


"""simple docstring"""






import numpy as np


class     __lowerCamelCase  :

     '''simple docstring'''


     def __init__(      self							)       ->							List[str]:
          _a    	=				(0, 0)
          _a    	=				None
          _a    	=				0
          _a    	=				0
          _a    	=				0


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





     def 							_UpperCAmelCase      (      self							)       ->							Any:
          print(self.position							)







class     __lowerCamelCase  :

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase=(5, 5)							)       ->							Dict:
          _a    	=				np.zeros(__UpperCAmelCase							)
          _a    	=				world_size[0]
          _a    	=				world_size[1]


     def 							_UpperCAmelCase      (      self							)       ->							int:
          print(self.w							)





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







def       A_   (   _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Union[str, Any],     _lowerCAmelCase     :					Optional[int]       ):






     """simple docstring"""

     _a    	=				[]
     _a    	=				[]
     _open.append(_lowerCAmelCase       )

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

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


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



"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def       A_   (   _lowerCAmelCase     :					Optional[Any]       ):






     """simple docstring"""

     _a    	=				[
         '''encoder.version''',
         '''decoder.version''',
         '''model.encoder.version''',
         '''model.decoder.version''',
         '''_float_tensor''',
         '''decoder.output_projection.weight''',
     ]
     for k in ignore_keys:
          state_dict.pop(_lowerCAmelCase,     _lowerCAmelCase       )
def       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

     _a	,      _a    	=				emb.weight.shape
     _a    	=				nn.Linear(_lowerCAmelCase,     _lowerCAmelCase,     bias=_lowerCAmelCase       )
     _a    	=				emb.weight.data
     return lin_layer
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					Union[str, Any]="facebook/mbart-large-en-ro",     _lowerCAmelCase     :					Any=False,     _lowerCAmelCase     :					int=False       ):






     """simple docstring"""

     _a    	=				torch.load(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']
     remove_ignore_keys_(_lowerCAmelCase       )
     _a    	=				state_dict['''encoder.embed_tokens.weight'''].shape[0]

     _a    	=				MBartConfig.from_pretrained(_lowerCAmelCase,     vocab_size=_lowerCAmelCase       )
     if mbart_aa and finetuned:
          _a    	=				'''relu'''

     _a    	=				state_dict['''decoder.embed_tokens.weight''']
     _a    	=				MBartForConditionalGeneration(_lowerCAmelCase       )
     model.model.load_state_dict(_lowerCAmelCase       )

     if finetuned:
          _a    	=				make_linear_from_emb(model.model.shared       )

     return model


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
   )
   parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   parser.add_argument(
       '''--hf_config''',
       default='''facebook/mbart-large-cc25''',
       type=str,
       help='''Which huggingface architecture to use: mbart-large''',
   )
   parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
   parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
   __snake_case		=       parser.parse_args()
   __snake_case		=       convert_fairseq_mbart_checkpoint_from_disk(
       args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
   )
   model.save_pretrained(args.pytorch_dump_folder_path)