






'''simple docstring'''




import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)

def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase       )       ->						Any:

      '''simple docstring'''


      if isinstance(__lowercase   ,  torch.Tensor       ):
            return image
      elif isinstance(__lowercase   ,  PIL.Image.Image       ):
            _A			     =	[image]

      if isinstance(image[0]   ,  PIL.Image.Image       ):
            _A			     =	[np.array(i.resize((w, h)   ,  resample=PIL_INTERPOLATION["lanczos"]       )       )[None, :] for i in image]
            _A			     =	np.concatenate(__lowercase   ,  axis=0       )
            _A			     =	np.array(__lowercase       ).astype(np.floataa       ) / 255.0
            _A			     =	image.transpose(0   ,  3   ,  1   ,  2       )
            _A			     =	2.0 * image - 1.0
            _A			     =	torch.from_numpy(__lowercase       )
      elif isinstance(image[0]   ,  torch.Tensor       ):
            _A			     =	torch.cat(__lowercase   ,  dim=0       )
      return image

def 					__lowercase						(			__lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase=0.9995       )       ->						Any:

      '''simple docstring'''


      if not isinstance(__lowercase   ,  np.ndarray       ):
            _A			     =	True
            _A			     =	va.device
            _A			     =	va.cpu().numpy()
            _A			     =	va.cpu().numpy()

      _A			     =	np.sum(va * va / (np.linalg.norm(__lowercase       ) * np.linalg.norm(__lowercase       ))       )
      if np.abs(__lowercase       ) > DOT_THRESHOLD:
            _A			     =	(1 - t) * va + t * va
      else:
            _A			     =	np.arccos(__lowercase       )
            _A			     =	np.sin(__lowercase       )
            _A			     =	theta_a * t
            _A			     =	np.sin(__lowercase       )
            _A			     =	np.sin(theta_a - theta_t       ) / sin_theta_a
            _A			     =	sin_theta_t / sin_theta_a
            _A			     =	sa * va + sa * va

      if inputs_are_torch:
            _A			     =	torch.from_numpy(__lowercase       ).to(__lowercase       )

      return va

def 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						Optional[int]:

      '''simple docstring'''


      _A			     =	F.normalize(__lowercase   ,  dim=-1       )
      _A			     =	F.normalize(__lowercase   ,  dim=-1       )
      return (x - y).norm(dim=-1       ).div(2       ).arcsin().pow(2       ).mul(2       )

def 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						List[Any]:

      '''simple docstring'''


      for param in model.parameters():
            _A			     =	value


class    _UpperCAmelCase			(     snake_case_						):




   """simple docstring"""

   def __init__(		self :	int      ,      __UpperCAmelCase :	AutoencoderKL      ,      __UpperCAmelCase :	CLIPTextModel      ,      __UpperCAmelCase :	CLIPModel      ,      __UpperCAmelCase :	CLIPTokenizer      ,      __UpperCAmelCase :	UNetaDConditionModel      ,      __UpperCAmelCase :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]      ,      __UpperCAmelCase :	CLIPFeatureExtractor      ,      __UpperCAmelCase :	Optional[Any]=None      ,      __UpperCAmelCase :	List[Any]=None      ,      __UpperCAmelCase :	Tuple=None      ,      ):





         '''simple docstring'''





         super().__init__()
         self.register_modules(
             vae=__UpperCAmelCase      ,      text_encoder=__UpperCAmelCase      ,      clip_model=__UpperCAmelCase      ,      tokenizer=__UpperCAmelCase      ,      unet=__UpperCAmelCase      ,      scheduler=__UpperCAmelCase      ,      feature_extractor=__UpperCAmelCase      ,      coca_model=__UpperCAmelCase      ,      coca_tokenizer=__UpperCAmelCase      ,      coca_transform=__UpperCAmelCase      ,      )
         _A			     =	(
             feature_extractor.size
             if isinstance(feature_extractor.size      ,      __UpperCAmelCase		)
             else feature_extractor.size["shortest_edge"]
         )
         _A			     =	transforms.Normalize(mean=feature_extractor.image_mean      ,      std=feature_extractor.image_std		)
         set_requires_grad(self.text_encoder      ,      __UpperCAmelCase		)
         set_requires_grad(self.clip_model      ,      __UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	List[Any]      ,      __UpperCAmelCase :	Optional[Union[str, int]] = "auto"		):





