

import math

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






def    SCREAMING_SNAKE_CASE__       (  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"
	__lowerCamelCase				:				Optional[int]					       = float(embedding_dim // 2			)
	__lowerCamelCase				:				Optional[Any]					       = math.log(max_timescale / min_timescale			) / (num_timescales - freq_shift)
	__lowerCamelCase				:				Tuple					       = min_timescale * jnp.exp(jnp.arange(lowerCamelCase__	, dtype=jnp.floataa			) * -log_timescale_increment			)
	__lowerCamelCase				:				List[str]					       = jnp.expand_dims(lowerCamelCase__	, 1			) * jnp.expand_dims(lowerCamelCase__	, 0			)

	# scale embeddings
	__lowerCamelCase				:				Dict					       = scale * emb

	if flip_sin_to_cos:
		__lowerCamelCase				:				List[Any]					       = jnp.concatenate([jnp.cos(lowerCamelCase__			), jnp.sin(lowerCamelCase__			)]	, axis=1			)
	else:
		__lowerCamelCase				:				Dict					       = jnp.concatenate([jnp.sin(lowerCamelCase__			), jnp.cos(lowerCamelCase__			)]	, axis=1			)
	__lowerCamelCase				:				Union[str, Any]					       = jnp.reshape(lowerCamelCase__	, [jnp.shape(lowerCamelCase__			)[0], embedding_dim]			)
	return signal






class        A_		(       nn.Module				):
							_UpperCAmelCase    :  int						      =      32
							_UpperCAmelCase    :  jnp.dtype						      =      jnp.floataa
							@nn.compact
							def __call__(		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			List[Any]):
								__lowerCamelCase				:				str					       = nn.Dense(self.time_embed_dim   ,dtype=self.dtype   ,name='linear_1')(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Any					       = nn.silu(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Union[str, Any]					       = nn.Dense(self.time_embed_dim   ,dtype=self.dtype   ,name='linear_2')(SCREAMING_SNAKE_CASE__)
								return temb






class        A_		(       nn.Module				):
							_UpperCAmelCase    :  int						      =      32
							_UpperCAmelCase    :  bool						      =      False
							_UpperCAmelCase    :  float						      =      1
							@nn.compact
							def __call__(		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			List[str]):
								return get_sinusoidal_embeddings(
								    SCREAMING_SNAKE_CASE__   ,embedding_dim=self.dim   ,flip_sin_to_cos=self.flip_sin_to_cos   ,freq_shift=self.freq_shift)







def 		_lowercase (     lowercase__   ,	lowercase__						):
      if a < 0 or b < 0:
            raise ValueError('''the value of both inputs must be positive'''						)
      __lowerCAmelCase					:						int					=					str(bin(lowercase__						)						)[2:]  # remove the leading "0b"
      __lowerCAmelCase					:						Any					=					str(bin(lowercase__						)						)[2:]
      __lowerCAmelCase					:						List[str]					=					max(len(lowercase__						)   ,	len(lowercase__						)						)
      return "0b" + "".join(
          str(int('''1''' in (char_a, char_b)						)						)
          for char_a, char_b in zip(a_binary.zfill(lowercase__						)   ,	b_binary.zfill(lowercase__						)						)						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


