

"""simple docstring"""





def   _A  (  UpperCamelCase_     :       str)	->						str:
   '''simple docstring'''


   return " ".join(
       "".join(word[::-1]) if len(UpperCamelCase_) > 4 else word for word in sentence.split())


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(reverse_long_words('Hey wollef sroirraw'))







"""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   _A  (  UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       List[str])	->						Optional[int]:
   '''simple docstring'''


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

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




def   _A  (  UpperCamelCase_     :       Dict,			UpperCamelCase_     :       str,			UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       List[Any]=0.9_995)	->						int:
   '''simple docstring'''


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

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

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

   return va




def   _A  (  UpperCamelCase_     :       List[str],			UpperCamelCase_     :       Union[str, Any])	->						int:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_     :       Optional[int],			UpperCamelCase_     :       str)	->						Optional[int]:
   '''simple docstring'''


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




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 def __init__(			self							:			Dict,					UpperCAmelCase__							:			AutoencoderKL,					UpperCAmelCase__							:			CLIPTextModel,					UpperCAmelCase__							:			CLIPModel,					UpperCAmelCase__							:			CLIPTokenizer,					UpperCAmelCase__							:			UNetaDConditionModel,					UpperCAmelCase__							:			Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler],					UpperCAmelCase__							:			CLIPFeatureExtractor,					UpperCAmelCase__							:			Union[str, Any]=None,					UpperCAmelCase__							:			List[str]=None,					UpperCAmelCase__							:			Any=None,					):
    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__,					)
    __lowercase								=						(
        feature_extractor.size
        if isinstance(feature_extractor.size,					UpperCAmelCase__	)
        else feature_extractor.size["shortest_edge"]
    )
    __lowercase								=						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 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			Optional[Union[str, int]] = "auto"	):
    if slice_size == "auto":
       # half the attention head size is usually a good trade-off between
       # speed and memory
       __lowercase								=						self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(UpperCAmelCase__	)


 def 					_lowercase (			self							:			int	):
    self.enable_attention_slicing(UpperCAmelCase__	)


 def 					_lowercase (			self							:			str	):
    set_requires_grad(self.vae,					UpperCAmelCase__	)


 def 					_lowercase (			self							:			Any	):
    set_requires_grad(self.vae,					UpperCAmelCase__	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    set_requires_grad(self.unet,					UpperCAmelCase__	)


 def 					_lowercase (			self							:			Any	):
    set_requires_grad(self.unet,					UpperCAmelCase__	)


 def 					_lowercase (			self							:			List[str],					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Optional[Any]	):
    # get the original timestep using init_timestep
    __lowercase								=						min(int(num_inference_steps * strength	),					UpperCAmelCase__	)

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

    return timesteps, num_inference_steps - t_start


 def 					_lowercase (			self							:			List[str],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			int=None	):
    if not isinstance(UpperCAmelCase__,					torch.Tensor	):
       raise ValueError(F"""`image` has to be of type `torch.Tensor` but is {type(UpperCAmelCase__	)}"""	)

    __lowercase								=						image.to(device=UpperCAmelCase__,					dtype=UpperCAmelCase__	)

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

    # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
    __lowercase								=						0.18_215 * init_latents
    __lowercase								=						init_latents.repeat_interleave(UpperCAmelCase__,					dim=0	)

    __lowercase								=						randn_tensor(init_latents.shape,					generator=UpperCAmelCase__,					device=UpperCAmelCase__,					dtype=UpperCAmelCase__	)

    # get latents
    __lowercase								=						self.scheduler.add_noise(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						init_latents

    return latents


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


 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Tuple	):
    __lowercase								=						self.feature_extractor.preprocess(UpperCAmelCase__	)
    __lowercase								=						torch.from_numpy(clip_image_input["pixel_values"][0]	).unsqueeze(0	).to(self.device	).half()
    __lowercase								=						self.clip_model.get_image_features(UpperCAmelCase__	)
    __lowercase								=						image_embeddings_clip / image_embeddings_clip.norm(p=2,					dim=-1,					keepdim=UpperCAmelCase__	)
    __lowercase								=						image_embeddings_clip.repeat_interleave(UpperCAmelCase__,					dim=0	)
    return image_embeddings_clip


