





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   lowercase__     (				__A	):







  def __init__(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    ):
       super().__init__()

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







  def 						UpperCAmelCase__		(						self     ,    _lowercase    ):
       with torch.no_grad():
            lowerCAmelCase_       :							int			 =		self.vae.encode(tfms.ToTensor()(_lowercase    ).unsqueeze(0    ).to(self.device    ) * 2 - 1    )
            lowerCAmelCase_       :							Tuple			 =		0.1_8215 * latent.latent_dist.sample()
       return latent







  def 						UpperCAmelCase__		(						self     ,    _lowercase    ):
       lowerCAmelCase_       :							Any			 =		(1 / 0.1_8215) * latent
       with torch.no_grad():
            lowerCAmelCase_       :							Optional[Any]			 =		self.vae.decode(_lowercase    ).sample
       lowerCAmelCase_       :							Any			 =		(img / 2 + 0.5).clamp(0     ,    1    )
       lowerCAmelCase_       :							int			 =		img.detach().cpu().permute(0     ,    2     ,    3     ,    1    ).numpy()
       lowerCAmelCase_       :							List[Any]			 =		(img * 255).round().astype("""uint8"""    )
       return Image.fromarray(img[0]    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase    ):
       lowerCAmelCase_       :							str			 =		self.tokenizer(
           _lowercase     ,    padding="""max_length"""     ,    max_length=self.tokenizer.model_max_length     ,    truncation=_lowercase     ,    return_tensors="""pt"""     ,    )

       lowerCAmelCase_       :							int			 =		self.text_encoder(text_input.input_ids.to(self.device    )    )[0]

       lowerCAmelCase_       :							Any			 =		self.tokenizer(
           """"""     ,    padding="""max_length"""     ,    max_length=self.tokenizer.model_max_length     ,    truncation=_lowercase     ,    return_tensors="""pt"""     ,    )

       lowerCAmelCase_       :							List[Any]			 =		self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

       return torch.cat([uncond_embedding, text_embedding]    )







  def __call__(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase = 0.3     ,    _lowercase = 0.6     ,    _lowercase = 0.5     ,    _lowercase = 42     ,    _lowercase = 50     ,    _lowercase = 7.5     ,    ):
       lowerCAmelCase_       :							int			 =		steps - int(kmin * steps    )
       lowerCAmelCase_       :							List[str]			 =		steps - int(kmax * steps    )

       lowerCAmelCase_       :							Dict			 =		self.prep_text(_lowercase    )

       self.scheduler.set_timesteps(_lowercase    )

       lowerCAmelCase_ ,	lowerCAmelCase_       :							str			 =		img.size
       lowerCAmelCase_       :							Dict			 =		self.encode(_lowercase    )

       torch.manual_seed(_lowercase    )
       lowerCAmelCase_       :							Any			 =		torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8)     ,    ).to(self.device    )

       lowerCAmelCase_       :							Union[str, Any]			 =		self.scheduler.add_noise(
           _lowercase     ,    _lowercase     ,    timesteps=self.scheduler.timesteps[tmax]     ,    )

       lowerCAmelCase_       :							str			 =		torch.cat([latents] * 2    )

       lowerCAmelCase_       :							Optional[Any]			 =		self.scheduler.scale_model_input(_lowercase     ,    self.scheduler.timesteps[tmax]    )

       with torch.no_grad():
            lowerCAmelCase_       :							Optional[int]			 =		self.unet(
                _lowercase     ,    self.scheduler.timesteps[tmax]     ,    encoder_hidden_states=_lowercase     ,    ).sample

       lowerCAmelCase_ ,	lowerCAmelCase_       :							Any			 =		pred.chunk(2    )
       lowerCAmelCase_       :							Union[str, Any]			 =		pred_uncond + guidance_scale * (pred_text - pred_uncond)

       lowerCAmelCase_       :							Union[str, Any]			 =		self.scheduler.step(_lowercase     ,    self.scheduler.timesteps[tmax]     ,    _lowercase    ).prev_sample

       for i, t in enumerate(tqdm(self.scheduler.timesteps    )    ):
            if i > tmax:
                 if i < tmin:  # layout generation phase
                      lowerCAmelCase_       :							Union[str, Any]			 =		self.scheduler.add_noise(
                          _lowercase     ,    _lowercase     ,    timesteps=_lowercase     ,    )

