





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	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   if isinstance(lowerCamelCase				,			torch.Tensor ):
      return image
   elif isinstance(lowerCamelCase				,			PIL.Image.Image ):
      a__						:    List[str]   		=	[image]

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






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase=0.9995 ):
   if not isinstance(lowerCamelCase				,			np.ndarray ):
      a__						:    Dict   		=	True
      a__						:    Dict   		=	va.device
      a__						:    List[str]   		=	va.cpu().numpy()
      a__						:    str   		=	va.cpu().numpy()

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

   if inputs_are_torch:
      a__						:    Optional[int]   		=	torch.from_numpy(lowerCamelCase ).to(lowerCamelCase )

   return va






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   a__						:    Optional[Any]   		=	F.normalize(lowerCamelCase				,			dim=-1 )
   a__						:    Union[str, Any]   		=	F.normalize(lowerCamelCase				,			dim=-1 )
   return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   for param in model.parameters():
      a__						:    int   		=	value







class 	__lowerCAmelCase      (       _UpperCamelCase	):





   def __init__(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case=None       ,      snake_case=None       ,      snake_case=None       ,      )					->	Optional[Any]:
      """simple docstring"""





      super().__init__()
      self.register_modules(
          vae=snake_case       ,      text_encoder=snake_case       ,      clip_model=snake_case       ,      tokenizer=snake_case       ,      unet=snake_case       ,      scheduler=snake_case       ,      feature_extractor=snake_case       ,      coca_model=snake_case       ,      coca_tokenizer=snake_case       ,      coca_transform=snake_case       ,      )
      a__						:    Union[str, Any]   		=	(
          feature_extractor.size
          if isinstance(feature_extractor.size       ,      snake_case   )
          else feature_extractor.size["shortest_edge"]
      )
      a__						:    Optional[Any]   		=	transforms.Normalize(mean=feature_extractor.image_mean       ,      std=feature_extractor.image_std   )
      set_requires_grad(self.text_encoder       ,      snake_case   )
      set_requires_grad(self.clip_model       ,      snake_case   )





   def 						_snake_case			(			self       ,      snake_case = "auto"   )					->	str:
      """simple docstring"""





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





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      self.enable_attention_slicing(snake_case   )





   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      set_requires_grad(self.vae       ,      snake_case   )





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      set_requires_grad(self.vae       ,      snake_case   )





   def 						_snake_case			(			self   )					->	Any:
      """simple docstring"""





      set_requires_grad(self.unet       ,      snake_case   )





   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      set_requires_grad(self.unet       ,      snake_case   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case   )					->	int:
      """simple docstring"""





      a__						:    Dict   		=	min(int(num_inference_steps * strength   )       ,      snake_case   )

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

      return timesteps, num_inference_steps - t_start





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case=None   )					->	Tuple:
      """simple docstring"""





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

      a__						:    List[str]   		=	image.to(device=snake_case       ,      dtype=snake_case   )

      if isinstance(snake_case       ,      snake_case   ):
         a__						:    Optional[int]   		=	[
             self.vae.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(snake_case   )
         ]
         a__						:    List[Any]   		=	torch.cat(snake_case       ,      dim=0   )
      else:
         a__						:    int   		=	self.vae.encode(snake_case   ).latent_dist.sample(snake_case   )

      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      a__						:    Optional[int]   		=	0.18_215 * init_latents
      a__						:    Any   		=	init_latents.repeat_interleave(snake_case       ,      dim=0   )

      a__						:    Optional[int]   		=	randn_tensor(init_latents.shape       ,      generator=snake_case       ,      device=snake_case       ,      dtype=snake_case   )

      # get latents
      a__						:    Tuple   		=	self.scheduler.add_noise(snake_case       ,      snake_case       ,      snake_case   )
      a__						:    Any   		=	init_latents

      return latents





   def 						_snake_case			(			self       ,      snake_case   )					->	Dict:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	Tuple:
      """simple docstring"""





      a__						:    Optional[int]   		=	self.feature_extractor.preprocess(snake_case   )
      a__						:    Optional[Any]   		=	torch.from_numpy(clip_image_input["pixel_values"][0]   ).unsqueeze(0   ).to(self.device   ).half()
      a__						:    Tuple   		=	self.clip_model.get_image_features(snake_case   )
      a__						:    Dict   		=	image_embeddings_clip / image_embeddings_clip.norm(p=2       ,      dim=-1       ,      keepdim=snake_case   )
      a__						:    Optional[int]   		=	image_embeddings_clip.repeat_interleave(snake_case       ,      dim=0   )
      return image_embeddings_clip





