



'''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 			__magic_name__		(					_UpperCamelCase     ):







							def __init__(							self      : Any			,_UpperCAmelCase      : AutoencoderKL			,_UpperCAmelCase      : CLIPTextModel			,_UpperCAmelCase      : CLIPTokenizer			,_UpperCAmelCase      : UNetaDConditionModel			,_UpperCAmelCase      : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]			,):
									super().__init__()

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







							def 							__lowercase    (							self      : Dict			,_UpperCAmelCase      : Tuple	):
									with torch.no_grad():
											_a  :   str				 =							self.vae.encode(tfms.ToTensor()(_UpperCAmelCase	).unsqueeze(0	).to(self.device	) * 2 - 1	)
											_a  :   int				 =							0.1_82_15 * latent.latent_dist.sample()
									return latent







							def 							__lowercase    (							self      : List[str]			,_UpperCAmelCase      : str	):
									_a  :   int				 =							(1 / 0.1_82_15) * latent
									with torch.no_grad():
											_a  :   str				 =							self.vae.decode(_UpperCAmelCase	).sample
									_a  :   Optional[Any]				 =							(img / 2 + 0.5).clamp(0			,1	)
									_a  :   List[Any]				 =							img.detach().cpu().permute(0			,2			,3			,1	).numpy()
									_a  :   Dict				 =							(img * 255).round().astype('uint8'	)
									return Image.fromarray(img[0]	)







							def 							__lowercase    (							self      : Union[str, Any]			,_UpperCAmelCase      : Union[str, Any]	):
									_a  :   str				 =							self.tokenizer(
									    _UpperCAmelCase			,padding='max_length'			,max_length=self.tokenizer.model_max_length			,truncation=_UpperCAmelCase			,return_tensors='pt'			,)

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

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

									_a  :   Optional[Any]				 =							self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

									return torch.cat([uncond_embedding, text_embedding]	)







							def __call__(							self      : Tuple			,_UpperCAmelCase      : Image.Image			,_UpperCAmelCase      : str			,_UpperCAmelCase      : float = 0.3			,_UpperCAmelCase      : float = 0.6			,_UpperCAmelCase      : float = 0.5			,_UpperCAmelCase      : int = 42			,_UpperCAmelCase      : int = 50			,_UpperCAmelCase      : float = 7.5			,):
									_a  :   Tuple				 =							steps - int(kmin * steps	)
									_a  :   int				 =							steps - int(kmax * steps	)

									_a  :   Optional[Any]				 =							self.prep_text(_UpperCAmelCase	)

									self.scheduler.set_timesteps(_UpperCAmelCase	)

									_a ,      _a  :   List[str]				 =							img.size
									_a  :   Union[str, Any]				 =							self.encode(_UpperCAmelCase	)

									torch.manual_seed(_UpperCAmelCase	)
									_a  :   List[str]				 =							torch.randn(
									    (1, self.unet.config.in_channels, height // 8, width // 8)			,).to(self.device	)

									_a  :   Tuple				 =							self.scheduler.add_noise(
									    _UpperCAmelCase			,_UpperCAmelCase			,timesteps=self.scheduler.timesteps[tmax]			,)

									_a  :   Optional[int]				 =							torch.cat([latents] * 2	)

									_a  :   str				 =							self.scheduler.scale_model_input(_UpperCAmelCase			,self.scheduler.timesteps[tmax]	)

									with torch.no_grad():
											_a  :   Union[str, Any]				 =							self.unet(
											    _UpperCAmelCase			,self.scheduler.timesteps[tmax]			,encoder_hidden_states=_UpperCAmelCase			,).sample

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

									_a  :   int				 =							self.scheduler.step(_UpperCAmelCase			,self.scheduler.timesteps[tmax]			,_UpperCAmelCase	).prev_sample

									for i, t in enumerate(tqdm(self.scheduler.timesteps	)	):
											if i > tmax:
													if i < tmin:  # layout generation phase
															_a  :   Optional[Any]				 =							self.scheduler.add_noise(
															    _UpperCAmelCase			,_UpperCAmelCase			,timesteps=_UpperCAmelCase			,)

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

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

													_a  :   List[Any]				 =							self.scheduler.scale_model_input(_UpperCAmelCase			,_UpperCAmelCase	)

													with torch.no_grad():
															_a  :   Optional[Any]				 =							self.unet(
															    _UpperCAmelCase			,_UpperCAmelCase			,encoder_hidden_states=_UpperCAmelCase			,).sample

													_a ,      _a  :   str				 =							pred.chunk(2	)
													_a  :   List[str]				 =							pred_uncond + guidance_scale * (pred_text - pred_uncond)

													_a  :   Any				 =							self.scheduler.step(_UpperCAmelCase			,_UpperCAmelCase			,_UpperCAmelCase	).prev_sample

									return self.decode(_UpperCAmelCase	)









'''simple docstring'''

from __future__ import annotations

# Define the Gravitational Constant G and the function
__lowerCAmelCase		    =     6.6743e-11  # unit of G : m^3 * kg^-1 * s^-2



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   dict[str, float]:
		_a  :   str				 =							mass_a * mass_a

		if (force, mass_a, mass_a, distance).count(0						) != 1:
				raise ValueError('One and only one argument must be 0'						)
		if force < 0:
				raise ValueError('Gravitational force can not be negative'						)
		if distance < 0:
				raise ValueError('Distance can not be negative'						)
		if mass_a < 0 or mass_a < 0:
				raise ValueError('Mass can not be negative'						)
		if force == 0:
				_a  :   List[Any]				 =							GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
				return {"force": force}
		elif mass_a == 0:
				_a  :   Any				 =							(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
				return {"mass_1": mass_a}
		elif mass_a == 0:
				_a  :   Tuple				 =							(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
				return {"mass_2": mass_a}
		elif distance == 0:
				_a  :   str				 =							(GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
				return {"distance": distance}
		raise ValueError('One and only one argument must be 0'						)


# Run doctest
if __name__ == "__main__":
							import doctest

							doctest.testmod()