         '''simple docstring'''





         if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               _A			     =	self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





         self.enable_attention_slicing(__UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	int		):





         '''simple docstring'''





         set_requires_grad(self.vae      ,      __UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	int		):





         '''simple docstring'''





         set_requires_grad(self.vae      ,      __UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         set_requires_grad(self.unet      ,      __UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	int		):





         '''simple docstring'''





         set_requires_grad(self.unet      ,      __UpperCAmelCase		)

   def 						lowerCAmelCase (		self :	Optional[int]      ,      __UpperCAmelCase :	List[Any]      ,      __UpperCAmelCase :	Optional[Any]      ,      __UpperCAmelCase :	Any		):





         '''simple docstring'''





         _A			     =	min(int(num_inference_steps * strength		)      ,      __UpperCAmelCase		)

         _A			     =	max(num_inference_steps - init_timestep      ,      0		)
         _A			     =	self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start

   def 						lowerCAmelCase (		self :	Any      ,      __UpperCAmelCase :	Union[str, Any]      ,      __UpperCAmelCase :	str      ,      __UpperCAmelCase :	Dict      ,      __UpperCAmelCase :	int      ,      __UpperCAmelCase :	int      ,      __UpperCAmelCase :	Dict=None		):





         '''simple docstring'''





         if not isinstance(__UpperCAmelCase      ,      torch.Tensor		):
               raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(__UpperCAmelCase		)}'''		)

         _A			     =	image.to(device=__UpperCAmelCase      ,      dtype=__UpperCAmelCase		)

         if isinstance(__UpperCAmelCase      ,      __UpperCAmelCase		):
               _A			     =	[
                   self.vae.encode(image[i : i + 1]		).latent_dist.sample(generator[i]		) for i in range(__UpperCAmelCase		)
               ]
               _A			     =	torch.cat(__UpperCAmelCase      ,      dim=0		)
         else:
               _A			     =	self.vae.encode(__UpperCAmelCase		).latent_dist.sample(__UpperCAmelCase		)

         # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
         _A			     =	0.18215 * init_latents
         _A			     =	init_latents.repeat_interleave(__UpperCAmelCase      ,      dim=0		)

         _A			     =	randn_tensor(init_latents.shape      ,      generator=__UpperCAmelCase      ,      device=__UpperCAmelCase      ,      dtype=__UpperCAmelCase		)

         # get latents
         _A			     =	self.scheduler.add_noise(__UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase		)
         _A			     =	init_latents

         return latents

   def 						lowerCAmelCase (		self :	List[str]      ,      __UpperCAmelCase :	Dict		):





         '''simple docstring'''





         _A			     =	self.coca_transform(__UpperCAmelCase		).unsqueeze(0		)
         with torch.no_grad(), torch.cuda.amp.autocast():
               _A			     =	self.coca_model.generate(transformed_image.to(device=self.device      ,      dtype=self.coca_model.dtype		)		)
         _A			     =	self.coca_tokenizer.decode(generated[0].cpu().numpy()		)
         return generated.split("<end_of_text>"		)[0].replace("<start_of_text>"      ,      ""		).rstrip(" .,"		)

   def 						lowerCAmelCase (		self :	Optional[int]      ,      __UpperCAmelCase :	Any      ,      __UpperCAmelCase :	int		):





         '''simple docstring'''





         _A			     =	self.feature_extractor.preprocess(__UpperCAmelCase		)
         _A			     =	torch.from_numpy(clip_image_input["pixel_values"][0]		).unsqueeze(0		).to(self.device		).half()
         _A			     =	self.clip_model.get_image_features(__UpperCAmelCase		)
         _A			     =	image_embeddings_clip / image_embeddings_clip.norm(p=2      ,      dim=-1      ,      keepdim=__UpperCAmelCase		)
         _A			     =	image_embeddings_clip.repeat_interleave(__UpperCAmelCase      ,      dim=0		)
         return image_embeddings_clip

