


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					42
					A__     =					42

					def __init__(self       :		Optional[Any] ,   _UpperCAmelCase       :		UNetaDModel ,   _UpperCAmelCase       :		ScoreSdeVeScheduler						)		->  Union[str, Any]:




							"""simple docstring"""


							super().__init__()
							self.register_modules(unet=_UpperCAmelCase ,   scheduler=_UpperCAmelCase						)


					@torch.no_grad()
					def __call__(self       :		List[Any] ,   _UpperCAmelCase       :		int = 1 ,   _UpperCAmelCase       :		int = 2000 ,   _UpperCAmelCase       :		Optional[Union[torch.Generator, List[torch.Generator]]] = None ,   _UpperCAmelCase       :		Optional[str] = "pil" ,   _UpperCAmelCase       :		bool = True ,   **_UpperCAmelCase       :		Any ,   )		->  Union[ImagePipelineOutput, Tuple]:




							"""simple docstring"""


							lowercase__	     =						self.unet.config.sample_size
							lowercase__	     =						(batch_size, 3, img_size, img_size)

							lowercase__	     =						self.unet

							lowercase__	     =						randn_tensor(_UpperCAmelCase ,   generator=_UpperCAmelCase						) * self.scheduler.init_noise_sigma
							lowercase__	     =						sample.to(self.device						)

							self.scheduler.set_timesteps(_UpperCAmelCase						)
							self.scheduler.set_sigmas(_UpperCAmelCase						)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
									lowercase__	     =						self.scheduler.sigmas[i] * torch.ones(shape[0] ,   device=self.device						)

									# correction step
									for _ in range(self.scheduler.config.correct_steps						):
											lowercase__	     =						self.unet(_UpperCAmelCase ,   _UpperCAmelCase						).sample
											lowercase__	     =						self.scheduler.step_correct(_UpperCAmelCase ,   _UpperCAmelCase ,   generator=_UpperCAmelCase						).prev_sample

									# prediction step
									lowercase__	     =						model(_UpperCAmelCase ,   _UpperCAmelCase						).sample
									lowercase__	     =						self.scheduler.step_pred(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   generator=_UpperCAmelCase						)

									lowercase__     ,      lowercase__	     =						output.prev_sample, output.prev_sample_mean

							lowercase__	     =						sample_mean.clamp(0 ,   1						)
							lowercase__	     =						sample.cpu().permute(0 ,   2 ,   3 ,   1						).numpy()
							if output_type == "pil":
									lowercase__	     =						self.numpy_to_pil(_UpperCAmelCase						)

							if not return_dict:
									return (sample,)

							return ImagePipelineOutput(images=_UpperCAmelCase						)



"""simple docstring"""






import math

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




def       A_  (				__lowercase						,	__lowercase						,	__lowercase = 1						,	__lowercase = 1						,	__lowercase = 1.0e4						,	__lowercase = False						,	__lowercase = 1.0						,	):
 assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
 assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''
 UpperCamelCase_     :					Optional[int]   	=float(embedding_dim // 2	)
 UpperCamelCase_     :					Optional[Any]   	=math.log(max_timescale / min_timescale	) / (num_timescales - freq_shift)
 UpperCamelCase_     :					List[Any]   	=min_timescale * jnp.exp(jnp.arange(__lowercase						,	dtype=jnp.floataa	) * -log_timescale_increment	)
 UpperCamelCase_     :					int   	=jnp.expand_dims(__lowercase						,	1	) * jnp.expand_dims(__lowercase						,	0	)

 # scale embeddings
 UpperCamelCase_     :					List[str]   	=scale * emb

 if flip_sin_to_cos:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.cos(__lowercase	), jnp.sin(__lowercase	)]						,	axis=1	)
 else:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.sin(__lowercase	), jnp.cos(__lowercase	)]						,	axis=1	)
 UpperCamelCase_     :					List[Any]   	=jnp.reshape(__lowercase						,	[jnp.shape(__lowercase	)[0], embedding_dim]	)
 return signal





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      jnp.floataa



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







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_1'   )(_lowerCamelCase   )
      UpperCamelCase_     :					Any   	=nn.silu(_lowerCamelCase   )
      UpperCamelCase_     :					Tuple   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_2'   )(_lowerCamelCase   )
      return temb





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      False
     UpperCAmelCase__         =      1



     @nn.compact
     def __call__(							self :Union[str, Any]      ,  _lowerCamelCase :Dict   ):







      '''simple docstring'''



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