
"""simple docstring"""
import math

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




def      __lowerCamelCase	(		UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__ = 1					,  UpperCamelCase__ = 1					,  UpperCamelCase__ = 1.0E4					,  UpperCamelCase__ = False					,  UpperCamelCase__ = 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"
	_UpperCAmelCase			=    float(embedding_dim // 2   )
	_UpperCAmelCase			=    math.log(max_timescale / min_timescale   ) / (num_timescales - freq_shift)
	_UpperCAmelCase			=    min_timescale * jnp.exp(jnp.arange(UpperCamelCase__					,  dtype=jnp.floataa   ) * -log_timescale_increment   )
	_UpperCAmelCase			=    jnp.expand_dims(UpperCamelCase__					,  1   ) * jnp.expand_dims(UpperCamelCase__					,  0   )

	# scale embeddings
	_UpperCAmelCase			=    scale * emb

	if flip_sin_to_cos:
		_UpperCAmelCase			=    jnp.concatenate([jnp.cos(UpperCamelCase__   ), jnp.sin(UpperCamelCase__   )]					,  axis=1   )
	else:
		_UpperCAmelCase			=    jnp.concatenate([jnp.sin(UpperCamelCase__   ), jnp.cos(UpperCamelCase__   )]					,  axis=1   )
	_UpperCAmelCase			=    jnp.reshape(UpperCamelCase__					,  [jnp.shape(UpperCamelCase__   )[0], embedding_dim]   )
	return signal


class  _lowerCAmelCase    (    nn.Module    ):
							lowercase_  :  Dict      =				32
							lowercase_  :  Any      =				jnp.floataa



							@nn.compact
							def __call__(   self			,    a_ )				->				str:
								_UpperCAmelCase			=    nn.Dense(self.time_embed_dim			,    dtype=self.dtype			,    name="linear_1" )(a_ )
								_UpperCAmelCase			=    nn.silu(a_ )
								_UpperCAmelCase			=    nn.Dense(self.time_embed_dim			,    dtype=self.dtype			,    name="linear_2" )(a_ )
								return temb





class  _lowerCAmelCase    (    nn.Module    ):
							lowercase_  :  Dict      =				32
							lowercase_  :  List[Any]      =				False
							lowercase_  :  str      =				1



							@nn.compact
							def __call__(   self			,    a_ )				->				Any:
								return get_sinusoidal_embeddings(
								    a_			,    embedding_dim=self.dim			,    flip_sin_to_cos=self.flip_sin_to_cos			,    freq_shift=self.freq_shift )










from __future__ import annotations

from typing import Any

def lowercase    (  SCREAMING_SNAKE_CASE )			->							None:


			'''simple docstring'''







			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    []       ,    0 )

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							None:


			'''simple docstring'''







			if index == len(SCREAMING_SNAKE_CASE ):
						print(SCREAMING_SNAKE_CASE )
						return

			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1 )
			current_subsequence.append(sequence[index] )
			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1 )
			current_subsequence.pop()


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     list[Any]		    =       [3, 1, 2, 4]
				generate_all_subsequences(seq)

				seq.clear()
				seq.extend(["A", "B", "C"])
				generate_all_subsequences(seq)