   @torch.enable_grad()
   def 						lowerCAmelCase (		self :	int      ,      __UpperCAmelCase :	Optional[Any]      ,      __UpperCAmelCase :	int      ,      __UpperCAmelCase :	Dict      ,      __UpperCAmelCase :	Any      ,      __UpperCAmelCase :	Optional[Any]      ,      __UpperCAmelCase :	List[Any]      ,      __UpperCAmelCase :	Dict      ,      ):





         '''simple docstring'''





         _A			     =	latents.detach().requires_grad_()

         _A			     =	self.scheduler.scale_model_input(__UpperCAmelCase      ,      __UpperCAmelCase		)

         # predict the noise residual
         _A			     =	self.unet(__UpperCAmelCase      ,      __UpperCAmelCase      ,      encoder_hidden_states=__UpperCAmelCase		).sample

         if isinstance(self.scheduler      ,      (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)		):
               _A			     =	self.scheduler.alphas_cumprod[timestep]
               _A			     =	1 - alpha_prod_t
               # compute predicted original sample from predicted noise also called
               # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
               _A			     =	(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

               _A			     =	torch.sqrt(__UpperCAmelCase		)
               _A			     =	pred_original_sample * (fac) + latents * (1 - fac)
         elif isinstance(self.scheduler      ,      __UpperCAmelCase		):
               _A			     =	self.scheduler.sigmas[index]
               _A			     =	latents - sigma * noise_pred
         else:
               raise ValueError(f'''scheduler type {type(self.scheduler		)} not supported'''		)

         # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
         _A			     =	1 / 0.18215 * sample
         _A			     =	self.vae.decode(__UpperCAmelCase		).sample
         _A			     =	(image / 2 + 0.5).clamp(0      ,      1		)

         _A			     =	transforms.Resize(self.feature_extractor_size		)(__UpperCAmelCase		)
         _A			     =	self.normalize(__UpperCAmelCase		).to(latents.dtype		)

         _A			     =	self.clip_model.get_image_features(__UpperCAmelCase		)
         _A			     =	image_embeddings_clip / image_embeddings_clip.norm(p=2      ,      dim=-1      ,      keepdim=__UpperCAmelCase		)

         _A			     =	spherical_dist_loss(__UpperCAmelCase      ,      __UpperCAmelCase		).mean() * clip_guidance_scale

         _A			     =	-torch.autograd.grad(__UpperCAmelCase      ,      __UpperCAmelCase		)[0]

         if isinstance(self.scheduler      ,      __UpperCAmelCase		):
               _A			     =	latents.detach() + grads * (sigma**2)
               _A			     =	noise_pred_original
         else:
               _A			     =	noise_pred_original - torch.sqrt(__UpperCAmelCase		) * grads
         return noise_pred, latents

   @torch.no_grad()
   def __call__(		self :	Union[str, Any]      ,      __UpperCAmelCase :	Union[torch.FloatTensor, PIL.Image.Image]      ,      __UpperCAmelCase :	Union[torch.FloatTensor, PIL.Image.Image]      ,      __UpperCAmelCase :	Optional[str] = None      ,      __UpperCAmelCase :	Optional[str] = None      ,      __UpperCAmelCase :	Optional[int] = 512      ,      __UpperCAmelCase :	Optional[int] = 512      ,      __UpperCAmelCase :	float = 0.6      ,      __UpperCAmelCase :	Optional[int] = 50      ,      __UpperCAmelCase :	Optional[float] = 7.5      ,      __UpperCAmelCase :	Optional[int] = 1      ,      __UpperCAmelCase :	float = 0.0      ,      __UpperCAmelCase :	Optional[float] = 100      ,      __UpperCAmelCase :	Optional[torch.Generator] = None      ,      __UpperCAmelCase :	Optional[str] = "pil"      ,      __UpperCAmelCase :	bool = True      ,      __UpperCAmelCase :	float = 0.8      ,      __UpperCAmelCase :	float = 0.1      ,      __UpperCAmelCase :	float = 0.1      ,      ):





