



'''simple docstring'''






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 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       42
	lowerCamelCase__			 =       42







	def __init__(						self			:     Union[str, Any]		,			__snake_case			:     UNetaDModel		,			__snake_case			:     ScoreSdeVeScheduler		)			->		int:
								super().__init__()
								self.register_modules(unet=__snake_case		,			scheduler=__snake_case		)







	@torch.no_grad()
	def __call__(						self			:     Optional[int]		,			__snake_case			:     int = 1		,			__snake_case			:     int = 2000		,			__snake_case			:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			__snake_case			:     Optional[str] = "pil"		,			__snake_case			:     bool = True		,			**__snake_case			:     Optional[int]		,			)			->		Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase :       str 							=		self.unet.config.sample_size
								UpperCAmelCase :       Union[str, Any] 							=		(batch_size, 3, img_size, img_size)

								UpperCAmelCase :       int 							=		self.unet

								UpperCAmelCase :       Any 							=		randn_tensor(__snake_case		,			generator=__snake_case		) * self.scheduler.init_noise_sigma
								UpperCAmelCase :       List[Any] 							=		sample.to(self.device		)

								self.scheduler.set_timesteps(__snake_case		)
								self.scheduler.set_sigmas(__snake_case		)

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

															# correction step
															for _ in range(self.scheduler.config.correct_steps		):
																						UpperCAmelCase :       Union[str, Any] 							=		self.unet(__snake_case		,			__snake_case		).sample
																						UpperCAmelCase :       Optional[Any] 							=		self.scheduler.step_correct(__snake_case		,			__snake_case		,			generator=__snake_case		).prev_sample

															# prediction step
															UpperCAmelCase :       Optional[Any] 							=		model(__snake_case		,			__snake_case		).sample
															UpperCAmelCase :       List[str] 							=		self.scheduler.step_pred(__snake_case		,			__snake_case		,			__snake_case		,			generator=__snake_case		)

															UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		output.prev_sample, output.prev_sample_mean

								UpperCAmelCase :       int 							=		sample_mean.clamp(0		,			1		)
								UpperCAmelCase :       Union[str, Any] 							=		sample.cpu().permute(0		,			2		,			3		,			1		).numpy()
								if output_type == "pil":
															UpperCAmelCase :       Optional[Any] 							=		self.numpy_to_pil(__snake_case		)

								if not return_dict:
															return (sample,)

								return ImagePipelineOutput(images=__snake_case		)












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

class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      Any	)			->	Any:
     lowerCamelCase__    :      str					 =		nn.Conv(
         self.out_channels			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )







 def __call__(      self  :      int			,     UpperCAmelCase  :      Dict	)			->	Optional[Any]:
     lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		hidden_states.shape
     lowerCamelCase__    :      Union[str, Any]					 =		jax.image.resize(
         UpperCAmelCase			,     shape=(batch, height * 2, width * 2, channels)			,     method='nearest'			,     )
     lowerCamelCase__    :      Optional[Any]					 =		self.conv(UpperCAmelCase	)
     return hidden_states


class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      List[str]	)			->	int:
     lowerCamelCase__    :      Tuple					 =		nn.Conv(
         self.out_channels			,     kernel_size=(3, 3)			,     strides=(2, 2)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )







 def __call__(      self  :      str			,     UpperCAmelCase  :      Union[str, Any]	)			->	Optional[Any]:
     # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
     # hidden_states = jnp.pad(hidden_states, pad_width=pad)
     lowerCamelCase__    :      Optional[Any]					 =		self.conv(UpperCAmelCase	)
     return hidden_states


class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		0.0
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      List[str]	)			->	Union[str, Any]:
     lowerCamelCase__    :      Optional[Any]					 =		self.in_channels if self.out_channels is None else self.out_channels

     lowerCamelCase__    :      Tuple					 =		nn.GroupNorm(num_groups=32			,     epsilon=1e-5	)
     lowerCamelCase__    :      int					 =		nn.Conv(
         UpperCAmelCase			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )

     lowerCamelCase__    :      Union[str, Any]					 =		nn.Dense(UpperCAmelCase			,     dtype=self.dtype	)

     lowerCamelCase__    :      Union[str, Any]					 =		nn.GroupNorm(num_groups=32			,     epsilon=1e-5	)
     lowerCamelCase__    :      List[Any]					 =		nn.Dropout(self.dropout_prob	)
     lowerCamelCase__    :      Tuple					 =		nn.Conv(
         UpperCAmelCase			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )

     lowerCamelCase__    :      Optional[Any]					 =		self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

     lowerCamelCase__    :      Union[str, Any]					 =		None
     if use_nin_shortcut:
         lowerCamelCase__    :      Dict					 =		nn.Conv(
             UpperCAmelCase			,     kernel_size=(1, 1)			,     strides=(1, 1)			,     padding='VALID'			,     dtype=self.dtype			,     )







 def __call__(      self  :      Optional[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      int=True	)			->	Optional[int]:
     lowerCamelCase__    :      Union[str, Any]					 =		hidden_states
     lowerCamelCase__    :      List[Any]					 =		self.norma(UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		nn.swish(UpperCAmelCase	)
     lowerCamelCase__    :      Any					 =		self.conva(UpperCAmelCase	)

     lowerCamelCase__    :      Optional[Any]					 =		self.time_emb_proj(nn.swish(UpperCAmelCase	)	)
     lowerCamelCase__    :      List[str]					 =		jnp.expand_dims(jnp.expand_dims(UpperCAmelCase			,     1	)			,     1	)
     lowerCamelCase__    :      List[str]					 =		hidden_states + temb

     lowerCamelCase__    :      Optional[Any]					 =		self.norma(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		nn.swish(UpperCAmelCase	)
     lowerCamelCase__    :      Optional[int]					 =		self.dropout(UpperCAmelCase			,     UpperCAmelCase	)
     lowerCamelCase__    :      str					 =		self.conva(UpperCAmelCase	)

     if self.conv_shortcut is not None:
         lowerCamelCase__    :      Dict					 =		self.conv_shortcut(UpperCAmelCase	)

     return hidden_states + residual





