


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   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):

       def __init__(      self						,    a__						,    a__						,    a__						,    a__						,    a__						,    ):






        '''simple docstring'''






        super().__init__()

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

       def 					UpperCAmelCase					(      self						,    a__       ):






        '''simple docstring'''






        with torch.no_grad():
         snake_case :			Optional[int]							=   self.vae.encode(tfms.ToTensor()(a__       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
         snake_case :			Dict							=   0.1_8215 * latent.latent_dist.sample()
        return latent

       def 					UpperCAmelCase					(      self						,    a__       ):






        '''simple docstring'''






        snake_case :			Tuple							=   (1 / 0.1_8215) * latent
        with torch.no_grad():
         snake_case :			int							=   self.vae.decode(a__       ).sample
        snake_case :			Optional[int]							=   (img / 2 + 0.5).clamp(0						,    1       )
        snake_case :			Optional[Any]							=   img.detach().cpu().permute(0						,    2						,    3						,    1       ).numpy()
        snake_case :			Optional[int]							=   (img * 2_55).round().astype('''uint8'''       )
        return Image.fromarray(img[0]       )

       def 					UpperCAmelCase					(      self						,    a__       ):






        '''simple docstring'''






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

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

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

        snake_case :			List[Any]							=   self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

        return torch.cat([uncond_embedding, text_embedding]       )



       def __call__(      self						,    a__						,    a__						,    a__ = 0.3						,    a__ = 0.6						,    a__ = 0.5						,    a__ = 42						,    a__ = 50						,    a__ = 7.5						,    ):






        '''simple docstring'''






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

        snake_case :			List[str]							=   self.prep_text(a__       )

        self.scheduler.set_timesteps(a__       )

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

        torch.manual_seed(a__       )
        snake_case :			Optional[Any]							=   torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)						,    ).to(self.device       )

        snake_case :			List[str]							=   self.scheduler.add_noise(
            a__						,    a__						,    timesteps=self.scheduler.timesteps[tmax]						,    )

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

        snake_case :			List[str]							=   self.scheduler.scale_model_input(a__						,    self.scheduler.timesteps[tmax]       )

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

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

        snake_case :			Optional[Any]							=   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
           snake_case :			Dict							=   self.scheduler.add_noise(
               a__						,    a__						,    timesteps=a__						,    )

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

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

          snake_case :			Any							=   self.scheduler.scale_model_input(a__						,    a__       )

          with torch.no_grad():
           snake_case :			List[str]							=   self.unet(
               a__						,    a__						,    encoder_hidden_states=a__						,    ).sample

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

          snake_case :			Tuple							=   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






