



import math
class 			_snake_case     :




				def 							A__ (  self   :  str,   __lowercase   :  list[list[float]],   __lowercase   :  list[int]  ):
							lowercase__   					=			0.0
							lowercase__   					=			0.0
							for i in range(len(__lowercase  )  ):
										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   :  int,   __lowercase   :  list[list[int | float]],   __lowercase   :  list[int],   __lowercase   :  int,   __lowercase   :  float  ):
							for i in range(len(__lowercase  )  ):
										weights[j][i] += alpha * (sample[i] - weights[j][i])
							return weights



def __lowerCAmelCase	(       ):
			# Training Examples ( m, n )
			lowercase__   					=			[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

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

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

									# Compute the winning vector
									lowercase__   					=			self_organizing_map.get_winner(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

									# Update the winning vector
									lowercase__   					=			self_organizing_map.update(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

    # classify test sample
			lowercase__   					=			[0, 0, 0, 1]
			lowercase__   					=			self_organizing_map.get_winner(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

			# 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()









import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowercase_						=       logging.get_logger(__name__)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			r"\w+[.]\d+"
			lowercase__   					=			re.findall(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
			for pat in pats:
						lowercase__   					=			key.replace(SCREAMING_SNAKE_CASE_  ,					"_".join(pat.split("."					)					)					)
			return key



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			pt_tuple_key[:-1] + ("scale",)
			if (
			    any("norm" in str_ for str_ in pt_tuple_key					)
			    and (pt_tuple_key[-1] == "bias")
			    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
			    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
			):
						lowercase__   					=			pt_tuple_key[:-1] + ("scale",)
						return renamed_pt_tuple_key, pt_tensor
			elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
						lowercase__   					=			pt_tuple_key[:-1] + ("scale",)
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
						lowercase__   					=			pt_tuple_key[:-1] + ("embedding",)
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			lowercase__   					=			pt_tuple_key[:-1] + ("kernel",)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
						lowercase__   					=			pt_tensor.transpose(2  ,					3  ,					1  ,					0					)
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			lowercase__   					=			pt_tuple_key[:-1] + ("kernel",)
			if pt_tuple_key[-1] == "weight":
						lowercase__   					=			pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm weight
			lowercase__   					=			pt_tuple_key[:-1] + ("weight",)
			if pt_tuple_key[-1] == "gamma":
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm bias
			lowercase__   					=			pt_tuple_key[:-1] + ("bias",)
			if pt_tuple_key[-1] == "beta":
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor




def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=42					):
			# Step 1: Convert pytorch tensor to numpy
			lowercase__   					=			{k: v.numpy() for k, v in pt_state_dict.items()}

			# Step 2: Since the model is stateless, get random Flax params
			lowercase__   					=			flax_model.init_weights(PRNGKey(SCREAMING_SNAKE_CASE_					)					)

			lowercase__   					=			flatten_dict(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			{}

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						lowercase__   					=			rename_key(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			tuple(renamed_pt_key.split("."					)					)

						# Correctly rename weight parameters
						lowercase__					,    lowercase__   					=			rename_key_and_reshape_tensor(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

						if flax_key in random_flax_state_dict:
									if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
												raise ValueError(
												    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
												    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''					)

        # also add unexpected weight so that warning is thrown
						lowercase__   					=			jnp.asarray(SCREAMING_SNAKE_CASE_					)

			return unflatten_dict(SCREAMING_SNAKE_CASE_					)




