


import numpy as np

class 		__a				:
		def __init__(      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      (0, 0)
									_UpperCAmelCase					=      None
									_UpperCAmelCase					=      0
									_UpperCAmelCase					=      0
									_UpperCAmelCase					=      0
		def __eq__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									return self.position == cell.position






		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									print(self.position						)



class 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=(5, 5)						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      np.zeros(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      world_size[0]
									_UpperCAmelCase					=      world_size[1]
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									print(self.w						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_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(_SCREAMING_SNAKE_CASE						)
									return neighbours



def 	lowerCAmelCase__			(      a__:		List[str]      ,  a__:		Any      ,  a__:		List[Any]	)  ->		List[str]:


							'''simple docstring'''





							_UpperCAmelCase					=      []
							_UpperCAmelCase					=      []
							_open.append(a__	)

							while _open:
														_UpperCAmelCase					=      np.argmin([n.f for n in _open]	)
														_UpperCAmelCase					=      _open[min_f]
														_closed.append(_open.pop(a__	)	)
														if current == goal:
																					break
														for n in world.get_neigbours(a__	):
																					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(a__	)
							_UpperCAmelCase					=      []
							while current.parent is not None:
														path.append(current.position	)
														_UpperCAmelCase					=      current.parent
							path.append(current.position	)
							return path[::-1]


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









import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
		lowerCAmelCase__  :Tuple             =							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''')
		lowerCAmelCase__  :Optional[int]             =							parser.parse_args()

		if args.model_type == "bert":
				lowerCAmelCase__  :Tuple             =							BertForMaskedLM.from_pretrained(args.model_name)
				lowerCAmelCase__  :Optional[int]             =							'''bert'''
		else:
				raise ValueError('''args.model_type should be "bert".''')

		lowerCAmelCase__  :Any             =							model.state_dict()
		lowerCAmelCase__  :Dict             =							{}

		for w in ["word_embeddings", "position_embeddings"]:
				lowerCAmelCase__  :List[Any]             =							state_dict[f'''{prefix}.embeddings.{w}.weight''']
		for w in ["weight", "bias"]:
				lowerCAmelCase__  :Union[str, Any]             =							state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

		lowerCAmelCase__  :str             =							0
		for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
				for w in ["weight", "bias"]:
						lowerCAmelCase__  :Any             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
						]
						lowerCAmelCase__  :List[str]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
						]

						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
						]
						lowerCAmelCase__  :int             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
						]

						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
						]
				std_idx += 1

		lowerCAmelCase__  :Optional[int]             =							state_dict['''cls.predictions.decoder.weight''']
		lowerCAmelCase__  :List[str]             =							state_dict['''cls.predictions.bias''']
		if args.vocab_transform:
				for w in ["weight", "bias"]:
						lowerCAmelCase__  :Any             =							state_dict[f'''cls.predictions.transform.dense.{w}''']
						lowerCAmelCase__  :List[str]             =							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)
