


from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst

def 	lowerCAmelCase__			(      )  ->		List[Any]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      9, 1_4  # noqa: F841
							_UpperCAmelCase					=      [
							    [0, 1, 4],
							    [0, 7, 8],
							    [1, 2, 8],
							    [7, 8, 7],
							    [7, 6, 1],
							    [2, 8, 2],
							    [8, 6, 6],
							    [2, 3, 7],
							    [2, 5, 4],
							    [6, 5, 2],
							    [3, 5, 1_4],
							    [3, 4, 9],
							    [5, 4, 1_0],
							    [1, 7, 1_1],
							]

							_UpperCAmelCase					=      defaultdict(a__	)
							for nodea, nodea, cost in edges:
														adjancency[nodea].append([nodea, cost]	)
														adjancency[nodea].append([nodea, cost]	)

							_UpperCAmelCase					=      mst(a__	)

							_UpperCAmelCase					=      [
							    [7, 6, 1],
							    [2, 8, 2],
							    [6, 5, 2],
							    [0, 1, 4],
							    [2, 5, 4],
							    [2, 3, 7],
							    [0, 7, 8],
							    [3, 4, 9],
							]

							for answer in expected:
														_UpperCAmelCase					=      tuple(answer[:2]	)
														_UpperCAmelCase					=      tuple(edge[::-1]	)
														assert edge in result or reverse in result




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)
