


'''simple docstring'''
import math

import flax.linen as nn
import jax.numpy as jnp





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__ = 1  ,					lowerCAmelCase__ = 1  ,					lowerCAmelCase__ = 1.0E4  ,					lowerCAmelCase__ = False  ,					lowerCAmelCase__ = 1.0  ,					)    ->	jnp.ndarray:
    assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
    assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
    UpperCAmelCase__			:		List[str]													=				float(embedding_dim // 2						)
    UpperCAmelCase__			:		Optional[Any]													=				math.log(max_timescale / min_timescale						) / (num_timescales - freq_shift)
    UpperCAmelCase__			:		Optional[int]													=				min_timescale * jnp.exp(jnp.arange(lowerCAmelCase__  ,					dtype=jnp.floataa						) * -log_timescale_increment						)
    UpperCAmelCase__			:		Union[str, Any]													=				jnp.expand_dims(lowerCAmelCase__  ,					1						) * jnp.expand_dims(lowerCAmelCase__  ,					0						)

    # scale embeddings
    UpperCAmelCase__			:		List[Any]													=				scale * emb

    if flip_sin_to_cos:
        UpperCAmelCase__			:		Tuple													=				jnp.concatenate([jnp.cos(lowerCAmelCase__						), jnp.sin(lowerCAmelCase__						)]  ,					axis=1						)
    else:
        UpperCAmelCase__			:		str													=				jnp.concatenate([jnp.sin(lowerCAmelCase__						), jnp.cos(lowerCAmelCase__						)]  ,					axis=1						)
    UpperCAmelCase__			:		Tuple													=				jnp.reshape(lowerCAmelCase__  ,					[jnp.shape(lowerCAmelCase__						)[0], embedding_dim]						)
    return signal




class 			lowerCamelCase_   (	nn.Module      ):
      lowerCAmelCase__		     =     3_2
      lowerCAmelCase__		     =     jnp.floataa






      @nn.compact
      def __call__(					self				:    Tuple						,						_A				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				nn.Dense(self.time_embed_dim						,						dtype=self.dtype						,						name='''linear_1''' )(_A )
          UpperCAmelCase__			:		int													=				nn.silu(_A )
          UpperCAmelCase__			:		Optional[int]													=				nn.Dense(self.time_embed_dim						,						dtype=self.dtype						,						name='''linear_2''' )(_A )
          return temb




class 			lowerCamelCase_   (	nn.Module      ):
      lowerCAmelCase__		     =     3_2
      lowerCAmelCase__		     =     False
      lowerCAmelCase__		     =     1






      @nn.compact
      def __call__(					self				:    List[Any]						,						_A				:    str ):


          '''simple docstring'''


          return get_sinusoidal_embeddings(
              _A						,						embedding_dim=self.dim						,						flip_sin_to_cos=self.flip_sin_to_cos						,						freq_shift=self.freq_shift )




'''simple docstring'''
import math




class 			lowerCamelCase_   :






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    list[list[float]]						,						_A				:    list[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				0.0
          UpperCAmelCase__			:		Union[str, Any]													=				0.0
          for i in range(len(_A ) ):
              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 lowercase_							(					self				:    Dict						,						_A				:    list[list[int | float]]						,						_A				:    list[int]						,						_A				:    int						,						_A				:    float ):


          '''simple docstring'''


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





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

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

    # training
    UpperCAmelCase__			:		Optional[Any]													=				SelfOrganizingMap()
    UpperCAmelCase__			:		Tuple													=				3
    UpperCAmelCase__			:		Dict													=				0.5

    for _ in range(lowerCAmelCase__						):
        for j in range(len(lowerCAmelCase__						)						):
            # training sample
            UpperCAmelCase__			:		Optional[int]													=				training_samples[j]

            # Compute the winning vector
            UpperCAmelCase__			:		str													=				self_organizing_map.get_winner(lowerCAmelCase__  ,					lowerCAmelCase__						)

            # Update the winning vector
            UpperCAmelCase__			:		int													=				self_organizing_map.update(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

    # classify test sample
    UpperCAmelCase__			:		str													=				[0, 0, 0, 1]
    UpperCAmelCase__			:		int													=				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()
