



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 	_lowerCAmelCase						(				__lowerCamelCase   :     str		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     List[Any]     ):



 """simple docstring"""





 if isinstance(_lowerCamelCase		,	torch.Tensor     ):
  return image
 elif isinstance(_lowerCamelCase		,	PIL.Image.Image     ):
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[image]

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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Tuple		,	__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     List[str]=0.9995     ):



 """simple docstring"""





 if not isinstance(_lowerCamelCase		,	np.ndarray     ):
  __SCREAMING_SNAKE_CASE       :    Any       =	True
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	va.device
  __SCREAMING_SNAKE_CASE       :    List[str]       =	va.cpu().numpy()
  __SCREAMING_SNAKE_CASE       :    List[str]       =	va.cpu().numpy()

 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	np.sum(va * va / (np.linalg.norm(_lowerCamelCase     ) * np.linalg.norm(_lowerCamelCase     ))     )
 if np.abs(_lowerCamelCase     ) > DOT_THRESHOLD:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	(1 - t) * va + t * va
 else:
  __SCREAMING_SNAKE_CASE       :    int       =	np.arccos(_lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    List[str]       =	np.sin(_lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    Any       =	theta_a * t
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	np.sin(_lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	np.sin(theta_a - theta_t     ) / sin_theta_a
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	sin_theta_t / sin_theta_a
  __SCREAMING_SNAKE_CASE       :    str       =	sa * va + sa * va

 if inputs_are_torch:
  __SCREAMING_SNAKE_CASE       :    str       =	torch.from_numpy(_lowerCamelCase     ).to(_lowerCamelCase     )

 return va






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str		,	__lowerCamelCase   :     Any     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Tuple       =	F.normalize(_lowerCamelCase		,	dim=-1     )
 __SCREAMING_SNAKE_CASE       :    Any       =	F.normalize(_lowerCamelCase		,	dim=-1     )
 return (x - y).norm(dim=-1     ).div(2     ).arcsin().pow(2     ).mul(2     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     Any     ):



 """simple docstring"""





 for param in model.parameters():
  __SCREAMING_SNAKE_CASE       :    int       =	value






class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):





     def __init__(       self :							Tuple     ,  UpperCamelCase :							int     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							List[str]     ,  UpperCamelCase :							List[str]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							str     ,  UpperCamelCase :							Union[str, Any]     ,  UpperCamelCase :							Any=None     ,  UpperCamelCase :							Optional[Any]=None     ,  UpperCamelCase :							Union[str, Any]=None     ,  )->Dict:
      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__     ,  )
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	(
          feature_extractor.size
          if isinstance(feature_extractor.size     ,  UpperCamelCase__				)
          else feature_extractor.size["shortest_edge"]
      )
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	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 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							List[Any] = "auto"				)->List[str]:





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





     def 	__snake_case    (       self :							Tuple				)->str:
      self.enable_attention_slicing(UpperCamelCase__				)





     def 	__snake_case    (       self :							Any				)->Union[str, Any]:
      set_requires_grad(self.vae     ,  UpperCamelCase__				)





     def 	__snake_case    (       self :							List[Any]				)->List[Any]:
      set_requires_grad(self.vae     ,  UpperCamelCase__				)





     def 	__snake_case    (       self :							Dict				)->Optional[int]:
      set_requires_grad(self.unet     ,  UpperCamelCase__				)





     def 	__snake_case    (       self :							List[str]				)->Union[str, Any]:
      set_requires_grad(self.unet     ,  UpperCamelCase__				)





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							int				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    str       =	min(int(num_inference_steps * strength				)     ,  UpperCamelCase__				)

      __SCREAMING_SNAKE_CASE       :    List[str]       =	max(num_inference_steps - init_timestep     ,  0				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.scheduler.timesteps[t_start:]

      return timesteps, num_inference_steps - t_start





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Any     ,  UpperCamelCase :							List[str]     ,  UpperCamelCase :							Union[str, Any]     ,  UpperCamelCase :							str     ,  UpperCamelCase :							Optional[int]=None				)->int:





      if not isinstance(UpperCamelCase__     ,  torch.Tensor				):
       raise ValueError(F"""`image` has to be of type `torch.Tensor` but is {type(UpperCamelCase__				)}"""				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	image.to(device=UpperCamelCase__     ,  dtype=UpperCamelCase__				)