 @torch.enable_grad()
 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Optional[int],					):
    __lowercase								=						latents.detach().requires_grad_()

    __lowercase								=						self.scheduler.scale_model_input(UpperCAmelCase__,					UpperCAmelCase__	)

    # predict the noise residual
    __lowercase								=						self.unet(UpperCAmelCase__,					UpperCAmelCase__,					encoder_hidden_states=UpperCAmelCase__	).sample

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

       __lowercase								=						torch.sqrt(UpperCAmelCase__	)
       __lowercase								=						pred_original_sample * (fac) + latents * (1 - fac)
    elif isinstance(self.scheduler,					UpperCAmelCase__	):
       __lowercase								=						self.scheduler.sigmas[index]
       __lowercase								=						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
    __lowercase								=						1 / 0.18_215 * sample
    __lowercase								=						self.vae.decode(UpperCAmelCase__	).sample
    __lowercase								=						(image / 2 + 0.5).clamp(0,					1	)

    __lowercase								=						transforms.Resize(self.feature_extractor_size	)(UpperCAmelCase__	)
    __lowercase								=						self.normalize(UpperCAmelCase__	).to(latents.dtype	)

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

    __lowercase								=						spherical_dist_loss(UpperCAmelCase__,					UpperCAmelCase__	).mean() * clip_guidance_scale

    __lowercase								=						-torch.autograd.grad(UpperCAmelCase__,					UpperCAmelCase__	)[0]

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



 @torch.no_grad()
 def __call__(			self							:			str,					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] = 5_1_2,					UpperCAmelCase__							:			Optional[int] = 5_1_2,					UpperCAmelCase__							:			float = 0.6,					UpperCAmelCase__							:			Optional[int] = 5_0,					UpperCAmelCase__							:			Optional[float] = 7.5,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[float] = 1_0_0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			float = 0.8,					UpperCAmelCase__							:			float = 0.1,					UpperCAmelCase__							:			float = 0.1,					):
    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:
       __lowercase								=						[generator] + [None] * (batch_size - 1)

    __lowercase								=						[
        ("model", self.coca_model is None),
        ("tokenizer", self.coca_tokenizer is None),
        ("transform", self.coca_transform is None),
    ]
    __lowercase								=						[x[0] for x in coca_is_none if x[1]]
    __lowercase								=						", ".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."""	)
       __lowercase								=						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."""	)
       __lowercase								=						self.get_image_description(UpperCAmelCase__	)

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

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

    __lowercase								=						slerp(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__	)

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

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

    __lowercase		,__lowercase								=						self.get_timesteps(UpperCAmelCase__,					UpperCAmelCase__,					self.device	)
    __lowercase								=						timesteps[:1].repeat(UpperCAmelCase__	)

    # Preprocess image
    __lowercase								=						preprocess(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						self.prepare_latents(
        UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					text_embeddings.dtype,					self.device,					UpperCAmelCase__	)

    __lowercase								=						preprocess(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						self.prepare_latents(
        UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					text_embeddings.dtype,					self.device,					UpperCAmelCase__	)

    __lowercase								=						slerp(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__	)

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

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

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

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

          # predict the noise residual
          __lowercase								=						self.unet(UpperCAmelCase__,					UpperCAmelCase__,					encoder_hidden_states=UpperCAmelCase__	).sample

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

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

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
    __lowercase								=						1 / 0.18_215 * latents
    __lowercase								=						self.vae.decode(UpperCAmelCase__	).sample

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

    if output_type == "pil":
       __lowercase								=						self.numpy_to_pil(UpperCAmelCase__	)

    if not return_dict:
       return (image, None)

    return StableDiffusionPipelineOutput(images=UpperCAmelCase__,					nsfw_content_detected=UpperCAmelCase__	)




