




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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	str:



       """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						) / 2_55.0
              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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_=0.99_95						)       ->	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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Dict:



       """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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	List[Any]:



       """simple docstring"""

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


class     UpperCamelCase_   (			_a					):

    '''simple docstring'''






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





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     Optional[int] = "auto")						->Any:





           '''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(__lowerCAmelCase)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->List[str]:





           '''simple docstring'''

           self.enable_attention_slicing(__lowerCAmelCase)


    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->Dict:





           '''simple docstring'''

           set_requires_grad(self.vae ,			__lowerCAmelCase)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Union[str, Any]:





           '''simple docstring'''

           set_requires_grad(self.vae ,			__lowerCAmelCase)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->List[str]:





           '''simple docstring'''

           set_requires_grad(self.unet ,			__lowerCAmelCase)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Optional[Any]:





           '''simple docstring'''

           set_requires_grad(self.unet ,			__lowerCAmelCase)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     Union[str, Any])						->Tuple:





           '''simple docstring'''

           A__							=   min(int(num_inference_steps * strength) ,			__lowerCAmelCase)

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

           return timesteps, num_inference_steps - t_start


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     Any ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Optional[int]=None)						->str:





           '''simple docstring'''

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

           A__							=   image.to(device=__lowerCAmelCase ,			dtype=__lowerCAmelCase)

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

           # 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(__lowerCAmelCase ,			dim=0)

           A__							=   randn_tensor(init_latents.shape ,			generator=__lowerCAmelCase ,			device=__lowerCAmelCase ,			dtype=__lowerCAmelCase)

           # get latents
           A__							=   self.scheduler.add_noise(__lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase)
           A__							=   init_latents

           return latents


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     str)						->Tuple:





           '''simple docstring'''

           A__							=   self.coca_transform(__lowerCAmelCase).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 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     int)						->Tuple:





           '''simple docstring'''

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


    @torch.enable_grad()
    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     Optional[int] ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     Optional[Any] ,			)						->int:





           '''simple docstring'''

           A__							=   latents.detach().requires_grad_()

           A__							=   self.scheduler.scale_model_input(__lowerCAmelCase ,			__lowerCAmelCase)

           # predict the noise residual
           A__							=   self.unet(__lowerCAmelCase ,			__lowerCAmelCase ,			encoder_hidden_states=__lowerCAmelCase).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(__lowerCAmelCase)
                  A__							=   pred_original_sample * (fac) + latents * (1 - fac)
           elif isinstance(self.scheduler ,			__lowerCAmelCase):
                  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(__lowerCAmelCase).sample
           A__							=   (image / 2 + 0.5).clamp(0 ,			1)

           A__							=   transforms.Resize(self.feature_extractor_size)(__lowerCAmelCase)
           A__							=   self.normalize(__lowerCAmelCase).to(latents.dtype)

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

           A__							=   spherical_dist_loss(__lowerCAmelCase ,			__lowerCAmelCase).mean() * clip_guidance_scale

           A__							=   -torch.autograd.grad(__lowerCAmelCase ,			__lowerCAmelCase)[0]

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





    @torch.no_grad()
    def __call__(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Optional[int] = None ,			UpperCAmelCase__				:     int = None ,			UpperCAmelCase__				:     Dict = 512 ,			UpperCAmelCase__				:     Dict = 512 ,			UpperCAmelCase__				:     str = 0.6 ,			UpperCAmelCase__				:     List[str] = 50 ,			UpperCAmelCase__				:     Union[str, Any] = 7.5 ,			UpperCAmelCase__				:     str = 1 ,			UpperCAmelCase__				:     Optional[Any] = 0.0 ,			UpperCAmelCase__				:     Optional[int] = 100 ,			UpperCAmelCase__				:     List[str] = None ,			UpperCAmelCase__				:     Tuple = "pil" ,			UpperCAmelCase__				:     str = True ,			UpperCAmelCase__				:     Tuple = 0.8 ,			UpperCAmelCase__				:     Union[str, Any] = 0.1 ,			UpperCAmelCase__				:     Tuple = 0.1 ,			)						->Tuple:





           '''simple docstring'''

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

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

           A__							=   self.tokenizer(
               __lowerCAmelCase ,			padding='''max_length''' ,			max_length=self.tokenizer.model_max_length ,			truncation=__lowerCAmelCase ,			return_tensors='''pt''' ,			)
           A__							=   self.text_encoder(style_text_input.input_ids.to(self.device))[0]

           A__							=   slerp(__lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase)

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

           # Preprocess image
           A__							=   preprocess(__lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase)
           A__							=   self.prepare_latents(
               __lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase ,			text_embeddings.dtype ,			self.device ,			__lowerCAmelCase)

           A__							=   preprocess(__lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase)
           A__							=   self.prepare_latents(
               __lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase ,			text_embeddings.dtype ,			self.device ,			__lowerCAmelCase)

           A__							=   slerp(__lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase)

           if clip_guidance_scale > 0:
                  A__							=   self.get_clip_image_embeddings(__lowerCAmelCase ,			__lowerCAmelCase)
                  A__							=   self.get_clip_image_embeddings(__lowerCAmelCase ,			__lowerCAmelCase)
                  A__							=   slerp(
                      __lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase)

           # 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=__lowerCAmelCase ,			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(__lowerCAmelCase ,			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(__lowerCAmelCase ,			generator=__lowerCAmelCase ,			device='''cpu''' ,			dtype=__lowerCAmelCase).to(
                             self.device)
                  else:
                         A__							=   torch.randn(__lowerCAmelCase ,			generator=__lowerCAmelCase ,			device=self.device ,			dtype=__lowerCAmelCase)
           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=__lowerCAmelCase):
                  for i, t in enumerate(__lowerCAmelCase):
                         # 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(__lowerCAmelCase ,			__lowerCAmelCase)

                         # predict the noise residual
                         A__							=   self.unet(__lowerCAmelCase ,			__lowerCAmelCase ,			encoder_hidden_states=__lowerCAmelCase).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(
                                    __lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase ,			)

                         # compute the previous noisy sample x_t -> x_t-1
                         A__							=   self.scheduler.step(__lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase ,			**__lowerCAmelCase).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(__lowerCAmelCase).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(__lowerCAmelCase)

           if not return_dict:
                  return (image, None)

           return StableDiffusionPipelineOutput(images=__lowerCAmelCase ,			nsfw_content_detected=__lowerCAmelCase)








def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

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


if __name__ == "__main__":
  import doctest

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