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

      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      __SCREAMING_SNAKE_CASE       :    str       =	0.1_8_2_1_5 * init_latents
      __SCREAMING_SNAKE_CASE       :    List[str]       =	init_latents.repeat_interleave(UpperCamelCase__     ,  dim=0				)

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	randn_tensor(init_latents.shape     ,  generator=UpperCamelCase__     ,  device=UpperCamelCase__     ,  dtype=UpperCamelCase__				)

      # get latents
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.scheduler.add_noise(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	init_latents

      return latents





     def 	__snake_case    (       self :							int     ,  UpperCamelCase :							Dict				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.coca_transform(UpperCamelCase__				).unsqueeze(0				)
      with torch.no_grad(), torch.cuda.amp.autocast():
       __SCREAMING_SNAKE_CASE       :    int       =	self.coca_model.generate(transformed_image.to(device=self.device     ,  dtype=self.coca_model.dtype				)				)
      __SCREAMING_SNAKE_CASE       :    str       =	self.coca_tokenizer.decode(generated[0].cpu().numpy()				)
      return generated.split("<end_of_text>"				)[0].replace("<start_of_text>"     ,  ""				).rstrip(" .,"				)





     def 	__snake_case    (       self :							int     ,  UpperCamelCase :							str     ,  UpperCamelCase :							List[Any]				)->Dict:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.feature_extractor.preprocess(UpperCamelCase__				)
      __SCREAMING_SNAKE_CASE       :    str       =	torch.from_numpy(clip_image_input["pixel_values"][0]				).unsqueeze(0				).to(self.device				).half()
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.clip_model.get_image_features(UpperCamelCase__				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	image_embeddings_clip / image_embeddings_clip.norm(p=2     ,  dim=-1     ,  keepdim=UpperCamelCase__				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	image_embeddings_clip.repeat_interleave(UpperCamelCase__     ,  dim=0				)
      return image_embeddings_clip





     @torch.enable_grad()
     def 	__snake_case    (       self :							Optional[Any]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							int     ,  UpperCamelCase :							Any     ,  UpperCamelCase :							str     ,  UpperCamelCase :							int     ,  UpperCamelCase :							Union[str, Any]     ,  UpperCamelCase :							int     ,  )->List[str]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	latents.detach().requires_grad_()

      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.scheduler.scale_model_input(UpperCamelCase__     ,  UpperCamelCase__				)

      # predict the noise residual
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.unet(UpperCamelCase__     ,  UpperCamelCase__     ,  encoder_hidden_states=UpperCamelCase__				).sample

      if isinstance(self.scheduler     ,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)				):
       __SCREAMING_SNAKE_CASE       :    int       =	self.scheduler.alphas_cumprod[timestep]
       __SCREAMING_SNAKE_CASE       :    str       =	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
       __SCREAMING_SNAKE_CASE       :    Tuple       =	(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.sqrt(UpperCamelCase__				)
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	pred_original_sample * (fac) + latents * (1 - fac)
      elif isinstance(self.scheduler     ,  UpperCamelCase__				):
       __SCREAMING_SNAKE_CASE       :    Dict       =	self.scheduler.sigmas[index]
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	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
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	1 / 0.1_8_2_1_5 * sample
      __SCREAMING_SNAKE_CASE       :    int       =	self.vae.decode(UpperCamelCase__				).sample
      __SCREAMING_SNAKE_CASE       :    Any       =	(image / 2 + 0.5).clamp(0     ,  1				)

      __SCREAMING_SNAKE_CASE       :    Any       =	transforms.Resize(self.feature_extractor_size				)(UpperCamelCase__				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.normalize(UpperCamelCase__				).to(latents.dtype				)

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.clip_model.get_image_features(UpperCamelCase__				)
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	image_embeddings_clip / image_embeddings_clip.norm(p=2     ,  dim=-1     ,  keepdim=UpperCamelCase__				)

