

'''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    (	a_			):


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					Optional[int]:
								super().__init__()

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


							def     _snake_case				(      self     ,      _snake_case)				->					List[Any]:
								with torch.no_grad():
									UpperCAmelCase_		:   Any													=       self.vae.encode(tfms.ToTensor()(_snake_case).unsqueeze(0).to(self.device) * 2 - 1)
									UpperCAmelCase_		:   int													=       0.18_215 * latent.latent_dist.sample()
								return latent


							def     _snake_case				(      self     ,      _snake_case)				->					Optional[int]:
								UpperCAmelCase_		:   Union[str, Any]													=       (1 / 0.18_215) * latent
								with torch.no_grad():
									UpperCAmelCase_		:   Tuple													=       self.vae.decode(_snake_case).sample
								UpperCAmelCase_		:   str													=       (img / 2 + 0.5).clamp(0     ,      1)
								UpperCAmelCase_		:   List[str]													=       img.detach().cpu().permute(0     ,      2     ,      3     ,      1).numpy()
								UpperCAmelCase_		:   List[Any]													=       (img * 255).round().astype('uint8')
								return Image.fromarray(img[0])


							def     _snake_case				(      self     ,      _snake_case)				->					int:
								UpperCAmelCase_		:   int													=       self.tokenizer(
								    _snake_case     ,      padding='max_length'     ,      max_length=self.tokenizer.model_max_length     ,      truncation=_snake_case     ,      return_tensors='pt'     ,      )

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

								UpperCAmelCase_		:   List[Any]													=       self.tokenizer(
								    ''     ,      padding='max_length'     ,      max_length=self.tokenizer.model_max_length     ,      truncation=_snake_case     ,      return_tensors='pt'     ,      )

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

								return torch.cat([uncond_embedding, text_embedding])





							def __call__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case = 0.3     ,      _snake_case = 0.6     ,      _snake_case = 0.5     ,      _snake_case = 42     ,      _snake_case = 50     ,      _snake_case = 7.5     ,      )				->					Image.Image:
								UpperCAmelCase_		:   int													=       steps - int(kmin * steps)
								UpperCAmelCase_		:   List[str]													=       steps - int(kmax * steps)

								UpperCAmelCase_		:   Optional[Any]													=       self.prep_text(_snake_case)

								self.scheduler.set_timesteps(_snake_case)

								UpperCAmelCase_						,     UpperCAmelCase_		:   Any													=       img.size
								UpperCAmelCase_		:   Optional[Any]													=       self.encode(_snake_case)

								torch.manual_seed(_snake_case)
								UpperCAmelCase_		:   Union[str, Any]													=       torch.randn(
								    (1, self.unet.config.in_channels, height // 8, width // 8)     ,      ).to(self.device)

								UpperCAmelCase_		:   Optional[Any]													=       self.scheduler.add_noise(
								    _snake_case     ,      _snake_case     ,      timesteps=self.scheduler.timesteps[tmax]     ,      )

								UpperCAmelCase_		:   Any													=       torch.cat([latents] * 2)

								UpperCAmelCase_		:   Optional[Any]													=       self.scheduler.scale_model_input(_snake_case     ,      self.scheduler.timesteps[tmax])

								with torch.no_grad():
									UpperCAmelCase_		:   int													=       self.unet(
									    _snake_case     ,      self.scheduler.timesteps[tmax]     ,      encoder_hidden_states=_snake_case     ,      ).sample

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

								UpperCAmelCase_		:   Tuple													=       self.scheduler.step(_snake_case     ,      self.scheduler.timesteps[tmax]     ,      _snake_case).prev_sample

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

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

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

										UpperCAmelCase_		:   Tuple													=       self.scheduler.scale_model_input(_snake_case     ,      _snake_case)

										with torch.no_grad():
											UpperCAmelCase_		:   str													=       self.unet(
											    _snake_case     ,      _snake_case     ,      encoder_hidden_states=_snake_case     ,      ).sample

										UpperCAmelCase_						,     UpperCAmelCase_		:   str													=       pred.chunk(2)
										UpperCAmelCase_		:   Any													=       pred_uncond + guidance_scale * (pred_text - pred_uncond)

										UpperCAmelCase_		:   Union[str, Any]													=       self.scheduler.step(_snake_case     ,      _snake_case     ,      _snake_case).prev_sample

								return self.decode(_snake_case)









'''simple docstring'''


from __future__ import annotations







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     int:

	# Base Case
	if counter == 0 or capacity == 0:
		return 0

	# If weight of the nth item is more than Knapsack of capacity,
	#   then this item cannot be included in the optimal solution,
	# else return the maximum of two cases:
	#   (1) nth item included
	#   (2) not included
	if weights[counter - 1] > capacity:
		return knapsack(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,counter - 1	)
	else:
		UpperCAmelCase_		:   int													=       capacity - weights[counter - 1]
		UpperCAmelCase_		:   Optional[int]													=       values[counter - 1] + knapsack(
		    UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,counter - 1	)
		UpperCAmelCase_		:   Any													=       knapsack(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,counter - 1	)
		return max(UpperCamelCase ,UpperCamelCase	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()






