


'''simple docstring'''







import math

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






def       lowercase					(_A ,    _A ,    _A = 1 ,    _A = 1 ,    _A = 1.0E4 ,    _A = False ,    _A = 1.0 ,    ):







       """simple docstring"""







       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, f'Embedding dimension {embedding_dim} should be even'
       _lowerCAmelCase     :				Any       =							float(embedding_dim // 2       )
       _lowerCAmelCase     :				str       =							math.log(max_timescale / min_timescale       ) / (num_timescales - freq_shift)
       _lowerCAmelCase     :				Dict       =							min_timescale * jnp.exp(jnp.arange(_A ,    dtype=jnp.floataa       ) * -log_timescale_increment       )
       _lowerCAmelCase     :				Union[str, Any]       =							jnp.expand_dims(_A ,    1       ) * jnp.expand_dims(_A ,    0       )

       # scale embeddings
       _lowerCAmelCase     :				Tuple       =							scale * emb

       if flip_sin_to_cos:
              _lowerCAmelCase     :				str       =							jnp.concatenate([jnp.cos(_A       ), jnp.sin(_A       )] ,    axis=1       )
       else:
              _lowerCAmelCase     :				List[Any]       =							jnp.concatenate([jnp.sin(_A       ), jnp.cos(_A       )] ,    axis=1       )
       _lowerCAmelCase     :				Union[str, Any]       =							jnp.reshape(_A ,    [jnp.shape(_A       )[0], embedding_dim]       )
       return signal
class 						UpperCamelCase__       (			nn.Module						):



     """simple docstring"""






     __magic_name__           =     3_2
     __magic_name__           =     jnp.floataa







     @nn.compact
     def __call__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							nn.Dense(self.time_embed_dim   ,						dtype=self.dtype   ,						name='linear_1' )(snake_case__ )
            _lowerCAmelCase     :				int       =							nn.silu(snake_case__ )
            _lowerCAmelCase     :				Tuple       =							nn.Dense(self.time_embed_dim   ,						dtype=self.dtype   ,						name='linear_2' )(snake_case__ )
            return temb
class 						UpperCamelCase__       (			nn.Module						):



     """simple docstring"""






     __magic_name__           =     3_2
     __magic_name__           =     False
     __magic_name__           =     1







     @nn.compact
     def __call__(			self   ,						snake_case__ ):

            '''simple docstring'''

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





'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							value
            _lowerCAmelCase     :				Node | None       =							None
            _lowerCAmelCase     :				Node | None       =							None
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							tree







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if node is None:
                   return 0
            return node.value + (
                self.depth_first_search(node.left ) + self.depth_first_search(node.right )
            )







     def __iter__(			self ):

            '''simple docstring'''

            yield self.depth_first_search(self.tree )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

