
"""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__		,	)    ->    Optional[Any]:
									"""simple docstring"""







									super().__init__()

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


				def 							A					(				self		,	lowercase__			)    ->    Optional[int]:
									"""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__			)    ->    List[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__			)    ->    Any:
									"""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"""




def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										SCREAMING_SNAKE_CASE				=	(
										    'operation can not be conducted on a object of type '
										    f'''{type(SCREAMING_SNAKE_CASE_			).__name__}'''
										)
										raise TypeError(SCREAMING_SNAKE_CASE_			)
					SCREAMING_SNAKE_CASE				=	''
					for _ in range(0,     3_2			):
										bit_string += str(number % 2			)
										SCREAMING_SNAKE_CASE				=	number >> 1
					return bit_string

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if number < 0:
										raise ValueError('the value of input must be positive'			)
					elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										raise TypeError('Input value must be a \'int\' type'			)
					elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''			)
					SCREAMING_SNAKE_CASE				=	0
					# iterator over [1 to 32],since we are dealing with 32 bit integer
					for _ in range(1,     3_3			):
										# left shift the bits by unity
										SCREAMING_SNAKE_CASE				=	result << 1
										# get the end bit
										SCREAMING_SNAKE_CASE				=	number % 2
										# right shift the bits by unity
										SCREAMING_SNAKE_CASE				=	number >> 1
										# add that bit to our ans
										SCREAMING_SNAKE_CASE				=	result | end_bit
					return get_reverse_bit_string(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
