'''simple docstring'''

import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput




class    lowerCamelCase__  (   nn.Module     ):



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=1.0     )				->     str:
        """simple docstring"""





        super().__init__()

        _UpperCamelCase  :Dict       =cut_size
        _UpperCamelCase  :Dict       =cut_power




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :str       =pixel_values.shape[2:4]
        _UpperCamelCase  :List[Any]       =min(lowerCAmelCase__					,      lowerCAmelCase__     )
        _UpperCamelCase  :List[Any]       =min(lowerCAmelCase__					,      lowerCAmelCase__					,      self.cut_size     )
        _UpperCamelCase  :Any       =[]
        for _ in range(lowerCAmelCase__     ):
             _UpperCamelCase  :Union[str, Any]       =int(torch.rand([]     ) ** self.cut_power * (max_size - min_size) + min_size     )
             _UpperCamelCase  :Any       =torch.randint(0					,      sideX - size + 1					,      ()     )
             _UpperCamelCase  :int       =torch.randint(0					,      sideY - size + 1					,      ()     )
             _UpperCamelCase  :Dict       =pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
             cutouts.append(F.adaptive_avg_poolad(lowerCAmelCase__					,      self.cut_size     )     )
        return torch.cat(lowerCAmelCase__     )




def  _lowerCAmelCase				(				__a			,      __a      )  ->      Dict:
     '''simple docstring'''







     _UpperCamelCase  :str       =F.normalize(__a			,      dim=-1      )
     _UpperCamelCase  :Optional[Any]       =F.normalize(__a			,      dim=-1      )
     return (x - y).norm(dim=-1      ).div(2      ).arcsin().pow(2      ).mul(2      )



def  _lowerCAmelCase				(				__a			,      __a      )  ->      List[str]:
     '''simple docstring'''







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




class    lowerCamelCase__  (   __snake_case     ):



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      )				->     Tuple:
        """simple docstring"""





        super().__init__()
        self.register_modules(
            vae=lowerCAmelCase__					,      text_encoder=lowerCAmelCase__					,      clip_model=lowerCAmelCase__					,      tokenizer=lowerCAmelCase__					,      unet=lowerCAmelCase__					,      scheduler=lowerCAmelCase__					,      feature_extractor=lowerCAmelCase__					,      )

        _UpperCamelCase  :int       =transforms.Normalize(mean=feature_extractor.image_mean					,      std=feature_extractor.image_std     )
        _UpperCamelCase  :int       =(
            feature_extractor.size
            if isinstance(feature_extractor.size					,      lowerCAmelCase__     )
            else feature_extractor.size["""shortest_edge"""]
        )
        _UpperCamelCase  :int       =MakeCutouts(self.cut_out_size     )

        set_requires_grad(self.text_encoder					,      lowerCAmelCase__     )
        set_requires_grad(self.clip_model					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__ = "auto"     )				->     Optional[Any]:
        """simple docstring"""





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



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





        self.enable_attention_slicing(lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Union[str, Any]:
        """simple docstring"""





        set_requires_grad(self.vae					,      lowerCAmelCase__     )



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





        set_requires_grad(self.vae					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Union[str, Any]:
        """simple docstring"""





        set_requires_grad(self.unet					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        set_requires_grad(self.unet					,      lowerCAmelCase__     )



   @torch.enable_grad()
   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__=True					,      )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :Dict       =latents.detach().requires_grad_()

        _UpperCamelCase  :Union[str, Any]       =self.scheduler.scale_model_input(lowerCAmelCase__					,      lowerCAmelCase__     )

        # predict the noise residual
        _UpperCamelCase  :Union[str, Any]       =self.unet(lowerCAmelCase__					,      lowerCAmelCase__					,      encoder_hidden_states=lowerCAmelCase__     ).sample

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

             _UpperCamelCase  :List[str]       =torch.sqrt(lowerCAmelCase__     )
             _UpperCamelCase  :str       =pred_original_sample * (fac) + latents * (1 - fac)
        elif isinstance(self.scheduler					,      lowerCAmelCase__     ):
             _UpperCamelCase  :Optional[int]       =self.scheduler.sigmas[index]
             _UpperCamelCase  :Dict       =latents - sigma * noise_pred
        else:
             raise ValueError(f'''scheduler type {type(self.scheduler     )} not supported'''     )

        _UpperCamelCase  :int       =1 / self.vae.config.scaling_factor * sample
        _UpperCamelCase  :Union[str, Any]       =self.vae.decode(lowerCAmelCase__     ).sample
        _UpperCamelCase  :Optional[Any]       =(image / 2 + 0.5).clamp(0					,      1     )

        if use_cutouts:
             _UpperCamelCase  :List[str]       =self.make_cutouts(lowerCAmelCase__					,      lowerCAmelCase__     )
        else:
             _UpperCamelCase  :List[Any]       =transforms.Resize(self.cut_out_size     )(lowerCAmelCase__     )
        _UpperCamelCase  :Dict       =self.normalize(lowerCAmelCase__     ).to(latents.dtype     )

