


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)



class   __lowerCamelCase (A__	):







       def __init__( self:   Optional[int],A_:   AutoencoderKL,A_:   CLIPTextModel,A_:   CLIPTokenizer,A_:   UNetaDConditionModel,A_:   Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler],):






            '''simple docstring'''




            super().__init__()

            self.register_modules(vae=A_,text_encoder=A_,tokenizer=A_,unet=A_,scheduler=A_				)







       def 		snake_case_  ( self:   Dict,A_:   Dict				):






            '''simple docstring'''




            with torch.no_grad():
                 __UpperCamelCase   = self.vae.encode(tfms.ToTensor()(A_				).unsqueeze(0				).to(self.device				) * 2 - 1				)
                 __UpperCamelCase   = 0.1_8_2_1_5 * latent.latent_dist.sample()
            return latent







       def 		snake_case_  ( self:   Any,A_:   Any				):






            '''simple docstring'''




            __UpperCamelCase   = (1 / 0.1_8_2_1_5) * latent
            with torch.no_grad():
                 __UpperCamelCase   = self.vae.decode(A_				).sample
            __UpperCamelCase   = (img / 2 + 0.5).clamp(0,1				)
            __UpperCamelCase   = img.detach().cpu().permute(0,2,3,1				).numpy()
            __UpperCamelCase   = (img * 255).round().astype('uint8'				)
            return Image.fromarray(img[0]				)







       def 		snake_case_  ( self:   List[str],A_:   str				):






            '''simple docstring'''




            __UpperCamelCase   = self.tokenizer(
                A_,padding='max_length',max_length=self.tokenizer.model_max_length,truncation=A_,return_tensors='pt',)

            __UpperCamelCase   = self.text_encoder(text_input.input_ids.to(self.device				)				)[0]

            __UpperCamelCase   = self.tokenizer(
                '',padding='max_length',max_length=self.tokenizer.model_max_length,truncation=A_,return_tensors='pt',)

            __UpperCamelCase   = self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]

            return torch.cat([uncond_embedding, text_embedding]				)







       def __call__( self:   List[Any],A_:   Image.Image,A_:   str,A_:   float = 0.3,A_:   float = 0.6,A_:   float = 0.5,A_:   int = 42,A_:   int = 50,A_:   float = 7.5,):






            '''simple docstring'''




            __UpperCamelCase   = steps - int(kmin * steps				)
            __UpperCamelCase   = steps - int(kmax * steps				)

            __UpperCamelCase   = self.prep_text(A_				)

            self.scheduler.set_timesteps(A_				)

            __UpperCamelCase   = img.size
            __UpperCamelCase   = self.encode(A_				)

            torch.manual_seed(A_				)
            __UpperCamelCase   = torch.randn(
                (1, self.unet.config.in_channels, height // 8, width // 8),).to(self.device				)

            __UpperCamelCase   = self.scheduler.add_noise(
                A_,A_,timesteps=self.scheduler.timesteps[tmax],)

            __UpperCamelCase   = torch.cat([latents] * 2				)

            __UpperCamelCase   = self.scheduler.scale_model_input(A_,self.scheduler.timesteps[tmax]				)

            with torch.no_grad():
                 __UpperCamelCase   = self.unet(
                     A_,self.scheduler.timesteps[tmax],encoder_hidden_states=A_,).sample

            __UpperCamelCase   = pred.chunk(2				)
            __UpperCamelCase   = pred_uncond + guidance_scale * (pred_text - pred_uncond)

            __UpperCamelCase   = self.scheduler.step(A_,self.scheduler.timesteps[tmax],A_				).prev_sample

            for i, t in enumerate(tqdm(self.scheduler.timesteps				)				):
                 if i > tmax:
                      if i < tmin:  # layout generation phase
                           __UpperCamelCase   = self.scheduler.add_noise(
                               A_,A_,timesteps=A_,)

                           __UpperCamelCase   = (mix_factor * latents) + (
                               1 - mix_factor
                           ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                           __UpperCamelCase   = torch.cat([input] * 2				)

                      else:  # content generation phase
                           __UpperCamelCase   = torch.cat([latents] * 2				)

                      __UpperCamelCase   = self.scheduler.scale_model_input(A_,A_				)

                      with torch.no_grad():
                           __UpperCamelCase   = self.unet(
                               A_,A_,encoder_hidden_states=A_,).sample

                      __UpperCamelCase   = pred.chunk(2				)
                      __UpperCamelCase   = pred_uncond + guidance_scale * (pred_text - pred_uncond)

                      __UpperCamelCase   = self.scheduler.step(A_,A_,A_				).prev_sample

            return self.decode(A_				)




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

import numpy
from matplotlib import pyplot

__snake_case             =	0.25 + 0.0j
__snake_case             =	-0.4 + 0.6j
__snake_case             =	-0.1 + 0.6_51j
__snake_case             =	-2.0
__snake_case             =	5_6
__snake_case             =	2.0
__snake_case             =	6_6_6






def 		_A		(		_lowercase				,     _lowercase   )				->   numpy.ndarray:


     """simple docstring"""


     return numpy.exp(_lowercase   ) + c_parameter






def 		_A		(		_lowercase				,     _lowercase   )				->   numpy.ndarray:


     """simple docstring"""


     return z_values * z_values + c_parameter






def 		_A		(		_lowercase				,     _lowercase   )				->   numpy.ndarray:


     """simple docstring"""


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






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase = None				,     )				->   numpy.ndarray:


     """simple docstring"""


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






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     )				->   None:


     """simple docstring"""


     __UpperCamelCase   = (abs(_lowercase   )).transpose()
     __UpperCamelCase   = 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 		_A		(		)				->   None:


     """simple docstring"""


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


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

    ignore_overflow_warnings()  # See file header for explanations

    __snake_case             =	2_4
    __snake_case             =	2 * abs(c_cauliflower) + 1
    __snake_case             =	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)

    __snake_case             =	6_4
    __snake_case             =	2 * abs(c_polynomial_a) + 1
    __snake_case             =	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)

    __snake_case             =	1_6_1
    __snake_case             =	2 * abs(c_polynomial_a) + 1
    __snake_case             =	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)

    __snake_case             =	1_2
    __snake_case             =	10_000.0
    __snake_case             =	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)


