


'''simple docstring'''


import math






class 	lowerCAmelCase__			:




			def 							lowerCAmelCase__    (		self  :     int	,  lowerCamelCase__  :     list[list[float]]	,  lowerCamelCase__  :     list[int] )   ->int:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      0.0
					_UpperCAmelCase		:       List[Any]								=      0.0
					for i in range(len(_lowerCAmelCase ) ):
							da += math.pow((sample[i] - weights[0][i])	,  2 )
							da += math.pow((sample[i] - weights[1][i])	,  2 )
							return 0 if da > da else 1
					return 0






			def 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     list[list[int | float]]	,  lowerCamelCase__  :     list[int]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     float )   ->list[list[int | float]]:



					'''simple docstring'''







					for i in range(len(_lowerCAmelCase ) ):
							weights[j][i] += alpha * (sample[i] - weights[j][i])
					return weights






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       str								=      [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

		# weight initialization ( n, C )
		_UpperCAmelCase		:       Optional[int]								=      [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

		# training
		_UpperCAmelCase		:       List[Any]								=      SelfOrganizingMap()
		_UpperCAmelCase		:       Union[str, Any]								=      3
		_UpperCAmelCase		:       List[Any]								=      0.5

		for _ in range(__lowerCAmelCase   ):
				for j in range(len(__lowerCAmelCase   )   ):
						# training sample
						_UpperCAmelCase		:       Optional[Any]								=      training_samples[j]

						# Compute the winning vector
						_UpperCAmelCase		:       int								=      self_organizing_map.get_winner(__lowerCAmelCase      , __lowerCAmelCase   )

						# Update the winning vector
						_UpperCAmelCase		:       List[Any]								=      self_organizing_map.update(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )

    # classify test sample
		_UpperCAmelCase		:       Tuple								=      [0, 0, 0, 1]
		_UpperCAmelCase		:       Dict								=      self_organizing_map.get_winner(__lowerCAmelCase      , __lowerCAmelCase   )

		# results
		print(F"""Clusters that the test sample belongs to : {winner}"""   )
		print(F"""Weights that have been trained : {weights}"""   )


# running the main() function
if __name__ == "__main__":
	main()







'''simple docstring'''


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
						lowerCamelCase__    	=     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__    	=     parser.parse_args()

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

						lowerCamelCase__    	=     model.state_dict()
						lowerCamelCase__    	=     {}

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

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

																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
																		]

																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
																		]
												std_idx += 1

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



