
"""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__			)







# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowerCamelCase       =	abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Tuple			):
		from transformers.testing_utils import pytest_addoption_shared

		pytest_addoption_shared(UpperCamelCase__			)






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							Tuple			):
		from transformers.testing_utils import pytest_terminal_summary_main

		SCREAMING_SNAKE_CASE__								=					terminalreporter.config.getoption("""--make-reports"""			)
		if make_reports:
				pytest_terminal_summary_main(UpperCamelCase__    ,      id=UpperCamelCase__			)