


'''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    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)					->       Any:
     """simple docstring"""




     if isinstance(__A					,	torch.Tensor		):
          return image
     elif isinstance(__A					,	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(__A					,	axis=0		)
          __a   			= np.array(__A		).astype(np.floataa		) / 255.0
          __a   			= image.transpose(0					,	3					,	1					,	2		)
          __a   			= 2.0 * image - 1.0
          __a   			= torch.from_numpy(__A		)
     elif isinstance(image[0]					,	torch.Tensor		):
          __a   			= torch.cat(__A					,	dim=0		)
     return image




def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE=0.9_995		)					->       Union[str, Any]:
     """simple docstring"""




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

     __a   			= np.sum(va * va / (np.linalg.norm(__A		) * np.linalg.norm(__A		))		)
     if np.abs(__A		) > DOT_THRESHOLD:
          __a   			= (1 - t) * va + t * va
     else:
          __a   			= np.arccos(__A		)
          __a   			= np.sin(__A		)
          __a   			= theta_a * t
          __a   			= np.sin(__A		)
          __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(__A		).to(__A		)

     return va




def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)					->       List[Any]:
     """simple docstring"""




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




def 							__lowercase    (   __SCREAMING_SNAKE_CASE					,	__SCREAMING_SNAKE_CASE		)					->       int:
     """simple docstring"""




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



class 			lowerCAmelCase_    (							snake_case__   ):





  """simple docstring"""



  def __init__(       self  :				Dict					,				SCREAMING_SNAKE_CASE__  :				Any					,				SCREAMING_SNAKE_CASE__  :				Union[str, Any]					,				SCREAMING_SNAKE_CASE__  :				List[str]					,				SCREAMING_SNAKE_CASE__  :				Optional[int]					,				SCREAMING_SNAKE_CASE__  :				Optional[int]					,				SCREAMING_SNAKE_CASE__  :				int					,				SCREAMING_SNAKE_CASE__  :				List[Any]					,				SCREAMING_SNAKE_CASE__  :				Optional[Any]=None					,				SCREAMING_SNAKE_CASE__  :				Any=None					,				SCREAMING_SNAKE_CASE__  :				Tuple=None					,				):





       '''simple docstring'''

       super().__init__()
       self.register_modules(
           vae=SCREAMING_SNAKE_CASE__					,				text_encoder=SCREAMING_SNAKE_CASE__					,				clip_model=SCREAMING_SNAKE_CASE__					,				tokenizer=SCREAMING_SNAKE_CASE__					,				unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__					,				feature_extractor=SCREAMING_SNAKE_CASE__					,				coca_model=SCREAMING_SNAKE_CASE__					,				coca_tokenizer=SCREAMING_SNAKE_CASE__					,				coca_transform=SCREAMING_SNAKE_CASE__					,				)
       __a   			= (
           feature_extractor.size
           if isinstance(feature_extractor.size					,				SCREAMING_SNAKE_CASE__    )
           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					,				SCREAMING_SNAKE_CASE__    )
       set_requires_grad(self.clip_model					,				SCREAMING_SNAKE_CASE__    )

  def __a    (       self  :				Tuple					,				SCREAMING_SNAKE_CASE__  :				Union[str, Any] = "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(SCREAMING_SNAKE_CASE__    )

  def __a    (       self  :				Any    ):





       '''simple docstring'''

       self.enable_attention_slicing(SCREAMING_SNAKE_CASE__    )

  def __a    (       self  :				Union[str, Any]    ):





       '''simple docstring'''

       set_requires_grad(self.vae					,				SCREAMING_SNAKE_CASE__    )

  def __a    (       self  :				List[Any]    ):





       '''simple docstring'''

       set_requires_grad(self.vae					,				SCREAMING_SNAKE_CASE__    )

  def __a    (       self  :				str    ):





       '''simple docstring'''

       set_requires_grad(self.unet					,				SCREAMING_SNAKE_CASE__    )

  def __a    (       self  :				str    ):





       '''simple docstring'''

       set_requires_grad(self.unet					,				SCREAMING_SNAKE_CASE__    )

  def __a    (       self  :				str					,				SCREAMING_SNAKE_CASE__  :				List[Any]					,				SCREAMING_SNAKE_CASE__  :				Tuple					,				SCREAMING_SNAKE_CASE__  :				List[Any]    ):





       '''simple docstring'''

       __a   			= min(int(num_inference_steps * strength    )					,				SCREAMING_SNAKE_CASE__    )

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

       return timesteps, num_inference_steps - t_start

  def __a    (       self  :				Optional[Any]					,				SCREAMING_SNAKE_CASE__  :				Optional[Any]					,				SCREAMING_SNAKE_CASE__  :				Optional[Any]					,				SCREAMING_SNAKE_CASE__  :				Dict					,				SCREAMING_SNAKE_CASE__  :				Any					,				SCREAMING_SNAKE_CASE__  :				Optional[int]					,				SCREAMING_SNAKE_CASE__  :				List[Any]=None    ):





       '''simple docstring'''

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

       __a   			= image.to(device=SCREAMING_SNAKE_CASE__					,				dtype=SCREAMING_SNAKE_CASE__    )

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

       # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       __a   			= 0.1_8_2_1_5 * init_latents
       __a   			= init_latents.repeat_interleave(SCREAMING_SNAKE_CASE__					,				dim=0    )

       __a   			= randn_tensor(init_latents.shape					,				generator=SCREAMING_SNAKE_CASE__					,				device=SCREAMING_SNAKE_CASE__					,				dtype=SCREAMING_SNAKE_CASE__    )