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

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

                 lowerCAmelCase_       :							Dict			 =		self.scheduler.scale_model_input(_lowercase     ,    _lowercase    )

                 with torch.no_grad():
                      lowerCAmelCase_       :							Optional[Any]			 =		self.unet(
                          _lowercase     ,    _lowercase     ,    encoder_hidden_states=_lowercase     ,    ).sample

                 lowerCAmelCase_ ,	lowerCAmelCase_       :							Union[str, Any]			 =		pred.chunk(2    )
                 lowerCAmelCase_       :							Optional[int]			 =		pred_uncond + guidance_scale * (pred_text - pred_uncond)

                 lowerCAmelCase_       :							List[Any]			 =		self.scheduler.step(_lowercase     ,    _lowercase     ,    _lowercase    ).prev_sample

       return self.decode(_lowercase    )







import math





class   lowercase__     :







  def __init__(						self     ,    _lowercase    ):
       lowerCAmelCase_       :							Optional[Any]			 =		len(_lowercase    )
       lowerCAmelCase_       :							Union[str, Any]			 =		[0] * (
           4 * self.N
       )  # approximate the overall size of segment tree with array N
       self.build(1     ,    0     ,    self.N - 1    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase    ):
       return idx * 2







  def 						UpperCAmelCase__		(						self     ,    _lowercase    ):
       return idx * 2 + 1







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):  # noqa: E741
       if l == r:
            lowerCAmelCase_       :							Dict			 =		A[l]
       else:
            lowerCAmelCase_       :							List[str]			 =		(l + r) // 2
            self.build(self.left(_lowercase    )     ,    _lowercase     ,    _lowercase    )
            self.build(self.right(_lowercase    )     ,    mid + 1     ,    _lowercase    )
            lowerCAmelCase_       :							Dict			 =		max(self.st[self.left(_lowercase    )]     ,    self.st[self.right(_lowercase    )]    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):
       return self.update_recursive(1     ,    0     ,    self.N - 1     ,    a - 1     ,    b - 1     ,    _lowercase    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):  # noqa: E741
       if r < a or l > b:
            return True
       if l == r:
            lowerCAmelCase_       :							List[str]			 =		val
            return True
       lowerCAmelCase_       :							List[str]			 =		(l + r) // 2
       self.update_recursive(self.left(_lowercase    )     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )
       self.update_recursive(self.right(_lowercase    )     ,    mid + 1     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )
       lowerCAmelCase_       :							List[Any]			 =		max(self.st[self.left(_lowercase    )]     ,    self.st[self.right(_lowercase    )]    )
       return True







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase    ):
       return self.query_recursive(1     ,    0     ,    self.N - 1     ,    a - 1     ,    b - 1    )







  def 						UpperCAmelCase__		(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):  # noqa: E741
       if r < a or l > b:
            return -math.inf
       if l >= a and r <= b:
            return self.st[idx]
       lowerCAmelCase_       :							int			 =		(l + r) // 2
       lowerCAmelCase_       :							List[Any]			 =		self.query_recursive(self.left(_lowercase    )     ,    _lowercase     ,    _lowercase     ,    _lowercase     ,    _lowercase    )
       lowerCAmelCase_       :							List[str]			 =		self.query_recursive(self.right(_lowercase    )     ,    mid + 1     ,    _lowercase     ,    _lowercase     ,    _lowercase    )
       return max(_lowercase     ,    _lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							List[Any]			 =		[]
       for i in range(1     ,    N + 1    ):
            show_list += [self.query(_lowercase     ,    _lowercase    )]
       print(_lowercase    )


if __name__ == "__main__":
      UpperCAmelCase_       : Any							=    [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
      UpperCAmelCase_       : Optional[Any]							=    15
      UpperCAmelCase_       : List[Any]							=    SegmentTree(A)
      print(segt.query(4, 6))
      print(segt.query(7, 11))
      print(segt.query(7, 12))
      segt.update(1, 3, 1_11)
      print(segt.query(1, 15))
      segt.update(7, 8, 2_35)
      segt.show_data()
