





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 			__A(    _a			):

     def __init__(   self,							A,							A,							A,							A,							A,							):





            """simple docstring"""

            super().__init__()

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

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            with torch.no_grad():
                   _UpperCamelCase														=    self.vae.encode(tfms.ToTensor()(snake_case_   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
                   _UpperCamelCase														=    0.18_215 * latent.latent_dist.sample()
            return latent

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    (1 / 0.18_215) * latent
            with torch.no_grad():
                   _UpperCamelCase														=    self.vae.decode(snake_case_   ).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 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    self.tokenizer(
                snake_case_,							padding='''max_length''',							max_length=self.tokenizer.model_max_length,							truncation=snake_case_,							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=snake_case_,							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,							A,							A,							A = 0.3,							A = 0.6,							A = 0.5,							A = 42,							A = 50,							A = 7.5,							):





            """simple docstring"""

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

            _UpperCamelCase														=    self.prep_text(snake_case_   )

            self.scheduler.set_timesteps(snake_case_   )

            _UpperCamelCase							,		_UpperCamelCase														=    img.size
            _UpperCamelCase														=    self.encode(snake_case_   )

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

            _UpperCamelCase														=    self.scheduler.add_noise(
                snake_case_,							snake_case_,							timesteps=self.scheduler.timesteps[tmax],							)

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

            _UpperCamelCase														=    self.scheduler.scale_model_input(snake_case_,							self.scheduler.timesteps[tmax]   )

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

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

            _UpperCamelCase														=    self.scheduler.step(snake_case_,							self.scheduler.timesteps[tmax],							snake_case_   ).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(
                                     snake_case_,							snake_case_,							timesteps=snake_case_,							)

                                 _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(snake_case_,							snake_case_   )

                          with torch.no_grad():
                                 _UpperCamelCase														=    self.unet(
                                     snake_case_,							snake_case_,							encoder_hidden_states=snake_case_,							).sample

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

                          _UpperCamelCase														=    self.scheduler.step(snake_case_,							snake_case_,							snake_case_   ).prev_sample

            return self.decode(snake_case_   )














"""simple docstring"""





def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE = 10_00       )       ->						str:
    """simple docstring"""

    if (not isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )) or (digit_position <= 0):
        raise ValueError('Digit position must be a positive integer'       )
    elif (not isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )) or (precision < 0):
        raise ValueError('Precision must be a nonnegative integer'       )

    # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
    # accurate
    UpperCamelCase__								=     (
        4 * _subsum(SCREAMING_SNAKE_CASE			,						1			,						SCREAMING_SNAKE_CASE       )
        - 2 * _subsum(SCREAMING_SNAKE_CASE			,						4			,						SCREAMING_SNAKE_CASE       )
        - _subsum(SCREAMING_SNAKE_CASE			,						5			,						SCREAMING_SNAKE_CASE       )
        - _subsum(SCREAMING_SNAKE_CASE			,						6			,						SCREAMING_SNAKE_CASE       )
    )

    # return the first hex digit of the fractional part of the result
    return hex(int((sum_result % 1) * 16       )       )[2:]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						float:
    """simple docstring"""

    UpperCamelCase__								=     0.0
    for sum_index in range(digit_pos_to_extract + precision       ):
        UpperCamelCase__								=     8 * sum_index + denominator_addend
        if sum_index < digit_pos_to_extract:
            # if the exponential term is an integer and we mod it by the denominator
            # before dividing, only the integer part of the sum will change;
            # the fractional part will not
            UpperCamelCase__								=     pow(
                16			,						digit_pos_to_extract - 1 - sum_index			,						SCREAMING_SNAKE_CASE       )
        else:
            UpperCamelCase__								=     pow(16			,						digit_pos_to_extract - 1 - sum_index       )
        total += exponential_term / denominator
    return total


if __name__ == "__main__":
  import doctest

  doctest.testmod()




