




"""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   (     __UpperCAmelCase):







				def __init__(				self :       List[str]   ,  _lowerCamelCase :       AutoencoderKL   ,  _lowerCamelCase :       CLIPTextModel   ,  _lowerCamelCase :       CLIPTokenizer   ,  _lowerCamelCase :       UNetaDConditionModel   ,  _lowerCamelCase :       Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]   ,  ):



					"""simple docstring"""

					super().__init__()

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







				def        a_	(				self :       Dict   ,  _lowerCamelCase :       Union[str, Any]							):



					"""simple docstring"""

					with torch.no_grad():
						A_	:					Optional[Any]						 =		self.vae.encode(tfms.ToTensor()(_lowerCamelCase							).unsqueeze(0							).to(self.device							) * 2 - 1							)
						A_	:					str						 =		0.18215 * latent.latent_dist.sample()
					return latent







				def        a_	(				self :       Any   ,  _lowerCamelCase :       Tuple							):



					"""simple docstring"""

					A_	:					List[str]						 =		(1 / 0.18215) * latent
					with torch.no_grad():
						A_	:					int						 =		self.vae.decode(_lowerCamelCase							).sample
					A_	:					List[Any]						 =		(img / 2 + 0.5).clamp(0   ,  1							)
					A_	:					Dict						 =		img.detach().cpu().permute(0   ,  2   ,  3   ,  1							).numpy()
					A_	:					Dict						 =		(img * 2_55).round().astype('''uint8'''							)
					return Image.fromarray(img[0]							)







				def        a_	(				self :       str   ,  _lowerCamelCase :       str							):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		self.tokenizer(
					    _lowerCamelCase   ,  padding='''max_length'''   ,  max_length=self.tokenizer.model_max_length   ,  truncation=_lowerCamelCase   ,  return_tensors='''pt'''   ,  )

					A_	:					int						 =		self.text_encoder(text_input.input_ids.to(self.device							)							)[0]

					A_	:					str						 =		self.tokenizer(
					    ''''''   ,  padding='''max_length'''   ,  max_length=self.tokenizer.model_max_length   ,  truncation=_lowerCamelCase   ,  return_tensors='''pt'''   ,  )

					A_	:					str						 =		self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

					return torch.cat([uncond_embedding, text_embedding]							)







				def __call__(				self :       Dict   ,  _lowerCamelCase :       Image.Image   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       float = 0.3   ,  _lowerCamelCase :       float = 0.6   ,  _lowerCamelCase :       float = 0.5   ,  _lowerCamelCase :       int = 42   ,  _lowerCamelCase :       int = 50   ,  _lowerCamelCase :       float = 7.5   ,  ):



					"""simple docstring"""

					A_	:					Optional[Any]						 =		steps - int(kmin * steps							)
					A_	:					Optional[Any]						 =		steps - int(kmax * steps							)

					A_	:					List[Any]						 =		self.prep_text(_lowerCamelCase							)

					self.scheduler.set_timesteps(_lowerCamelCase							)

					A_ ,			A_	:					Dict						 =		img.size
					A_	:					int						 =		self.encode(_lowerCamelCase							)

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

					A_	:					Dict						 =		self.scheduler.add_noise(
					    _lowerCamelCase   ,  _lowerCamelCase   ,  timesteps=self.scheduler.timesteps[tmax]   ,  )

					A_	:					Dict						 =		torch.cat([latents] * 2							)

					A_	:					int						 =		self.scheduler.scale_model_input(_lowerCamelCase   ,  self.scheduler.timesteps[tmax]							)

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

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

					A_	:					List[Any]						 =		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_	:					Any						 =		self.scheduler.add_noise(
								    _lowerCamelCase   ,  _lowerCamelCase   ,  timesteps=_lowerCamelCase   ,  )

								A_	:					Optional[Any]						 =		(mix_factor * latents) + (
								    1 - mix_factor
								) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
								A_	:					Union[str, Any]						 =		torch.cat([input] * 2							)

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

							A_	:					List[Any]						 =		self.scheduler.scale_model_input(_lowerCamelCase   ,  _lowerCamelCase							)

							with torch.no_grad():
								A_	:					int						 =		self.unet(
								    _lowerCamelCase   ,  _lowerCamelCase   ,  encoder_hidden_states=_lowerCamelCase   ,  ).sample

							A_ ,			A_	:					List[Any]						 =		pred.chunk(2							)
							A_	:					Dict						 =		pred_uncond + guidance_scale * (pred_text - pred_uncond)

							A_	:					List[Any]						 =		self.scheduler.step(_lowerCamelCase   ,  _lowerCamelCase   ,  _lowerCamelCase							).prev_sample

					return self.decode(_lowerCamelCase							)











"""simple docstring"""

from __future__ import annotations

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	assert (
	    c % greatest_common_divisor(_UpperCAmelCase	,   _UpperCAmelCase						) == 0
	)  # greatest_common_divisor(a,b) function implemented below
	((A_) ,			(A_) ,			(A_))	:					List[Any]						 =		extended_gcd(_UpperCAmelCase	,   _UpperCAmelCase						)  # extended_gcd(a,b) function implemented below
	A_	:					int						 =		c / d
	return (r * x, r * y)

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase = 2						):




	"""simple docstring"""






	((A_) ,			(A_))	:					Any						 =		diophantine(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						)  # Initial value
	A_	:					Union[str, Any]						 =		greatest_common_divisor(_UpperCAmelCase	,   _UpperCAmelCase						)
	A_	:					List[str]						 =		a // d
	A_	:					str						 =		b // d

	for i in range(_UpperCAmelCase						):
		A_	:					int						 =		xa + i * q
		A_	:					str						 =		ya - i * p
		print(_UpperCAmelCase	,   _UpperCAmelCase						)

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	if a < b:
		A_ ,			A_	:					Tuple						 =		b, a

	while a % b != 0:
		A_ ,			A_	:					int						 =		b, a % b

	return b

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	assert a >= 0 and b >= 0

	if b == 0:
		A_ ,			A_ ,			A_	:					str						 =		a, 1, 0
	else:
		((A_) ,			(A_) ,			(A_))	:					List[str]						 =		extended_gcd(_UpperCAmelCase	,   a % b						)
		A_	:					Optional[int]						 =		q
		A_	:					Tuple						 =		p - q * (a // b)

	assert a % d == 0 and b % d == 0
	assert d == a * x + b * y

	return (d, x, y)


if __name__ == "__main__":
		from doctest import testmod

		testmod(name='diophantine', verbose=True)
		testmod(name='diophantine_all_soln', verbose=True)
		testmod(name='extended_gcd', verbose=True)
		testmod(name='greatest_common_divisor', verbose=True)





