


import numpy as np






class    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self   )  ->	str:
         __UpperCamelCase   							=(0, 0)
         __UpperCamelCase   							=None
         __UpperCamelCase   							=0
         __UpperCamelCase   							=0
         __UpperCamelCase   							=0






    def __eq__(      self ,       A_   )  ->	List[Any]:
         return self.position == cell.position






    def _a (      self   )  ->	Tuple:
         print(self.position   )






class    UpperCAmelCase__	:



    """simple docstring"""






    def __init__(      self ,       A_=(5, 5)   )  ->	Union[str, Any]:
         __UpperCamelCase   							=np.zeros(A_   )
         __UpperCamelCase   							=world_size[0]
         __UpperCamelCase   							=world_size[1]






    def _a (      self   )  ->	Dict:
         print(self.w   )






    def _a (      self ,       A_   )  ->	str:
         __UpperCamelCase   							=[
             (-1, -1),
             (-1, 0),
             (-1, 1),
             (0, -1),
             (0, 1),
             (1, -1),
             (1, 0),
             (1, 1),
         ]
         __UpperCamelCase   							=cell.position[0]
         __UpperCamelCase   							=cell.position[1]
         __UpperCamelCase   							=[]
         for n in neughbour_cord:
              __UpperCamelCase   							=current_x + n[0]
              __UpperCamelCase   							=current_y + n[1]
              if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                   __UpperCamelCase   							=Cell()
                   __UpperCamelCase   							=(x, y)
                   __UpperCamelCase   							=cell
                   neighbours.append(A_   )
         return neighbours

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Any    ):
     __UpperCamelCase   							=[]
     __UpperCamelCase   							=[]
     _open.append(SCREAMING_SNAKE_CASE__    )

     while _open:
          __UpperCamelCase   							=np.argmin([n.f for n in _open]    )
          __UpperCamelCase   							=_open[min_f]
          _closed.append(_open.pop(SCREAMING_SNAKE_CASE__    )    )
          if current == goal:
               break
          for n in world.get_neigbours(SCREAMING_SNAKE_CASE__    ):
               for c in _closed:
                    if c == n:
                         continue
               __UpperCamelCase   							=current.g + 1
               __UpperCamelCase							,				__UpperCamelCase   							=n.position
               __UpperCamelCase							,				__UpperCamelCase   							=goal.position
               __UpperCamelCase   							=(ya - ya) ** 2 + (xa - xa) ** 2
               __UpperCamelCase   							=n.h + n.g

               for c in _open:
                    if c == n and c.f < n.f:
                         continue
               _open.append(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=[]
     while current.parent is not None:
          path.append(current.position    )
          __UpperCamelCase   							=current.parent
     path.append(current.position    )
     return path[::-1]


if __name__ == "__main__":
   _A			=						Gridworld()
   # Start position and goal
   _A			=						Cell()
   _A			=						(0, 0)
   _A			=						Cell()
   _A			=						(4, 4)
   print(f"""path from {start.position} to {goal.position}""")
   _A			=						astar(world, start, goal)
   #   Just for visual reasons.
   for i in s:
      _A			=						1
   print(world.w)









import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
   _A			=						argparse.ArgumentParser(
       description=(
           'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
           ' Distillation'
       )
   )
   parser.add_argument('--model_type', default='bert', choices=['bert'])
   parser.add_argument('--model_name', default='bert-base-uncased', type=str)
   parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
   parser.add_argument('--vocab_transform', action='store_true')
   _A			=						parser.parse_args()

   if args.model_type == "bert":
      _A			=						BertForMaskedLM.from_pretrained(args.model_name)
      _A			=						'bert'
   else:
      raise ValueError('args.model_type should be "bert".')

   _A			=						model.state_dict()
   _A			=						{}

   for w in ["word_embeddings", "position_embeddings"]:
      _A			=						state_dict[f"""{prefix}.embeddings.{w}.weight"""]
   for w in ["weight", "bias"]:
      _A			=						state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

   _A			=						0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      for w in ["weight", "bias"]:
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
         ]

         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
         ]

         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
         ]
         _A			=						state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
         ]
      std_idx += 1

   _A			=						state_dict['cls.predictions.decoder.weight']
   _A			=						state_dict['cls.predictions.bias']
   if args.vocab_transform:
      for w in ["weight", "bias"]:
         _A			=						state_dict[f"""cls.predictions.transform.dense.{w}"""]
         _A			=						state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

   print(f"""N layers selected for distillation: {std_idx}""")
   print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

   print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
   torch.save(compressed_sd, args.dump_checkpoint)