         '''simple docstring'''





         if isinstance(__UpperCAmelCase      ,      __UpperCAmelCase		) and len(__UpperCAmelCase		) != batch_size:
               raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(__UpperCAmelCase		)} generators.'''		)

         if height % 8 != 0 or width % 8 != 0:
               raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''		)

         if isinstance(__UpperCAmelCase      ,      torch.Generator		) and batch_size > 1:
               _A			     =	[generator] + [None] * (batch_size - 1)

         _A			     =	[
             ("model", self.coca_model is None),
             ("tokenizer", self.coca_tokenizer is None),
             ("transform", self.coca_transform is None),
         ]
         _A			     =	[x[0] for x in coca_is_none if x[1]]
         _A			     =	", ".join(__UpperCAmelCase		)
         # generate prompts with coca model if prompt is None
         if content_prompt is None:
               if len(__UpperCAmelCase		):
                     raise ValueError(
                         f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
                         f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''		)
               _A			     =	self.get_image_description(__UpperCAmelCase		)
         if style_prompt is None:
               if len(__UpperCAmelCase		):
                     raise ValueError(
                         f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
                         f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''		)
               _A			     =	self.get_image_description(__UpperCAmelCase		)

         # get prompt text embeddings for content and style
         _A			     =	self.tokenizer(
             __UpperCAmelCase      ,      padding="max_length"      ,      max_length=self.tokenizer.model_max_length      ,      truncation=__UpperCAmelCase      ,      return_tensors="pt"      ,      )
         _A			     =	self.text_encoder(content_text_input.input_ids.to(self.device		)		)[0]

         _A			     =	self.tokenizer(
             __UpperCAmelCase      ,      padding="max_length"      ,      max_length=self.tokenizer.model_max_length      ,      truncation=__UpperCAmelCase      ,      return_tensors="pt"      ,      )
         _A			     =	self.text_encoder(style_text_input.input_ids.to(self.device		)		)[0]

         _A			     =	slerp(__UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase		)

         # duplicate text embeddings for each generation per prompt
         _A			     =	text_embeddings.repeat_interleave(__UpperCAmelCase      ,      dim=0		)

         # set timesteps
         _A			     =	"offset" in set(inspect.signature(self.scheduler.set_timesteps		).parameters.keys()		)
         _A			     =	{}
         if accepts_offset:
               _A			     =	1

         self.scheduler.set_timesteps(__UpperCAmelCase      ,      **__UpperCAmelCase		)
         # Some schedulers like PNDM have timesteps as arrays
         # It's more optimized to move all timesteps to correct device beforehand
         self.scheduler.timesteps.to(self.device		)

         _A     ,	_A			     =	self.get_timesteps(__UpperCAmelCase      ,      __UpperCAmelCase      ,      self.device		)
         _A			     =	timesteps[:1].repeat(__UpperCAmelCase		)

         # Preprocess image
         _A			     =	preprocess(__UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase		)
         _A			     =	self.prepare_latents(
             __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      text_embeddings.dtype      ,      self.device      ,      __UpperCAmelCase		)

         _A			     =	preprocess(__UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase		)
         _A			     =	self.prepare_latents(
             __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      text_embeddings.dtype      ,      self.device      ,      __UpperCAmelCase		)

         _A			     =	slerp(__UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase		)

         if clip_guidance_scale > 0:
               _A			     =	self.get_clip_image_embeddings(__UpperCAmelCase      ,      __UpperCAmelCase		)
               _A			     =	self.get_clip_image_embeddings(__UpperCAmelCase      ,      __UpperCAmelCase		)
               _A			     =	slerp(
                   __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase		)

         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         _A			     =	guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
               _A			     =	content_text_input.input_ids.shape[-1]
               _A			     =	self.tokenizer([""]      ,      padding="max_length"      ,      max_length=__UpperCAmelCase      ,      return_tensors="pt"		)
               _A			     =	self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]
               # duplicate unconditional embeddings for each generation per prompt
               _A			     =	uncond_embeddings.repeat_interleave(__UpperCAmelCase      ,      dim=0		)

               # For classifier free guidance, we need to do two forward passes.
               # Here we concatenate the unconditional and text embeddings into a single batch
               # to avoid doing two forward passes
               _A			     =	torch.cat([uncond_embeddings, text_embeddings]		)

