



'''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 __A      (  nn.Module						):



 '''simple docstring'''







 def __init__(self			,       A			,       A=1.0	)   ->					Dict:


      """simple docstring"""



      super().__init__()

      _a     =  cut_size
      _a     =  cut_power



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


      """simple docstring"""



      _a  ,   _a     =  pixel_values.shape[2:4]
      _a     =  min(lowerCAmelCase__			,       lowerCAmelCase__	)
      _a     =  min(lowerCAmelCase__			,       lowerCAmelCase__			,       self.cut_size	)
      _a     =  []
      for _ in range(lowerCAmelCase__	):
           _a     =  int(torch.rand([]	) ** self.cut_power * (max_size - min_size) + min_size	)
           _a     =  torch.randint(0			,       sideX - size + 1			,       ()	)
           _a     =  torch.randint(0			,       sideY - size + 1			,       ()	)
           _a     =  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):



     """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 lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



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






class __A      (  _UpperCAmelCase						):



 '''simple docstring'''







 def __init__(self			,       A			,       A			,       A			,       A			,       A			,       A			,       A			,       )   ->					Union[str, 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__			,       )

      _a     =  transforms.Normalize(mean=feature_extractor.image_mean			,       std=feature_extractor.image_std	)
      _a     =  (
          feature_extractor.size
          if isinstance(feature_extractor.size			,       lowerCAmelCase__	)
          else feature_extractor.size['''shortest_edge''']
      )
      _a     =  MakeCutouts(self.cut_out_size	)

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



 def  a__  (self			,       A = "auto"	)   ->					List[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  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      self.enable_attention_slicing(lowerCAmelCase__	)



 def  a__  (self	)   ->					Optional[Any]:


      """simple docstring"""



      set_requires_grad(self.vae			,       lowerCAmelCase__	)



 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      set_requires_grad(self.vae			,       lowerCAmelCase__	)



 def  a__  (self	)   ->					Any:


      """simple docstring"""



      set_requires_grad(self.unet			,       lowerCAmelCase__	)



 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      set_requires_grad(self.unet			,       lowerCAmelCase__	)



 @torch.enable_grad()
 def  a__  (self			,       A			,       A			,       A			,       A			,       A			,       A			,       A			,       A			,       A=True			,       )   ->					Any:


      """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'''	)

      _a     =  1 / self.vae.config.scaling_factor * sample
      _a     =  self.vae.decode(lowerCAmelCase__	).sample
      _a     =  (image / 2 + 0.5).clamp(0			,       1	)

      if use_cutouts:
           _a     =  self.make_cutouts(lowerCAmelCase__			,       lowerCAmelCase__	)
      else:
           _a     =  transforms.Resize(self.cut_out_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__	)

      if use_cutouts:
           _a     =  spherical_dist_loss(lowerCAmelCase__			,       lowerCAmelCase__	)
           _a     =  dists.view([num_cutouts, sample.shape[0], -1]	)
           _a     =  dists.sum(2	).mean(0	).sum() * clip_guidance_scale
      else:
           _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			,       A			,       A = 512			,       A = 512			,       A = 50			,       A = 7.5			,       A = 1			,       A = 0.0			,       A = 100			,       A = None			,       A = 4			,       A = True			,       A = None			,       A = None			,       A = "pil"			,       A = True			,       )   ->					List[Any]:


      """simple docstring"""



      if isinstance(lowerCAmelCase__			,       lowerCAmelCase__	):
           _a     =  1
      elif isinstance(lowerCAmelCase__			,       lowerCAmelCase__	):
           _a     =  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
      _a     =  self.tokenizer(
          lowerCAmelCase__			,       padding='''max_length'''			,       max_length=self.tokenizer.model_max_length			,       truncation=lowerCAmelCase__			,       return_tensors='''pt'''			,       )
      _a     =  self.text_encoder(text_input.input_ids.to(self.device	)	)[0]
      # duplicate text embeddings for each generation per prompt
      _a     =  text_embeddings.repeat_interleave(lowerCAmelCase__			,       dim=0	)

