
"""simple docstring"""




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 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__						,      lowercase__						,      lowercase__						,      lowercase__						,      lowercase__						,      )       ->       List[Any]:
									"""simple docstring"""







									super().__init__()

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


				def 				A   (							self						,      lowercase__     )       ->       Any:
									"""simple docstring"""







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


				def 				A   (							self						,      lowercase__     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      (1 / 0.18_215) * latent
									with torch.no_grad():
														SCREAMING_SNAKE_CASE           =      self.vae.decode(lowercase__     ).sample
									SCREAMING_SNAKE_CASE           =      (img / 2 + 0.5).clamp(0						,      1     )
									SCREAMING_SNAKE_CASE           =      img.detach().cpu().permute(0						,      2						,      3						,      1     ).numpy()
									SCREAMING_SNAKE_CASE           =      (img * 255).round().astype('uint8'     )
									return Image.fromarray(img[0]     )


				def 				A   (							self						,      lowercase__     )       ->       Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.tokenizer(
									    lowercase__						,      padding='max_length'						,      max_length=self.tokenizer.model_max_length						,      truncation=lowercase__						,      return_tensors='pt'						,      )

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

									SCREAMING_SNAKE_CASE           =      self.tokenizer(
									    ''						,      padding='max_length'						,      max_length=self.tokenizer.model_max_length						,      truncation=lowercase__						,      return_tensors='pt'						,      )

									SCREAMING_SNAKE_CASE           =      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						,      )       ->       Image.Image:
									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE           =      self.prep_text(lowercase__     )

									self.scheduler.set_timesteps(lowercase__     )

									SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      img.size
									SCREAMING_SNAKE_CASE           =      self.encode(lowercase__     )

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

									SCREAMING_SNAKE_CASE           =      self.scheduler.add_noise(
									    lowercase__						,      lowercase__						,      timesteps=self.scheduler.timesteps[tmax]						,      )

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

									SCREAMING_SNAKE_CASE           =      self.scheduler.scale_model_input(lowercase__						,      self.scheduler.timesteps[tmax]     )

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

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

									SCREAMING_SNAKE_CASE           =      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
																								SCREAMING_SNAKE_CASE           =      self.scheduler.add_noise(
																								    lowercase__						,      lowercase__						,      timesteps=lowercase__						,      )

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

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

																			SCREAMING_SNAKE_CASE           =      self.scheduler.scale_model_input(lowercase__						,      lowercase__     )

																			with torch.no_grad():
																								SCREAMING_SNAKE_CASE           =      self.unet(
																								    lowercase__						,      lowercase__						,      encoder_hidden_states=lowercase__						,      ).sample

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

																			SCREAMING_SNAKE_CASE           =      self.scheduler.step(lowercase__						,      lowercase__						,      lowercase__     ).prev_sample

									return self.decode(lowercase__     )


"""simple docstring"""




from itertools import permutations

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_ = 5     ):
					if gon_side < 3 or gon_side > 5:
										raise ValueError('gon_side must be in the range [3, 5]'     )

					# Since it's 16, we know 10 is on the outer ring
					# Put the big numbers at the end so that they are never the first number
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 1,	0,	-1     )     )
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 2,	gon_side * 2 + 1     )     )

					for perm in permutations(small_numbers + big_numbers     ):
										SCREAMING_SNAKE_CASE           =      generate_gon_ring(SCREAMING_SNAKE_CASE_,	list(SCREAMING_SNAKE_CASE_     )     )
										if is_magic_gon(SCREAMING_SNAKE_CASE_     ):
															return int(''.join(str(SCREAMING_SNAKE_CASE_     ) for n in numbers     )     )

					SCREAMING_SNAKE_CASE           =      f'''Magic {gon_side}-gon ring is impossible'''
					raise ValueError(SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      [0] * (gon_side * 3)
					SCREAMING_SNAKE_CASE           =      perm[0:3]
					perm.append(perm[1]     )

					SCREAMING_SNAKE_CASE           =      1 if gon_side < 5 else 2

					for i in range(1,	len(SCREAMING_SNAKE_CASE_     ) // 3 + magic_number     ):
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 1]
										SCREAMING_SNAKE_CASE           =      result[3 * i - 1]
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 2]

					return result

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if len(SCREAMING_SNAKE_CASE_     ) % 3 != 0:
										raise ValueError('a gon ring should have a length that is a multiple of 3'     )

					if min(numbers[::3]     ) != numbers[0]:
										return False

					SCREAMING_SNAKE_CASE           =      sum(numbers[:3]     )

					return all(sum(numbers[i : i + 3]     ) == total for i in range(3,	len(SCREAMING_SNAKE_CASE_     ),	3     )     )


if __name__ == "__main__":
	print(solution())