        _UpperCamelCase  :Optional[int]       =self.clip_model.get_image_features(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =image_embeddings_clip / image_embeddings_clip.norm(p=2					,      dim=-1					,      keepdim=lowerCAmelCase__     )

        if use_cutouts:
             _UpperCamelCase  :Dict       =spherical_dist_loss(lowerCAmelCase__					,      lowerCAmelCase__     )
             _UpperCamelCase  :Any       =dists.view([num_cutouts, sample.shape[0], -1]     )
             _UpperCamelCase  :Any       =dists.sum(2     ).mean(0     ).sum() * clip_guidance_scale
        else:
             _UpperCamelCase  :int       =spherical_dist_loss(lowerCAmelCase__					,      lowerCAmelCase__     ).mean() * clip_guidance_scale

        _UpperCamelCase  :Optional[int]       =-torch.autograd.grad(lowerCAmelCase__					,      lowerCAmelCase__     )[0]

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




   @torch.no_grad()
   def __call__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__ = 512					,      lowerCAmelCase__ = 512					,      lowerCAmelCase__ = 50					,      lowerCAmelCase__ = 7.5					,      lowerCAmelCase__ = 1					,      lowerCAmelCase__ = 0.0					,      lowerCAmelCase__ = 100					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = 4					,      lowerCAmelCase__ = True					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = "pil"					,      lowerCAmelCase__ = True					,      )				->     Optional[Any]:
        """simple docstring"""





        if isinstance(lowerCAmelCase__					,      lowerCAmelCase__     ):
             _UpperCamelCase  :str       =1
        elif isinstance(lowerCAmelCase__					,      lowerCAmelCase__     ):
             _UpperCamelCase  :Optional[int]       =len(lowerCAmelCase__     )
        else:
             raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__     )}'''     )

        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}.'''     )

        # get prompt text embeddings
        _UpperCamelCase  :Tuple       =self.tokenizer(
            lowerCAmelCase__					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      truncation=lowerCAmelCase__					,      return_tensors="""pt"""					,      )
        _UpperCamelCase  :int       =self.text_encoder(text_input.input_ids.to(self.device     )     )[0]
        # duplicate text embeddings for each generation per prompt
        _UpperCamelCase  :Optional[Any]       =text_embeddings.repeat_interleave(lowerCAmelCase__					,      dim=0     )

        if clip_guidance_scale > 0:
             if clip_prompt is not None:
                  _UpperCamelCase  :Dict       =self.tokenizer(
                      lowerCAmelCase__					,      padding="""max_length"""					,      max_length=self.tokenizer.model_max_length					,      truncation=lowerCAmelCase__					,      return_tensors="""pt"""					,      ).input_ids.to(self.device     )
             else:
                  _UpperCamelCase  :Optional[int]       =text_input.input_ids.to(self.device     )
             _UpperCamelCase  :List[Any]       =self.clip_model.get_text_features(lowerCAmelCase__     )
             _UpperCamelCase  :Union[str, Any]       =text_embeddings_clip / text_embeddings_clip.norm(p=2					,      dim=-1					,      keepdim=lowerCAmelCase__     )
             # duplicate text embeddings clip for each generation per prompt
             _UpperCamelCase  :Tuple       =text_embeddings_clip.repeat_interleave(lowerCAmelCase__					,      dim=0     )

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

        # set timesteps
        _UpperCamelCase  :List[Any]       ="""offset""" in set(inspect.signature(self.scheduler.set_timesteps     ).parameters.keys()     )
        _UpperCamelCase  :int       ={}
        if accepts_offset:
             _UpperCamelCase  :Any       =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
        _UpperCamelCase  :str       =self.scheduler.timesteps.to(self.device     )

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

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

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

             # predict the noise residual
             _UpperCamelCase  :Optional[Any]       =self.unet(lowerCAmelCase__					,      lowerCAmelCase__					,      encoder_hidden_states=lowerCAmelCase__     ).sample

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

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

             # compute the previous noisy sample x_t -> x_t-1
             _UpperCamelCase  :Union[str, Any]       =self.scheduler.step(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      **lowerCAmelCase__     ).prev_sample

        # scale and decode the image latents with vae
        _UpperCamelCase  :Tuple       =1 / self.vae.config.scaling_factor * latents
        _UpperCamelCase  :Any       =self.vae.decode(lowerCAmelCase__     ).sample

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

        if output_type == "pil":
             _UpperCamelCase  :Dict       =self.numpy_to_pil(lowerCAmelCase__     )

        if not return_dict:
             return (image, None)

        return StableDiffusionPipelineOutput(images=lowerCAmelCase__					,      nsfw_content_detected=lowerCAmelCase__     )
'''simple docstring'''

from __future__ import annotations

_lowerCamelCase :				Optional[int]														= """Alexander Joslin"""



def  _lowerCAmelCase				(				__a      )  ->      int:
     '''simple docstring'''







     if number <= 0:
          _UpperCamelCase  :Optional[Any]       =F'''n must be greater than 0. Got n = {number}'''
          raise ValueError(__a      )

     _UpperCamelCase  :Optional[Any]       =[number + 1] * (number + 1)

     # starting position
     _UpperCamelCase  :int       =0
     for i in range(1			,      __a      ):
          _UpperCamelCase  :List[str]       =min(table[i + 1]			,      table[i] + 1      )
          # check if out of bounds
          if i * 2 <= number:
               _UpperCamelCase  :int       =min(table[i * 2]			,      table[i] + 1      )
          # check if out of bounds
          if i * 3 <= number:
               _UpperCamelCase  :Tuple       =min(table[i * 3]			,      table[i] + 1      )
     return table[number]


if __name__ == "__main__":
      import doctest

      doctest.testmod()