






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      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     AutoencoderKL							,     SCREAMING_SNAKE_CASE      :     CLIPTextModel							,     SCREAMING_SNAKE_CASE      :     CLIPTokenizer							,     SCREAMING_SNAKE_CASE      :     UNetaDConditionModel							,     SCREAMING_SNAKE_CASE      :     Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]							,     ):


      '''simple docstring'''






      super().__init__()

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







 def       __lowercase      (	self      :     Dict							,     SCREAMING_SNAKE_CASE      :     Dict		):


      '''simple docstring'''






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







 def       __lowercase      (	self      :     Any							,     SCREAMING_SNAKE_CASE      :     Any		):


      '''simple docstring'''






      UpperCamelCase__    :     List[Any]        = (1 / 0.1_8_2_1_5) * latent
      with torch.no_grad():
           UpperCamelCase__    :     Tuple        = self.vae.decode(SCREAMING_SNAKE_CASE		).sample
      UpperCamelCase__    :     List[Any]        = (img / 2 + 0.5).clamp(0							,     1		)
      UpperCamelCase__    :     Optional[Any]        = img.detach().cpu().permute(0							,     2							,     3							,     1		).numpy()
      UpperCamelCase__    :     Tuple        = (img * 2_55).round().astype("uint8"		)
      return Image.fromarray(img[0]		)







 def       __lowercase      (	self      :     List[str]							,     SCREAMING_SNAKE_CASE      :     str		):


      '''simple docstring'''






      UpperCamelCase__    :     List[Any]        = self.tokenizer(
          SCREAMING_SNAKE_CASE							,     padding="max_length"							,     max_length=self.tokenizer.model_max_length							,     truncation=SCREAMING_SNAKE_CASE							,     return_tensors="pt"							,     )

      UpperCamelCase__    :     List[Any]        = self.text_encoder(text_input.input_ids.to(self.device		)		)[0]

      UpperCamelCase__    :     Union[str, Any]        = self.tokenizer(
          ""							,     padding="max_length"							,     max_length=self.tokenizer.model_max_length							,     truncation=SCREAMING_SNAKE_CASE							,     return_tensors="pt"							,     )

      UpperCamelCase__    :     Dict        = self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

      return torch.cat([uncond_embedding, text_embedding]		)







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


      '''simple docstring'''






      UpperCamelCase__    :     Dict        = steps - int(kmin * steps		)
      UpperCamelCase__    :     Optional[int]        = steps - int(kmax * steps		)

      UpperCamelCase__    :     List[str]        = self.prep_text(SCREAMING_SNAKE_CASE		)

      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     Optional[int]        = img.size
      UpperCamelCase__    :     str        = self.encode(SCREAMING_SNAKE_CASE		)

      torch.manual_seed(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Optional[int]        = torch.randn(
          (1, self.unet.config.in_channels, height // 8, width // 8)							,     ).to(self.device		)

      UpperCamelCase__    :     List[Any]        = self.scheduler.add_noise(
          SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     timesteps=self.scheduler.timesteps[tmax]							,     )

      UpperCamelCase__    :     Tuple        = torch.cat([latents] * 2		)

      UpperCamelCase__    :     Optional[int]        = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE							,     self.scheduler.timesteps[tmax]		)

      with torch.no_grad():
           UpperCamelCase__    :     str        = self.unet(
               SCREAMING_SNAKE_CASE							,     self.scheduler.timesteps[tmax]							,     encoder_hidden_states=SCREAMING_SNAKE_CASE							,     ).sample

      UpperCamelCase__    :     int        = pred.chunk(2		)
      UpperCamelCase__    :     Optional[Any]        = pred_uncond + guidance_scale * (pred_text - pred_uncond)

      UpperCamelCase__    :     List[str]        = self.scheduler.step(SCREAMING_SNAKE_CASE							,     self.scheduler.timesteps[tmax]							,     SCREAMING_SNAKE_CASE		).prev_sample

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

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

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

                UpperCamelCase__    :     Union[str, Any]        = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)

                with torch.no_grad():
                     UpperCamelCase__    :     Optional[int]        = self.unet(
                         SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     encoder_hidden_states=SCREAMING_SNAKE_CASE							,     ).sample

                UpperCamelCase__    :     Optional[int]        = pred.chunk(2		)
                UpperCamelCase__    :     Optional[Any]        = pred_uncond + guidance_scale * (pred_text - pred_uncond)

                UpperCamelCase__    :     List[str]        = self.scheduler.step(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		).prev_sample

      return self.decode(SCREAMING_SNAKE_CASE		)







from __future__ import annotations







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			list[int]:
     if not list_of_ints:
          return []

     if min(__lowerCAmelCase    ) < 0:
          raise ValueError("All numbers must be positive"    )

     UpperCamelCase__    :     Dict        =						max(len(bin(__lowerCAmelCase    )[2:]    ) for x in list_of_ints    )
     return _msd_radix_sort(__lowerCAmelCase      ,					__lowerCAmelCase    )







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			list[int]:
     if bit_position == 0 or len(__lowerCAmelCase    ) in [0, 1]:
          return list_of_ints

     UpperCamelCase__    :     Union[str, Any]        =						[]
     UpperCamelCase__    :     Dict        =						[]
     # Split numbers based on bit at bit_position from the right
     for number in list_of_ints:
          if (number >> (bit_position - 1)) & 1:
               # number has a one at bit bit_position
               ones.append(__lowerCAmelCase    )
          else:
               # number has a zero at bit bit_position
               zeros.append(__lowerCAmelCase    )

    # recursively split both lists further
     UpperCamelCase__    :     str        =						_msd_radix_sort(__lowerCAmelCase      ,					bit_position - 1    )
     UpperCamelCase__    :     Optional[Any]        =						_msd_radix_sort(__lowerCAmelCase      ,					bit_position - 1    )

     # recombine lists
     UpperCamelCase__    :     Optional[int]        =						zeros
     res.extend(__lowerCAmelCase    )

     return res







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			Dict:
     UpperCamelCase__    :     Optional[int]        =						len(__lowerCAmelCase    )
     if not list_of_ints or length == 1:
          return

     if min(__lowerCAmelCase    ) < 0:
          raise ValueError("All numbers must be positive"    )

     UpperCamelCase__    :     int        =						max(len(bin(__lowerCAmelCase    )[2:]    ) for x in list_of_ints    )
     _msd_radix_sort_inplace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					0      ,					__lowerCAmelCase    )







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )				->			List[str]:
     if bit_position == 0 or end_index - begin_index <= 1:
          return

     bit_position -= 1

     UpperCamelCase__    :     Any        =						begin_index
     UpperCamelCase__    :     List[str]        =						end_index - 1
     while i <= j:
          UpperCamelCase__    :     Tuple        =						False
          if not (list_of_ints[i] >> bit_position) & 1:
               # found zero at the beginning
               i += 1
               UpperCamelCase__    :     Any        =						True
          if (list_of_ints[j] >> bit_position) & 1:
               # found one at the end
               j -= 1
               UpperCamelCase__    :     Dict        =						True

          if changed:
               continue

          UpperCamelCase__       ,  UpperCamelCase__    :     Any        =						list_of_ints[j], list_of_ints[i]
          j -= 1
          if j != i:
               i += 1

     _msd_radix_sort_inplace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )
     _msd_radix_sort_inplace(__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase    )


if __name__ == "__main__":
 import doctest

 doctest.testmod()