         # get the initial random noise unless the user supplied it

         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
         _A			     =	(batch_size, self.unet.config.in_channels, height // 8, width // 8)
         _A			     =	text_embeddings.dtype
         if latents is None:
               if self.device.type == "mps":
                     # randn does not work reproducibly on mps
                     _A			     =	torch.randn(__UpperCAmelCase      ,      generator=__UpperCAmelCase      ,      device="cpu"      ,      dtype=__UpperCAmelCase		).to(
                         self.device		)
               else:
                     _A			     =	torch.randn(__UpperCAmelCase      ,      generator=__UpperCAmelCase      ,      device=self.device      ,      dtype=__UpperCAmelCase		)
         else:
               if latents.shape != latents_shape:
                     raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''		)
               _A			     =	latents.to(self.device		)

         # scale the initial noise by the standard deviation required by the scheduler
         _A			     =	latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         _A			     =	"eta" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
         _A			     =	{}
         if accepts_eta:
               _A			     =	eta

         # check if the scheduler accepts generator
         _A			     =	"generator" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
         if accepts_generator:
               _A			     =	generator

         with self.progress_bar(total=__UpperCAmelCase		):
               for i, t in enumerate(__UpperCAmelCase		):
                     # expand the latents if we are doing classifier free guidance
                     _A			     =	torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
                     _A			     =	self.scheduler.scale_model_input(__UpperCAmelCase      ,      __UpperCAmelCase		)

                     # predict the noise residual
                     _A			     =	self.unet(__UpperCAmelCase      ,      __UpperCAmelCase      ,      encoder_hidden_states=__UpperCAmelCase		).sample

                     # perform classifier free guidance
                     if do_classifier_free_guidance:
                           _A     ,	_A			     =	noise_pred.chunk(2		)
                           _A			     =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                     # perform clip guidance
                     if clip_guidance_scale > 0:
                           _A			     =	(
                               text_embeddings.chunk(2		)[1] if do_classifier_free_guidance else text_embeddings
                           )
                           _A     ,	_A			     =	self.cond_fn(
                               __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      )

                     # compute the previous noisy sample x_t -> x_t-1
                     _A			     =	self.scheduler.step(__UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      **__UpperCAmelCase		).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
         _A			     =	1 / 0.18215 * latents
         _A			     =	self.vae.decode(__UpperCAmelCase		).sample

         _A			     =	(image / 2 + 0.5).clamp(0      ,      1		)
         _A			     =	image.cpu().permute(0      ,      2      ,      3      ,      1		).numpy()

         if output_type == "pil":
               _A			     =	self.numpy_to_pil(__UpperCAmelCase		)

         if not return_dict:
               return (image, None)

         return StableDiffusionPipelineOutput(images=__UpperCAmelCase      ,      nsfw_content_detected=__UpperCAmelCase		)










'''simple docstring'''





# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class __A			:



 '''simple docstring'''







 __lowerCamelCase    :       CommonSchedulerState

 # setable values
 __lowerCamelCase    :       jnp.ndarray
 __lowerCamelCase    :       jnp.ndarray
 __lowerCamelCase    :       Optional[int]          =       None



 @classmethod
 def 					a__  (cls ,   A ,   A ,   A       )    ->     str:


      """simple docstring"""



      return cls(common=A ,   init_noise_sigma=A ,   timesteps=A       )






@dataclass
class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       DDPMSchedulerState






class __A			(  A   ,   A						):



 '''simple docstring'''







 __lowerCamelCase    :       Dict          =       [e.name for e in FlaxKarrasDiffusionSchedulers]

 __lowerCamelCase    :       jnp.dtype



 @property
 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      return True



 @register_to_config
 def __init__(self ,   A = 1_000 ,   A = 0.0001 ,   A = 0.02 ,   A = "linear" ,   A = None ,   A = "fixed_small" ,   A = True ,   A = "epsilon" ,   A = jnp.floataa ,   )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  dtype



 def 					a__  (self ,   A = None       )    ->     DDPMSchedulerState:


      """simple docstring"""



      if common is None:
           _a      =  CommonSchedulerState.create(self       )

