



"""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__		(   UpperCamelCase  ):



		def __init__(			self	:     Optional[Any]   ,				snake_case	:     Tuple   ,				snake_case	:     List[Any]   ,				snake_case	:     Dict   ,				snake_case	:     Optional[int]   ,				snake_case	:     Any   ,				)      ->    List[str]:






									'''simple docstring'''

									super().__init__()

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



		def        A_		(			self	:     List[Any]   ,				snake_case	:     Optional[int]					)      ->    Any:






									'''simple docstring'''

									with torch.no_grad():
																A    =       self.vae.encode(tfms.ToTensor()(a__					).unsqueeze(0					).to(self.device					) * 2 - 1					)
																A    =       0.18215 * latent.latent_dist.sample()
									return latent



		def        A_		(			self	:     Union[str, Any]   ,				snake_case	:     int					)      ->    str:






									'''simple docstring'''

									A    =       (1 / 0.18215) * latent
									with torch.no_grad():
																A    =       self.vae.decode(a__					).sample
									A    =       (img / 2 + 0.5).clamp(0   ,				1					)
									A    =       img.detach().cpu().permute(0   ,				2   ,				3   ,				1					).numpy()
									A    =       (img * 255).round().astype('uint8'					)
									return Image.fromarray(img[0]					)



		def        A_		(			self	:     Dict   ,				snake_case	:     List[str]					)      ->    Optional[Any]:






									'''simple docstring'''

									A    =       self.tokenizer(
									    a__   ,				padding='max_length'   ,				max_length=self.tokenizer.model_max_length   ,				truncation=a__   ,				return_tensors='pt'   ,				)

									A    =       self.text_encoder(text_input.input_ids.to(self.device					)					)[0]

									A    =       self.tokenizer(
									    ''   ,				padding='max_length'   ,				max_length=self.tokenizer.model_max_length   ,				truncation=a__   ,				return_tensors='pt'   ,				)

									A    =       self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

									return torch.cat([uncond_embedding, text_embedding]					)



		def __call__(			self	:     Optional[int]   ,				snake_case	:     List[Any]   ,				snake_case	:     Union[str, Any]   ,				snake_case	:     Tuple = 0.3   ,				snake_case	:     Union[str, Any] = 0.6   ,				snake_case	:     Any = 0.5   ,				snake_case	:     List[Any] = 42   ,				snake_case	:     List[Any] = 50   ,				snake_case	:     Tuple = 7.5   ,				)      ->    Optional[Any]:






									'''simple docstring'''

									A    =       steps - int(kmin * steps					)
									A    =       steps - int(kmax * steps					)

									A    =       self.prep_text(a__					)

									self.scheduler.set_timesteps(a__					)

									A    =       img.size
									A    =       self.encode(a__					)

									torch.manual_seed(a__					)
									A    =       torch.randn(
									    (1, self.unet.config.in_channels, height // 8, width // 8)   ,				).to(self.device					)

									A    =       self.scheduler.add_noise(
									    a__   ,				a__   ,				timesteps=self.scheduler.timesteps[tmax]   ,				)

									A    =       torch.cat([latents] * 2					)

									A    =       self.scheduler.scale_model_input(a__   ,				self.scheduler.timesteps[tmax]					)

									with torch.no_grad():
																A    =       self.unet(
																    a__   ,				self.scheduler.timesteps[tmax]   ,				encoder_hidden_states=a__   ,				).sample

									A    =       pred.chunk(2					)
									A    =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

									A    =       self.scheduler.step(a__   ,				self.scheduler.timesteps[tmax]   ,				a__					).prev_sample

									for i, t in enumerate(tqdm(self.scheduler.timesteps					)					):
																if i > tmax:
																							if i < tmin:  # layout generation phase
																														A    =       self.scheduler.add_noise(
																														    a__   ,				a__   ,				timesteps=a__   ,				)

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

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

																							A    =       self.scheduler.scale_model_input(a__   ,				a__					)

																							with torch.no_grad():
																														A    =       self.unet(
																														    a__   ,				a__   ,				encoder_hidden_states=a__   ,				).sample

																							A    =       pred.chunk(2					)
																							A    =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

																							A    =       self.scheduler.step(a__   ,				a__   ,				a__					).prev_sample

									return self.decode(a__					)







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a					:				Optional[int]       =      False

@nightly
@require_torch_gpu
class   lowerCamelCase						(	unittest.TestCase  ):

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			Union[str, Any]							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )

        snake_case :			str							=   torch.manual_seed(0       )
        snake_case :			Tuple							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=2						,    output_type='''numpy'''						,    ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(a__       )
         snake_case :			str							=   VersatileDiffusionDualGuidedPipeline.from_pretrained(a__       )

        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			Optional[Any]							=   generator.manual_seed(0       )
        snake_case :			List[str]							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=2						,    output_type='''numpy'''						,    ).images

        assert np.abs(image - new_image       ).sum() < 1e-5, "Models don't have the same forward pass"



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Tuple							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			int							=   '''cyberpunk 2077'''
        snake_case :			Dict							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )
        snake_case :			Optional[int]							=   torch.manual_seed(0       )
        snake_case :			Union[str, Any]							=   pipe(
            prompt=a__						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=50						,    output_type='''numpy'''						,    ).images

        snake_case :			str							=   image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        snake_case :			int							=   np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]       )

        assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2