      if clip_guidance_scale > 0:
           if clip_prompt is not None:
                _a     =  self.tokenizer(
                    lowerCAmelCase__			,       padding='''max_length'''			,       max_length=self.tokenizer.model_max_length			,       truncation=lowerCAmelCase__			,       return_tensors='''pt'''			,       ).input_ids.to(self.device	)
           else:
                _a     =  text_input.input_ids.to(self.device	)
           _a     =  self.clip_model.get_text_features(lowerCAmelCase__	)
           _a     =  text_embeddings_clip / text_embeddings_clip.norm(p=2			,       dim=-1			,       keepdim=lowerCAmelCase__	)
           # duplicate text embeddings clip for each generation per prompt
           _a     =  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.
      _a     =  guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
           _a     =  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 * num_images_per_prompt, 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	)

      # 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
      _a     =  self.scheduler.timesteps.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

      for i, t in enumerate(self.progress_bar(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__			,       lowerCAmelCase__			,       lowerCAmelCase__			,       )

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

      # scale and decode the image latents with vae
      _a     =  1 / self.vae.config.scaling_factor * 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__	)








'''simple docstring'''





import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

lowercase_			=  0.25 + 0.0J
lowercase_			=  -0.4 + 0.6J
lowercase_			=  -0.1 + 0.651J
lowercase_			=  -2.0
lowercase_			=  56
lowercase_			=  2.0
lowercase_			=  666




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     return numpy.exp(__A) + c_parameter




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     return z_values * z_values + c_parameter




def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



     _a     =  numpy.linspace(-window_size  ,     __A  ,     __A)
     _a     =  x.reshape((nb_pixels, 1))
     _a     =  numpy.linspace(-window_size  ,     __A  ,     __A)
     _a     =  y.reshape((1, nb_pixels))
     return x + 1.0J * y




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A  ,     __A = None  ,     ):



     """simple docstring"""



     _a     =  z_a.astype('''complex64''')
     for _ in range(__A):
          _a     =  eval_function(__A  ,     __A)
          if infinity is not None:
               numpy.nan_to_num(__A  ,     copy=__A  ,     nan=__A)
               _a     =  infinity
     return z_n




def lowerCAmelCase  (__A  ,     __A  ,     __A  ,     __A  ,     ):



     """simple docstring"""



     _a     =  (abs(__A)).transpose()
     _a     =  abs_z_final[::-1, :]
     pyplot.matshow(abs_z_final < escape_radius)
     pyplot.title(F'''Julia set of ${function_label}$, $c={function_params}$''')
     pyplot.show()




def lowerCAmelCase  ():



     """simple docstring"""



     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''overflow encountered in multiply''')
     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''invalid value encountered in multiply'''  ,     )
     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''overflow encountered in absolute''')
     warnings.filterwarnings(
         '''ignore'''  ,     category=__A  ,     message='''overflow encountered in exp''')


if __name__ == "__main__":
  lowercase_			=  prepare_grid(window_size, nb_pixels)

  ignore_overflow_warnings()  # See file header for explanations

  lowercase_			=  24
  lowercase_			=  2 * abs(c_cauliflower) + 1
  lowercase_			=  iterate_function(
      eval_quadratic_polynomial,
      c_cauliflower,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("z^2+c", c_cauliflower, escape_radius, z_final)

  lowercase_			=  64
  lowercase_			=  2 * abs(c_polynomial_a) + 1
  lowercase_			=  iterate_function(
      eval_quadratic_polynomial,
      c_polynomial_a,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

  lowercase_			=  161
  lowercase_			=  2 * abs(c_polynomial_a) + 1
  lowercase_			=  iterate_function(
      eval_quadratic_polynomial,
      c_polynomial_a,
      nb_iterations,
      z_a,
      infinity=1.1 * escape_radius,
  )
  show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

  lowercase_			=  12
  lowercase_			=  1_0000.0
  lowercase_			=  iterate_function(
      eval_exponential,
      c_exponential,
      nb_iterations,
      z_a + 2,
      infinity=1.0e10,
  )
  show_results("e^z+c", c_exponential, escape_radius, z_final)