      # standard deviation of the initial noise distribution
      _a      =  jnp.array(1.0 ,   dtype=self.dtype       )

      _a      =  jnp.arange(0 ,   self.config.num_train_timesteps       ).round()[::-1]

      return DDPMSchedulerState.create(
          common=A ,   init_noise_sigma=A ,   timesteps=A ,   )



 def 					a__  (self ,   A ,   A ,   A = None       )    ->     jnp.ndarray:


      """simple docstring"""



      return sample



 def 					a__  (self ,   A ,   A ,   A = ()       )    ->     DDPMSchedulerState:


      """simple docstring"""



      _a      =  self.config.num_train_timesteps // num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # rounding to avoid issues when num_inference_step is power of 3
      _a      =  (jnp.arange(0 ,   A       ) * step_ratio).round()[::-1]

      return state.replace(
          num_inference_steps=A ,   timesteps=A ,   )



 def 					a__  (self ,   A ,   A ,   A=None ,   A=None       )    ->     int:


      """simple docstring"""



      _a      =  state.common.alphas_cumprod[t]
      _a      =  jnp.where(t > 0 ,   state.common.alphas_cumprod[t - 1] ,   jnp.array(1.0 ,   dtype=self.dtype       )       )

      # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
      # and sample from it to get previous sample
      # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
      _a      =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

      if variance_type is None:
           _a      =  self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small":
           _a      =  jnp.clip(A ,   a_min=1E-20       )
      # for rl-diffuser https://arxiv.org/abs/2205.09991
      elif variance_type == "fixed_small_log":
           _a      =  jnp.log(jnp.clip(A ,   a_min=1E-20       )       )
      elif variance_type == "fixed_large":
           _a      =  state.common.betas[t]
      elif variance_type == "fixed_large_log":
           # Glide max_log
           _a      =  jnp.log(state.common.betas[t]       )
      elif variance_type == "learned":
           return predicted_variance
      elif variance_type == "learned_range":
           _a      =  variance
           _a      =  state.common.betas[t]
           _a      =  (predicted_variance + 1) / 2
           _a      =  frac * max_log + (1 - frac) * min_log

      return variance



 def 					a__  (self ,   A ,   A ,   A ,   A ,   A = None ,   A = True ,   )    ->     Union[FlaxDDPMSchedulerOutput, Tuple]:


      """simple docstring"""



      _a      =  timestep

      if key is None:
           _a      =  jax.random.PRNGKey(0       )

      if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
           _a  ,    _a      =  jnp.split(A ,   sample.shape[1] ,   axis=1       )
      else:
           _a      =  None

      # 1. compute alphas, betas
      _a      =  state.common.alphas_cumprod[t]
      _a      =  jnp.where(t > 0 ,   state.common.alphas_cumprod[t - 1] ,   jnp.array(1.0 ,   dtype=self.dtype       )       )
      _a      =  1 - alpha_prod_t
      _a      =  1 - alpha_prod_t_prev

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if self.config.prediction_type == "epsilon":
           _a      =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
           _a      =  model_output
      elif self.config.prediction_type == "v_prediction":
           _a      =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
      else:
           raise ValueError(
               f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
               ''' for the FlaxDDPMScheduler.'''       )

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
           _a      =  jnp.clip(A ,   -1 ,   1       )

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      _a      =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
      _a      =  state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      _a      =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      def random_variance():
           _a      =  jax.random.split(A ,   num=1       )
           _a      =  jax.random.normal(A ,   shape=model_output.shape ,   dtype=self.dtype       )
           return (self._get_variance(A ,   A ,   predicted_variance=A       ) ** 0.5) * noise

      _a      =  jnp.where(t > 0 ,   random_variance() ,   jnp.zeros(model_output.shape ,   dtype=self.dtype       )       )

      _a      =  pred_prev_sample + variance

      if not return_dict:
           return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=A ,   state=A       )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   )    ->     jnp.ndarray:


      """simple docstring"""



      return add_noise_common(state.common ,   A ,   A ,   A       )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   )    ->     jnp.ndarray:


      """simple docstring"""



      return get_velocity_common(state.common ,   A ,   A ,   A       )



 def __len__(self       )    ->     Tuple:


      """simple docstring"""



      return self.config.num_train_timesteps



