tensor.shape			,		name=__magic_name__			,		initializer=tf.zeros_initializer()   )
          session.run(tf.variables_initializer([tf_var]   )   )
          session.run(__magic_name__   )
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               lowercase :       Any       =			to_tf_var_name(__magic_name__   )
               lowercase :       Tuple       =			state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose   ):
                    lowercase :       Dict       =			torch_tensor.T
               lowercase :       Tuple       =			create_tf_var(tensor=__magic_name__			,		name=__magic_name__			,		session=__magic_name__   )
               tf.keras.backend.set_value(__magic_name__			,		__magic_name__   )
               lowercase :       List[str]       =			session.run(__magic_name__   )
               print(F"""Successfully created {tf_name}: {np.allclose(__magic_name__			,		__magic_name__   )}"""   )

          lowercase :       str       =			tf.train.Saver(tf.trainable_variables()   )
          saver.save(__magic_name__			,		os.path.join(__magic_name__			,		model_name.replace('''-'''			,		'''_'''   ) + '''.ckpt'''   )   )






def  snake_case(    __magic_name__=None   )			->							List[Any]:





     '''simple docstring'''


     lowercase :       str       =			argparse.ArgumentParser()
     parser.add_argument('''--model_name'''			,		type=__magic_name__			,		required=__magic_name__			,		help='''model name e.g. bert-base-uncased'''   )
     parser.add_argument(
         '''--cache_dir'''			,		type=__magic_name__			,		default=__magic_name__			,		required=__magic_name__			,		help='''Directory containing pytorch model'''   )
     parser.add_argument('''--pytorch_model_path'''			,		type=__magic_name__			,		required=__magic_name__			,		help='''/path/to/<pytorch-model-name>.bin'''   )
     parser.add_argument('''--tf_cache_dir'''			,		type=__magic_name__			,		required=__magic_name__			,		help='''Directory in which to save tensorflow model'''   )
     lowercase :       int       =			parser.parse_args(__magic_name__   )

     lowercase :       Union[str, Any]       =			BertModel.from_pretrained(
         pretrained_model_name_or_path=args.model_name			,		state_dict=torch.load(args.pytorch_model_path   )			,		cache_dir=args.cache_dir			,		)

     convert_pytorch_checkpoint_to_tf(model=__magic_name__			,		ckpt_dir=args.tf_cache_dir			,		model_name=args.model_name   )


if __name__ == "__main__":
 main()





from __future__ import annotations

import time

lowerCAmelCase_						=			list[tuple[int, int]]

lowerCAmelCase_						=			[
    [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],
]

lowerCAmelCase_						=			[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right
class 						_A :






  def __init__(  self		:     List[Any]				,						_A		:     int				,						_A		:     int				,						_A		:     int				,						_A		:     int				,						_A		:     Node | None				)		->   str:






       """simple docstring"""







       lowercase :       Any       =			pos_x
       lowercase :       Union[str, Any]       =			pos_y
       lowercase :       int       =			(pos_y, pos_x)
       lowercase :       List[str]       =			goal_x
       lowercase :       Optional[int]       =			goal_y
       lowercase :       Union[str, Any]       =			parent


class 						_A :






  def __init__(  self		:     Optional[Any]				,						_A		:     tuple[int, int]				,						_A		:     tuple[int, int]				)		->   List[Any]:






       """simple docstring"""







       lowercase :       int       =			Node(start[1]				,						start[0]				,						goal[1]				,						goal[0]				,						_A				)
       lowercase :       int       =			Node(goal[1]				,						goal[0]				,						goal[1]				,						goal[0]				,						_A				)

       lowercase :       List[str]       =			[self.start]
       lowercase :       Optional[int]       =			False






  def  __a      (  self		:     Dict				)		->   Path | None:






       """simple docstring"""







       while self.node_queue:
            lowercase :       Optional[Any]       =			self.node_queue.pop(0				)

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

            lowercase :       Union[str, Any]       =			self.get_successors(_A				)

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

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






  def  __a      (  self		:     str				,						_A		:     Node				)		->   list[Node]:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			[]
       for action in delta:
            lowercase :       List[str]       =			parent.pos_x + action[1]
            lowercase :       str       =			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  __a      (  self		:     Any				,						_A		:     Node | None				)		->   Path:






       """simple docstring"""







       lowercase :       Optional[int]       =			node
       lowercase :       Dict       =			[]
       while current_node is not None:
            path.append((current_node.pos_y, current_node.pos_x)				)
            lowercase :       Tuple       =			current_node.parent
       path.reverse()
       return path


class 						_A :






  def __init__(  self		:     Optional[int]				,						_A		:     List[str]				,						_A		:     List[str]				)		->   int:






       """simple docstring"""







       lowercase :       str       =			BreadthFirstSearch(_A				,						_A				)
       lowercase :       Optional[Any]       =			BreadthFirstSearch(_A				,						_A				)
       lowercase :       List[str]       =			False






  def  __a      (  self		:     Tuple				)		->   Path | None:






       """simple docstring"""







       while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
            lowercase :       str       =			self.fwd_bfs.node_queue.pop(0				)
            lowercase :       Tuple       =			self.bwd_bfs.node_queue.pop(0				)

            if current_bwd_node.pos == current_fwd_node.pos:
                 lowercase :       Optional[Any]       =			True
                 return self.retrace_bidirectional_path(
                     _A				,						_A				)

            lowercase :       Optional[int]       =			current_bwd_node
            lowercase :       Optional[int]       =			current_fwd_node

            lowercase :       Union[str, Any]       =			{
                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  __a      (  self		:     List[Any]				,						_A		:     Node				,						_A		:     Node				)		->   Path:






       """simple docstring"""







       lowercase :       Optional[Any]       =			self.fwd_bfs.retrace_path(_A				)
       lowercase :       Union[str, Any]       =			self.bwd_bfs.retrace_path(_A				)
       bwd_path.pop()
       bwd_path.reverse()
       lowercase :       Optional[int]       =			fwd_path + bwd_path
       return path


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

 doctest.testmod()
 lowerCAmelCase_						=			(0, 0)
 lowerCAmelCase_						=			(len(grid) - 1, len(grid[0]) - 1)
 for elem in grid:
  print(elem)

 lowerCAmelCase_						=			time.time()
 lowerCAmelCase_						=			BreadthFirstSearch(init, goal)
 lowerCAmelCase_						=			bfs.search()
 lowerCAmelCase_						=			time.time() - start_bfs_time

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

 lowerCAmelCase_						=			time.time()
 lowerCAmelCase_						=			BidirectionalBreadthFirstSearch(init, goal)
 lowerCAmelCase_						=			bd_bfs.search()
 lowerCAmelCase_						=			time.time() - start_bd_bfs_time

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