   @torch.enable_grad()
   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      )					->	List[Any]:
      """simple docstring"""





      a__						:    Any   		=	latents.detach().requires_grad_()

      a__						:    Any   		=	self.scheduler.scale_model_input(snake_case       ,      snake_case   )

      # predict the noise residual
      a__						:    int   		=	self.unet(snake_case       ,      snake_case       ,      encoder_hidden_states=snake_case   ).sample

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

         a__						:    Tuple   		=	torch.sqrt(snake_case   )
         a__						:    int   		=	pred_original_sample * (fac) + latents * (1 - fac)
      elif isinstance(self.scheduler       ,      snake_case   ):
         a__						:    Union[str, Any]   		=	self.scheduler.sigmas[index]
         a__						:    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
      a__						:    Optional[int]   		=	1 / 0.18_215 * sample
      a__						:    Union[str, Any]   		=	self.vae.decode(snake_case   ).sample
      a__						:    List[str]   		=	(image / 2 + 0.5).clamp(0       ,      1   )

      a__						:    Any   		=	transforms.Resize(self.feature_extractor_size   )(snake_case   )
      a__						:    Optional[int]   		=	self.normalize(snake_case   ).to(latents.dtype   )

      a__						:    Dict   		=	self.clip_model.get_image_features(snake_case   )
      a__						:    Union[str, Any]   		=	image_embeddings_clip / image_embeddings_clip.norm(p=2       ,      dim=-1       ,      keepdim=snake_case   )

      a__						:    Dict   		=	spherical_dist_loss(snake_case       ,      snake_case   ).mean() * clip_guidance_scale

      a__						:    int   		=	-torch.autograd.grad(snake_case       ,      snake_case   )[0]

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





   @torch.no_grad()
   def __call__(			self       ,      snake_case       ,      snake_case       ,      snake_case = None       ,      snake_case = None       ,      snake_case = 512       ,      snake_case = 512       ,      snake_case = 0.6       ,      snake_case = 50       ,      snake_case = 7.5       ,      snake_case = 1       ,      snake_case = 0.0       ,      snake_case = 100       ,      snake_case = None       ,      snake_case = "pil"       ,      snake_case = True       ,      snake_case = 0.8       ,      snake_case = 0.1       ,      snake_case = 0.1       ,      )					->	str:
      """simple docstring"""





      if isinstance(snake_case       ,      snake_case   ) and len(snake_case   ) != batch_size:
         raise ValueError(F"""You have passed {batch_size} batch_size, but only {len(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(snake_case       ,      torch.Generator   ) and batch_size > 1:
         a__						:    List[Any]   		=	[generator] + [None] * (batch_size - 1)

      a__						:    Tuple   		=	[
          ("model", self.coca_model is None),
          ("tokenizer", self.coca_tokenizer is None),
          ("transform", self.coca_transform is None),
      ]
      a__						:    Any   		=	[x[0] for x in coca_is_none if x[1]]
      a__						:    Any   		=	", ".join(snake_case   )
      # generate prompts with coca model if prompt is None
      if content_prompt is None:
         if len(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__						:    Tuple   		=	self.get_image_description(snake_case   )
      if style_prompt is None:
         if len(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__						:    List[Any]   		=	self.get_image_description(snake_case   )

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

      a__						:    str   		=	self.tokenizer(
          snake_case       ,      padding="max_length"       ,      max_length=self.tokenizer.model_max_length       ,      truncation=snake_case       ,      return_tensors="pt"       ,      )
      a__						:    List[Any]   		=	self.text_encoder(style_text_input.input_ids.to(self.device   )   )[0]

      a__						:    Optional[int]   		=	slerp(snake_case       ,      snake_case       ,      snake_case   )

