"""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 			_lowercase		(   __lowerCamelCase  ):

				def __init__(     self       :    Dict       ,    _lowercase       :    AutoencoderKL       ,    _lowercase       :    CLIPTextModel       ,    _lowercase       :    CLIPTokenizer       ,    _lowercase       :    UNetaDConditionModel       ,    _lowercase       :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]       ,    ):


										'''simple docstring'''
										super().__init__()

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

				def        A (     self       :    Optional[Any]       ,    _lowercase       :    Optional[Any]     ):


										'''simple docstring'''
										with torch.no_grad():
																UpperCAmelCase__						=		self.vae.encode(tfms.ToTensor()(_lowercase     ).unsqueeze(0     ).to(self.device     ) * 2 - 1     )
																UpperCAmelCase__						=		0.1_8215 * latent.latent_dist.sample()
										return latent

				def        A (     self       :    Union[str, Any]       ,    _lowercase       :    List[Any]     ):


										'''simple docstring'''
										UpperCAmelCase__						=		(1 / 0.1_8215) * latent
										with torch.no_grad():
																UpperCAmelCase__						=		self.vae.decode(_lowercase     ).sample
										UpperCAmelCase__						=		(img / 2 + 0.5).clamp(0       ,    1     )
										UpperCAmelCase__						=		img.detach().cpu().permute(0       ,    2       ,    3       ,    1     ).numpy()
										UpperCAmelCase__						=		(img * 2_5_5).round().astype("""uint8"""     )
										return Image.fromarray(img[0]     )

				def        A (     self       :    Any       ,    _lowercase       :    Union[str, Any]     ):


										'''simple docstring'''
										UpperCAmelCase__						=		self.tokenizer(
										    _lowercase       ,    padding="""max_length"""       ,    max_length=self.tokenizer.model_max_length       ,    truncation=_lowercase       ,    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=_lowercase       ,    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       :    Tuple       ,    _lowercase       :    Image.Image       ,    _lowercase       :    str       ,    _lowercase       :    float = 0.3       ,    _lowercase       :    float = 0.6       ,    _lowercase       :    float = 0.5       ,    _lowercase       :    int = 4_2       ,    _lowercase       :    int = 5_0       ,    _lowercase       :    float = 7.5       ,    ):


										'''simple docstring'''
										UpperCAmelCase__						=		steps - int(kmin * steps     )
										UpperCAmelCase__						=		steps - int(kmax * steps     )

										UpperCAmelCase__						=		self.prep_text(_lowercase     )

										self.scheduler.set_timesteps(_lowercase     )

										UpperCAmelCase__	,      UpperCAmelCase__						=		img.size
										UpperCAmelCase__						=		self.encode(_lowercase     )

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

										UpperCAmelCase__						=		self.scheduler.add_noise(
										    _lowercase       ,    _lowercase       ,    timesteps=self.scheduler.timesteps[tmax]       ,    )

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

										UpperCAmelCase__						=		self.scheduler.scale_model_input(_lowercase       ,    self.scheduler.timesteps[tmax]     )

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

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

										UpperCAmelCase__						=		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
																												UpperCAmelCase__						=		self.scheduler.add_noise(
																												    _lowercase       ,    _lowercase       ,    timesteps=_lowercase       ,    )

																												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(_lowercase       ,    _lowercase     )

																						with torch.no_grad():
																												UpperCAmelCase__						=		self.unet(
																												    _lowercase       ,    _lowercase       ,    encoder_hidden_states=_lowercase       ,    ).sample

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

																						UpperCAmelCase__						=		self.scheduler.step(_lowercase       ,    _lowercase       ,    _lowercase     ).prev_sample

										return self.decode(_lowercase     )


"""simple docstring"""







from itertools import combinations_with_replacement


def   a__      (		UpperCAmelCase__       :			int = 1_0_0			)->	int:
						UpperCAmelCase__						=		[*list(range(1		,			2_1			)			), 2_5]
						UpperCAmelCase__						=		[2 * x for x in range(1		,			2_1			)] + [5_0]
						UpperCAmelCase__						=		[3 * x for x in range(1		,			2_1			)]
						UpperCAmelCase__						=		singles + doubles + triples + [0]

						UpperCAmelCase__						=		0
						UpperCAmelCase__						=		42
						UpperCAmelCase__						=		42
						UpperCAmelCase__						=		42
						UpperCAmelCase__						=		42

						for double in doubles:
												for throwa, throwa in combinations_with_replacement(UpperCAmelCase__		,			2			):
																		UpperCAmelCase__						=		double + throwa + throwa
																		if checkout_total < limit:
																								num_checkouts += 1

						return num_checkouts


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

