





'''simple docstring'''


import math






class       UpperCamelCase__  :







	def 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   list[list[float]] , lowerCamelCase			:   list[int]		):




				'''simple docstring'''
				a__     = 0.0
				a__     = 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 						__a   (				self			:   str , lowerCamelCase			:   list[list[int | float]] , lowerCamelCase			:   list[int] , lowerCamelCase			:   int , lowerCamelCase			:   float		):




				'''simple docstring'''
				for i in range(len(lowerCamelCase		)		):
							weights[j][i] += alpha * (sample[i] - weights[j][i])
				return weights







def    _lowerCamelCase  ()   ->    None:
			# Training Examples ( m, n )
			a__     = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

			# weight initialization ( n, C )
			a__     = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

			# training
			a__     = SelfOrganizingMap()
			a__     = 3
			a__     = 0.5

			for _ in range(__lowerCamelCase     ):
						for j in range(len(__lowerCamelCase     )     ):
									# training sample
									a__     = training_samples[j]

									# Compute the winning vector
									a__     = self_organizing_map.get_winner(__lowerCamelCase					,  __lowerCamelCase     )

									# Update the winning vector
									a__     = self_organizing_map.update(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )

    # classify test sample
			a__     = [0, 0, 0, 1]
			a__     = 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 torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    Optional[int]:
			a__     = [
			    "encoder.version",
			    "decoder.version",
			    "model.encoder.version",
			    "model.decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "encoder.embed_positions._float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    List[Any]:
			a__   ,				a__     = emb.weight.shape
			a__     = nn.Linear(__lowerCamelCase					,  __lowerCamelCase					,  bias=__lowerCamelCase     )
			a__     = emb.weight.data
			return lin_layer







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    Any:
			a__     = torch.load(__lowerCamelCase					,  map_location="cpu"     )
			a__     = mam_aaa["args"] or mam_aaa["cfg"]["model"]
			a__     = mam_aaa["model"]
			remove_ignore_keys_(__lowerCamelCase     )
			a__     = state_dict["encoder.embed_tokens.weight"].shape[0]

			a__     = MaMaaaConfig(
			    vocab_size=__lowerCamelCase					,  max_position_embeddings=1024					,  encoder_layers=args.encoder_layers					,  decoder_layers=args.decoder_layers					,  encoder_attention_heads=args.encoder_attention_heads					,  decoder_attention_heads=args.decoder_attention_heads					,  encoder_ffn_dim=args.encoder_ffn_embed_dim					,  decoder_ffn_dim=args.decoder_ffn_embed_dim					,  d_model=args.encoder_embed_dim					,  encoder_layerdrop=args.encoder_layerdrop					,  decoder_layerdrop=args.decoder_layerdrop					,  dropout=args.dropout					,  attention_dropout=args.attention_dropout					,  activation_dropout=args.activation_dropout					,  activation_function="relu"					,  )

			a__     = state_dict["decoder.embed_tokens.weight"]
			a__     = MaMaaaForConditionalGeneration(__lowerCamelCase     )
			model.model.load_state_dict(__lowerCamelCase					,  strict=__lowerCamelCase     )
			a__     = make_linear_from_emb(model.model.shared     )

			return model


if __name__ == "__main__":
		lowerCAmelCase_   :   Tuple       =					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("fairseq_path", type=str, help="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.")
		lowerCAmelCase_   :   Tuple       =					parser.parse_args()
		lowerCAmelCase_   :   int       =					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)