      # duplicate text embeddings for each generation per prompt
      a__						:    List[Any]   		=	text_embeddings.repeat_interleave(snake_case       ,      dim=0   )

      # set timesteps
      a__						:    Optional[Any]   		=	"offset" in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
      a__						:    Tuple   		=	{}
      if accepts_offset:
         a__						:    Union[str, Any]   		=	1

      self.scheduler.set_timesteps(snake_case       ,      **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__						:    Optional[Any]   		=	self.get_timesteps(snake_case       ,      snake_case       ,      self.device   )
      a__						:    List[str]   		=	timesteps[:1].repeat(snake_case   )

      # Preprocess image
      a__						:    Tuple   		=	preprocess(snake_case       ,      snake_case       ,      snake_case   )
      a__						:    str   		=	self.prepare_latents(
          snake_case       ,      snake_case       ,      snake_case       ,      text_embeddings.dtype       ,      self.device       ,      snake_case   )

      a__						:    List[str]   		=	preprocess(snake_case       ,      snake_case       ,      snake_case   )
      a__						:    Tuple   		=	self.prepare_latents(
          snake_case       ,      snake_case       ,      snake_case       ,      text_embeddings.dtype       ,      self.device       ,      snake_case   )

      a__						:    Union[str, Any]   		=	slerp(snake_case       ,      snake_case       ,      snake_case   )

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

      # scale the initial noise by the standard deviation required by the scheduler
      a__						:    Tuple   		=	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__						:    List[Any]   		=	"eta" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
      a__						:    Optional[Any]   		=	{}
      if accepts_eta:
         a__						:    List[str]   		=	eta

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

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

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

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

            # perform clip guidance
            if clip_guidance_scale > 0:
               a__						:    Dict   		=	(
                   text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
               )
               a__		,  a__						:    List[str]   		=	self.cond_fn(
                   snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      )

            # compute the previous noisy sample x_t -> x_t-1
            a__						:    List[Any]   		=	self.scheduler.step(snake_case       ,      snake_case       ,      snake_case       ,      **snake_case   ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      a__						:    List[Any]   		=	1 / 0.18_215 * latents
      a__						:    Dict   		=	self.vae.decode(snake_case   ).sample

      a__						:    List[Any]   		=	(image / 2 + 0.5).clamp(0       ,      1   )
      a__						:    List[str]   		=	image.cpu().permute(0       ,      2       ,      3       ,      1   ).numpy()

      if output_type == "pil":
         a__						:    str   		=	self.numpy_to_pil(snake_case   )

      if not return_dict:
         return (image, None)

      return StableDiffusionPipelineOutput(images=snake_case       ,      nsfw_content_detected=snake_case   )








# Function to print upper half of diamond (pyramid)






def    _A	(   lowerCamelCase ):
   for i in range(0				,			lowerCamelCase ):
      for _ in range(0				,			n - i - 1 ):  # printing spaces
         print(" "				,			end="" )
      for _ in range(0				,			i + 1 ):  # printing stars
         print("* "				,			end="" )
      print()






def    _A	(   lowerCamelCase ):
   for i in range(lowerCamelCase				,			0				,			-1 ):
      for _ in range(lowerCamelCase				,			0				,			-1 ):  # printing stars
         print("* "				,			end="" )
      print()
      for _ in range(n - i + 1				,			0				,			-1 ):  # printing spaces
         print(" "				,			end="" )






def    _A	(   lowerCamelCase ):
   if n <= 0:
      print("       ...       ....        nothing printing :(" )
      return
   floyd(lowerCamelCase )  # upper half
   reverse_floyd(lowerCamelCase )  # lower half


if __name__ == "__main__":
  print(R"""| /\ | |- |  |-  |--| |\  /| |-""")
  print(R"""|/  \| |- |_ |_  |__| | \/ | |_""")
  SCREAMING_SNAKE_CASE__							:      Dict         =    1
  while K:
    SCREAMING_SNAKE_CASE__							:      str         =    int(input("""enter the number and , and see the magic : """))
    print()
    pretty_print(user_number)
    SCREAMING_SNAKE_CASE__							:      Tuple         =    int(input("""press 0 to exit... and 1 to continue..."""))

  print("""Good Bye...""")