      __SCREAMING_SNAKE_CASE       :    Tuple       =	spherical_dist_loss(UpperCamelCase__     ,  UpperCamelCase__				).mean() * clip_guidance_scale

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	-torch.autograd.grad(UpperCamelCase__     ,  UpperCamelCase__				)[0]

      if isinstance(self.scheduler     ,  UpperCamelCase__				):
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	latents.detach() + grads * (sigma**2)
       __SCREAMING_SNAKE_CASE       :    List[str]       =	noise_pred_original
      else:
       __SCREAMING_SNAKE_CASE       :    List[str]       =	noise_pred_original - torch.sqrt(UpperCamelCase__				) * grads
      return noise_pred, latents






     @torch.no_grad()
     def __call__(       self :							Optional[int]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							int = None     ,  UpperCamelCase :							Optional[int] = None     ,  UpperCamelCase :							Dict = 5_1_2     ,  UpperCamelCase :							Optional[int] = 5_1_2     ,  UpperCamelCase :							Any = 0.6     ,  UpperCamelCase :							str = 5_0     ,  UpperCamelCase :							str = 7.5     ,  UpperCamelCase :							Dict = 1     ,  UpperCamelCase :							List[Any] = 0.0     ,  UpperCamelCase :							Any = 1_0_0     ,  UpperCamelCase :							int = None     ,  UpperCamelCase :							Any = "pil"     ,  UpperCamelCase :							Dict = True     ,  UpperCamelCase :							Optional[int] = 0.8     ,  UpperCamelCase :							Optional[Any] = 0.1     ,  UpperCamelCase :							Tuple = 0.1     ,  )->Union[str, Any]:





      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:
       __SCREAMING_SNAKE_CASE       :    str       =	[generator] + [None] * (batch_size - 1)

      __SCREAMING_SNAKE_CASE       :    str       =	[
          ("model", self.coca_model is None),
          ("tokenizer", self.coca_tokenizer is None),
          ("transform", self.coca_transform is None),
      ]
      __SCREAMING_SNAKE_CASE       :    Any       =	[x[0] for x in coca_is_none if x[1]]
      __SCREAMING_SNAKE_CASE       :    int       =	", ".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."""				)
       __SCREAMING_SNAKE_CASE       :    int       =	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."""				)
       __SCREAMING_SNAKE_CASE       :    Any       =	self.get_image_description(UpperCamelCase__				)

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

      __SCREAMING_SNAKE_CASE       :    Any       =	self.tokenizer(
          UpperCamelCase__     ,  padding="max_length"     ,  max_length=self.tokenizer.model_max_length     ,  truncation=UpperCamelCase__     ,  return_tensors="pt"     ,  )
      __SCREAMING_SNAKE_CASE       :    int       =	self.text_encoder(style_text_input.input_ids.to(self.device				)				)[0]

      __SCREAMING_SNAKE_CASE       :    str       =	slerp(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__				)

      # duplicate text embeddings for each generation per prompt
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	text_embeddings.repeat_interleave(UpperCamelCase__     ,  dim=0				)

      # set timesteps
      __SCREAMING_SNAKE_CASE       :    Dict       =	"offset" in set(inspect.signature(self.scheduler.set_timesteps				).parameters.keys()				)
      __SCREAMING_SNAKE_CASE       :    int       =	{}
      if accepts_offset:
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	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				)

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	self.get_timesteps(UpperCamelCase__     ,  UpperCamelCase__     ,  self.device				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	timesteps[:1].repeat(UpperCamelCase__				)

      # Preprocess image
      __SCREAMING_SNAKE_CASE       :    Dict       =	preprocess(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.prepare_latents(
          UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  text_embeddings.dtype     ,  self.device     ,  UpperCamelCase__				)

      __SCREAMING_SNAKE_CASE       :    int       =	preprocess(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.prepare_latents(
          UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  text_embeddings.dtype     ,  self.device     ,  UpperCamelCase__				)

