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_    (     __snake_case		):



							def __init__(						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													super().__init__()

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



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



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



							def        UpperCamelCase       (						self      ,				lowercase_				):
													_snake_case						:     str				=    self.tokenizer(
													    lowercase_      ,				padding="max_length"      ,				max_length=self.tokenizer.model_max_length      ,				truncation=lowercase_      ,				return_tensors="pt"      ,				)

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

													_snake_case						:     Dict				=    self.tokenizer(
													    ""      ,				padding="max_length"      ,				max_length=self.tokenizer.model_max_length      ,				truncation=lowercase_      ,				return_tensors="pt"      ,				)

													_snake_case						:     Tuple				=    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      ,				):
													_snake_case						:     str				=    steps - int(kmin * steps				)
													_snake_case						:     Tuple				=    steps - int(kmax * steps				)

													_snake_case						:     Union[str, Any]				=    self.prep_text(lowercase_				)

													self.scheduler.set_timesteps(lowercase_				)

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

													torch.manual_seed(lowercase_				)
													_snake_case						:     str				=    torch.randn(
													    (1, self.unet.config.in_channels, height // 8, width // 8)      ,				).to(self.device				)

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

													_snake_case						:     Tuple				=    torch.cat([latents] * 2				)

													_snake_case						:     Optional[int]				=    self.scheduler.scale_model_input(lowercase_      ,				self.scheduler.timesteps[tmax]				)

													with torch.no_grad():
																			_snake_case						:     List[Any]				=    self.unet(
																			    lowercase_      ,				self.scheduler.timesteps[tmax]      ,				encoder_hidden_states=lowercase_      ,				).sample

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

													_snake_case						:     int				=    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
																															_snake_case						:     Tuple				=    self.scheduler.add_noise(
																															    lowercase_      ,				lowercase_      ,				timesteps=lowercase_      ,				)

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

																									else:  # content generation phase
																															_snake_case						:     List[str]				=    torch.cat([latents] * 2				)

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

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

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

																									_snake_case						:     Optional[Any]				=    self.scheduler.step(lowercase_      ,				lowercase_      ,				lowercase_				).prev_sample

													return self.decode(lowercase_				)







from dataclasses import dataclass
from typing import Callable, Optional

import datasets




@dataclass
class 							a_ (						datasets.BuilderConfig		):


							'''simple docstring'''






							__a:	Optional[Callable]     =    None
							__a:	Optional[dict]     =    None
							__a:	Optional[datasets.Features]     =    None

							def      _lowercase       (		self   )					->					Optional[int]:
														'''simple docstring'''

														assert self.generator is not None, "generator must be specified"

														if self.gen_kwargs is None:
																					lowerCAmelCase_										=     {}




class 							a_ (						datasets.GeneratorBasedBuilder		):


							'''simple docstring'''






							__a:	Optional[Any]     =    GeneratorConfig

							def      _lowercase       (		self   )					->					Union[str, Any]:
														'''simple docstring'''

														return datasets.DatasetInfo(features=self.config.features   )

							def      _lowercase       (		self			,			lowercase_   )					->					Dict:
														'''simple docstring'''

														return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,			gen_kwargs=self.config.gen_kwargs   )]






							def      _lowercase       (		self			,			**lowercase_   )					->					Union[str, Any]:
														'''simple docstring'''

														for idx, ex in enumerate(self.config.generator(**lowercase_   )   ):
																					yield idx, ex



