






'''simple docstring'''


import math

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






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

     # scale embeddings
     a_       		=scale * emb

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







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 __magic_name__					: int  =      32
 __magic_name__					: jnp.dtype  =      jnp.floataa




 @nn.compact
 def __call__(   self							,  lowerCAmelCase_) ->      Any:



      """simple docstring"""







      a_       		=nn.Dense(self.time_embed_dim							,  dtype=self.dtype							,  name="linear_1")(lowercase__)
      a_       		=nn.silu(lowercase__)
      a_       		=nn.Dense(self.time_embed_dim							,  dtype=self.dtype							,  name="linear_2")(lowercase__)
      return temb







class 	UpperCAmelCase	(		nn.Module):


 '''simple docstring'''




 __magic_name__					: int  =      32
 __magic_name__					: bool  =      False
 __magic_name__					: float  =      1




 @nn.compact
 def __call__(   self							,  lowerCAmelCase_) ->      Optional[int]:



      """simple docstring"""







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







'''simple docstring'''


import torch

from diffusers import StableDiffusionPipeline


lowercase							       =			'''path-to-your-trained-model'''
lowercase							       =			StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowercase							       =			'''A photo of sks dog in a bucket'''
lowercase							       =			pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')


