



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__  (					UpperCAmelCase__):



	"""simple docstring"""

	def __init__(						self				:				Tuple ,	__lowerCAmelCase				:				Tuple ,	__lowerCAmelCase				:				List[str] ,	__lowerCAmelCase				:				str ,	__lowerCAmelCase				:				str ,	__lowerCAmelCase				:				str ,	)      ->  Union[str, Any]:
				super().__init__()

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

	def 						snake_case_			(						self				:				Optional[Any] ,	__lowerCAmelCase				:				str			)      ->  str:
				with torch.no_grad():
							_A						     =					self.vae.encode(tfms.ToTensor()(lowerCamelCase__			).unsqueeze(0			).to(self.device			) * 2 - 1			)
							_A						     =					0.1_8215 * latent.latent_dist.sample()
				return latent

	def 						snake_case_			(						self				:				int ,	__lowerCAmelCase				:				Optional[int]			)      ->  Dict:
				_A						     =					(1 / 0.1_8215) * latent
				with torch.no_grad():
							_A						     =					self.vae.decode(lowerCamelCase__			).sample
				_A						     =					(img / 2 + 0.5).clamp(0 ,	1			)
				_A						     =					img.detach().cpu().permute(0 ,	2 ,	3 ,	1			).numpy()
				_A						     =					(img * 2_55).round().astype('''uint8'''			)
				return Image.fromarray(img[0]			)

	def 						snake_case_			(						self				:				List[str] ,	__lowerCAmelCase				:				List[Any]			)      ->  List[str]:
				_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]

				_A						     =					self.tokenizer(
				    '''''' ,	padding='''max_length''' ,	max_length=self.tokenizer.model_max_length ,	truncation=lowerCamelCase__ ,	return_tensors='''pt''' ,	)

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

				return torch.cat([uncond_embedding, text_embedding]			)

	def __call__(						self				:				Optional[Any] ,	__lowerCAmelCase				:				Any ,	__lowerCAmelCase				:				Any ,	__lowerCAmelCase				:				List[Any] = 0.3 ,	__lowerCAmelCase				:				List[str] = 0.6 ,	__lowerCAmelCase				:				Dict = 0.5 ,	__lowerCAmelCase				:				List[str] = 42 ,	__lowerCAmelCase				:				Dict = 50 ,	__lowerCAmelCase				:				Any = 7.5 ,	)      ->  Tuple:
				_A						     =					steps - int(kmin * steps			)
				_A						     =					steps - int(kmax * steps			)

				_A						     =					self.prep_text(lowerCamelCase__			)

				self.scheduler.set_timesteps(lowerCamelCase__			)

				_A						     =					img.size
				_A						     =					self.encode(lowerCamelCase__			)

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

				_A						     =					self.scheduler.add_noise(
				    lowerCamelCase__ ,	lowerCamelCase__ ,	timesteps=self.scheduler.timesteps[tmax] ,	)

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

				_A						     =					self.scheduler.scale_model_input(lowerCamelCase__ ,	self.scheduler.timesteps[tmax]			)

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

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

				_A						     =					self.scheduler.step(lowerCamelCase__ ,	self.scheduler.timesteps[tmax] ,	lowerCamelCase__			).prev_sample

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

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

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

										_A						     =					self.scheduler.scale_model_input(lowerCamelCase__ ,	lowerCamelCase__			)

										with torch.no_grad():
													_A						     =					self.unet(
													    lowerCamelCase__ ,	lowerCamelCase__ ,	encoder_hidden_states=lowerCamelCase__ ,	).sample

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

										_A						     =					self.scheduler.step(lowerCamelCase__ ,	lowerCamelCase__ ,	lowerCamelCase__			).prev_sample

				return self.decode(lowerCamelCase__			)








import math
from decimal import Decimal, getcontext

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :int = 4_000_000							)     ->    int:

   try:
      _A						     =					int(_snake_case							)
   except (TypeError, ValueError):
      raise TypeError('''Parameter n must be int or castable to int.'''							)
   if n <= 0:
      raise ValueError('''Parameter n must be greater than or equal to one.'''							)
   _A						     =					100
   _A						     =					(Decimal(5							) ** Decimal(0.5							) + 1) / Decimal(2							)

   _A						     =					(math.floor(math.log(n * (phi + 2)  ,      _snake_case							) - 1							) // 3) * 3 + 2
   _A						     =					Decimal(round(phi ** Decimal(index + 1							)							)							) / (phi + 2)
   _A						     =					num // 2
   return int(_snake_case							)


if __name__ == "__main__":
   print(f'{solution() = }')