      __SCREAMING_SNAKE_CASE       :    List[str]       =	slerp(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__				)

      if clip_guidance_scale > 0:
       __SCREAMING_SNAKE_CASE       :    List[str]       =	self.get_clip_image_embeddings(UpperCamelCase__     ,  UpperCamelCase__				)
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.get_clip_image_embeddings(UpperCamelCase__     ,  UpperCamelCase__				)
       __SCREAMING_SNAKE_CASE       :    str       =	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.
      __SCREAMING_SNAKE_CASE       :    Tuple       =	guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
       __SCREAMING_SNAKE_CASE       :    Any       =	content_text_input.input_ids.shape[-1]
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.tokenizer([""]     ,  padding="max_length"     ,  max_length=UpperCamelCase__     ,  return_tensors="pt"				)
       __SCREAMING_SNAKE_CASE       :    Dict       =	self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
       # duplicate unconditional embeddings for each generation per prompt
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	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
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	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`.
      __SCREAMING_SNAKE_CASE       :    Dict       =	(batch_size, self.unet.config.in_channels, height // 8, width // 8)
      __SCREAMING_SNAKE_CASE       :    str       =	text_embeddings.dtype
      if latents is None:
       if self.device.type == "mps":
        # randn does not work reproducibly on mps
        __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	torch.randn(UpperCamelCase__     ,  generator=UpperCamelCase__     ,  device="cpu"     ,  dtype=UpperCamelCase__				).to(
            self.device				)
       else:
        __SCREAMING_SNAKE_CASE       :    int       =	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}"""				)
       __SCREAMING_SNAKE_CASE       :    Tuple       =	latents.to(self.device				)

      # scale the initial noise by the standard deviation required by the scheduler
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	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]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	"eta" in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
      __SCREAMING_SNAKE_CASE       :    int       =	{}
      if accepts_eta:
       __SCREAMING_SNAKE_CASE       :    Dict       =	eta

      # check if the scheduler accepts generator
      __SCREAMING_SNAKE_CASE       :    Tuple       =	"generator" in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
      if accepts_generator:
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	generator

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

        # predict the noise residual
        __SCREAMING_SNAKE_CASE       :    Dict       =	self.unet(UpperCamelCase__     ,  UpperCamelCase__     ,  encoder_hidden_states=UpperCamelCase__				).sample

        # perform classifier free guidance
        if do_classifier_free_guidance:
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	noise_pred.chunk(2				)
         __SCREAMING_SNAKE_CASE       :    Optional[int]       =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

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

        # compute the previous noisy sample x_t -> x_t-1
        __SCREAMING_SNAKE_CASE       :    int       =	self.scheduler.step(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__				).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      __SCREAMING_SNAKE_CASE       :    Tuple       =	1 / 0.1_8_2_1_5 * latents
      __SCREAMING_SNAKE_CASE       :    Dict       =	self.vae.decode(UpperCamelCase__				).sample

      __SCREAMING_SNAKE_CASE       :    str       =	(image / 2 + 0.5).clamp(0     ,  1				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	image.cpu().permute(0     ,  2     ,  3     ,  1				).numpy()

      if output_type == "pil":
       __SCREAMING_SNAKE_CASE       :    Any       =	self.numpy_to_pil(UpperCamelCase__				)

      if not return_dict:
       return (image, None)

      return StableDiffusionPipelineOutput(images=UpperCamelCase__     ,  nsfw_content_detected=UpperCamelCase__				)











def 	_lowerCAmelCase						(				__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 if height >= 1:
  move_tower(height - 1		,	__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )
  move_disk(__lowerCamelCase		,	__lowerCamelCase     )
  move_tower(height - 1		,	__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[Any]		,	__lowerCamelCase   :     Optional[int]     ):



 """simple docstring"""





 print("moving disk from"		,	__lowerCamelCase		,	"to"		,	__lowerCamelCase     )






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[Any]       =	int(input("Height of hanoi: "     ).strip()     )
 move_tower(__lowerCamelCase		,	"A"		,	"B"		,	"C"     )


if __name__ == "__main__":
      main()