       # get latents
       __a   			= self.scheduler.add_noise(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )
       __a   			= init_latents

       return latents

  def __a    (       self  :				Dict					,				SCREAMING_SNAKE_CASE__  :				Any    ):





       '''simple docstring'''

       __a   			= self.coca_transform(SCREAMING_SNAKE_CASE__    ).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 __a    (       self  :				int					,				SCREAMING_SNAKE_CASE__  :				Dict					,				SCREAMING_SNAKE_CASE__  :				str    ):





       '''simple docstring'''

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

  @torch.enable_grad()
  def __a    (       self  :				Union[str, Any]					,				SCREAMING_SNAKE_CASE__  :				int					,				SCREAMING_SNAKE_CASE__  :				List[Any]					,				SCREAMING_SNAKE_CASE__  :				str					,				SCREAMING_SNAKE_CASE__  :				Optional[Any]					,				SCREAMING_SNAKE_CASE__  :				Dict					,				SCREAMING_SNAKE_CASE__  :				str					,				SCREAMING_SNAKE_CASE__  :				Dict					,				):





       '''simple docstring'''

       __a   			= latents.detach().requires_grad_()

       __a   			= self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )

       # predict the noise residual
       __a   			= self.unet(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				encoder_hidden_states=SCREAMING_SNAKE_CASE__    ).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(SCREAMING_SNAKE_CASE__    )
            __a   			= pred_original_sample * (fac) + latents * (1 - fac)
       elif isinstance(self.scheduler					,				SCREAMING_SNAKE_CASE__    ):
            __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 / 0.1_8_2_1_5 * sample
       __a   			= self.vae.decode(SCREAMING_SNAKE_CASE__    ).sample
       __a   			= (image / 2 + 0.5).clamp(0					,				1    )

       __a   			= transforms.Resize(self.feature_extractor_size    )(SCREAMING_SNAKE_CASE__    )
       __a   			= self.normalize(SCREAMING_SNAKE_CASE__    ).to(latents.dtype    )

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

       __a   			= spherical_dist_loss(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    ).mean() * clip_guidance_scale

       __a   			= -torch.autograd.grad(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )[0]

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


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





       '''simple docstring'''

       if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    ) and len(SCREAMING_SNAKE_CASE__    ) != batch_size:
            raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(SCREAMING_SNAKE_CASE__    )} 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(SCREAMING_SNAKE_CASE__					,				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(SCREAMING_SNAKE_CASE__    )
       # generate prompts with coca model if prompt is None
       if content_prompt is None:
            if len(SCREAMING_SNAKE_CASE__    ):
                 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(SCREAMING_SNAKE_CASE__    )
       if style_prompt is None:
            if len(SCREAMING_SNAKE_CASE__    ):
                 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(SCREAMING_SNAKE_CASE__    )

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

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

       __a   			= slerp(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )

       # duplicate text embeddings for each generation per prompt
       __a   			= text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE__					,				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(SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__    )
       # 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(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				self.device    )
       __a   			= timesteps[:1].repeat(SCREAMING_SNAKE_CASE__    )

       # Preprocess image
       __a   			= preprocess(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )
       __a   			= self.prepare_latents(
           SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				text_embeddings.dtype					,				self.device					,				SCREAMING_SNAKE_CASE__    )

       __a   			= preprocess(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )
       __a   			= self.prepare_latents(
           SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				text_embeddings.dtype					,				self.device					,				SCREAMING_SNAKE_CASE__    )

       __a   			= slerp(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )

       if clip_guidance_scale > 0:
            __a   			= self.get_clip_image_embeddings(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )
            __a   			= self.get_clip_image_embeddings(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )
            __a   			= slerp(
                SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )

       # 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=SCREAMING_SNAKE_CASE__					,				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(SCREAMING_SNAKE_CASE__					,				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(SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__					,				device="""cpu"""					,				dtype=SCREAMING_SNAKE_CASE__    ).to(
                     self.device    )
            else:
                 __a   			= torch.randn(SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__					,				device=self.device					,				dtype=SCREAMING_SNAKE_CASE__    )
       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=SCREAMING_SNAKE_CASE__    ):
            for i, t in enumerate(SCREAMING_SNAKE_CASE__    ):
                 # 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(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )

                 # predict the noise residual
                 __a   			= self.unet(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				encoder_hidden_states=SCREAMING_SNAKE_CASE__    ).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(
                          SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				)

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       __a   			= 1 / 0.1_8_2_1_5 * latents
       __a   			= self.vae.decode(SCREAMING_SNAKE_CASE__    ).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(SCREAMING_SNAKE_CASE__    )

       if not return_dict:
            return (image, None)

       return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE__					,				nsfw_content_detected=SCREAMING_SNAKE_CASE__    )











'''simple docstring'''





class __A     :



 '''simple docstring'''







 def __init__(self					,  A			)       ->	None:


      """simple docstring"""



      _a           =    len(A			)
      _a           =    [0] * len_array

      if len_array > 0:
           _a           =    array[0]

      for i in range(1					,  A			):
           _a           =    self.prefix_sum[i - 1] + array[i]



 def    a__    (self					,  A					,  A			)       ->	int:


      """simple docstring"""



      if start == 0:
           return self.prefix_sum[end]

      return self.prefix_sum[end] - self.prefix_sum[start - 1]



 def    a__    (self					,  A			)       ->	bool:


      """simple docstring"""



      _a           =    {0}
      for sum_item in self.prefix_sum:
           if sum_item - target_sum in sums:
                return True

           sums.add(A			)

      return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()



