
"""simple docstring"""





import gc
import tempfile
import unittest

import numpy as np
import torch

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


_a		       =							False


class A_	(unittest.TestCase      ):




  '''simple docstring'''
  pass




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




  '''simple docstring'''







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"				,   torch_dtype=torch.floataa  )
     pipe.to(lowercase_  )
     pipe.set_progress_bar_config(disable=lowercase_  )

     UpperCAmelCase_					:					List[str]			     =    load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )

     UpperCAmelCase_					:					List[str]			     =    torch.manual_seed(0  )
     UpperCAmelCase_					:					int			     =    pipe.dual_guided(
         prompt="first prompt"				,   image=lowercase_				,   text_to_image_strength=0.75				,   generator=lowercase_				,   guidance_scale=7.5				,   num_inference_steps=2				,   output_type="numpy"				,   ).images

     with tempfile.TemporaryDirectory() as tmpdirname:
        pipe.save_pretrained(lowercase_  )
        UpperCAmelCase_					:					Tuple			     =    VersatileDiffusionPipeline.from_pretrained(lowercase_				,   torch_dtype=torch.floataa  )
     pipe.to(lowercase_  )
     pipe.set_progress_bar_config(disable=lowercase_  )

     UpperCAmelCase_					:					Optional[int]			     =    generator.manual_seed(0  )
     UpperCAmelCase_					:					List[Any]			     =    pipe.dual_guided(
         prompt="first prompt"				,   image=lowercase_				,   text_to_image_strength=0.75				,   generator=lowercase_				,   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"""
     UpperCAmelCase_					:					List[str]			     =    VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"				,   torch_dtype=torch.floataa  )
     pipe.to(lowercase_  )
     pipe.set_progress_bar_config(disable=lowercase_  )

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

     UpperCAmelCase_					:					Union[str, Any]			     =    image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     UpperCAmelCase_					:					Optional[Any]			     =    np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]  )

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

     UpperCAmelCase_					:					Any			     =    "A painting of a squirrel eating a burger "
     UpperCAmelCase_					:					Dict			     =    torch.manual_seed(0  )
     UpperCAmelCase_					:					Tuple			     =    pipe.text_to_image(
         prompt=lowercase_				,   generator=lowercase_				,   guidance_scale=7.5				,   num_inference_steps=50				,   output_type="numpy"  ).images

     UpperCAmelCase_					:					List[str]			     =    image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     UpperCAmelCase_					:					int			     =    np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]  )

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

     UpperCAmelCase_					:					Optional[Any]			     =    pipe.image_variation(lowercase_				,   generator=lowercase_				,   output_type="numpy"  ).images

     UpperCAmelCase_					:					Tuple			     =    image[0, 253:256, 253:256, -1]

     assert image.shape == (1, 512, 512, 3)
     UpperCAmelCase_					:					Union[str, Any]			     =    np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]  )

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






import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def 							_UpperCAmelCase				(UpperCamelCase__     :  Union[str, Any]						):
       _A    ,			_A      :       Any				    = image.size
       _A    ,			_A      :       str				    = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
       _A      :       List[str]				    = image.resize((w, h)				,							resample=PIL_INTERPOLATION["lanczos"]						)
       _A      :       Any				    = np.array(UpperCamelCase__						).astype(np.floataa						) / 2_55.0
       _A      :       Optional[Any]				    = image[None].transpose(0				,							3				,							1				,							2						)
       _A      :       Union[str, Any]				    = torch.from_numpy(UpperCamelCase__						)
       return 2.0 * image - 1.0







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						)			->				Optional[int]:
            super().__init__()
            self.register_modules(vqvae=__lowerCamelCase ,						unet=__lowerCamelCase ,						scheduler=__lowerCamelCase)


     @torch.no_grad()
     def __call__(						self ,						__lowerCamelCase = None ,						__lowerCamelCase = 1 ,						__lowerCamelCase = 1_0_0 ,						__lowerCamelCase = 0.0 ,						__lowerCamelCase = None ,						__lowerCamelCase = "pil" ,						__lowerCamelCase = True ,						)			->				Union[Tuple, ImagePipelineOutput]:
            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Tuple				    = 1
            elif isinstance(__lowerCamelCase ,						torch.Tensor):
                   _A      :       Union[str, Any]				    = image.shape[0]
            else:
                   raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowerCamelCase)}")

            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Union[str, Any]				    = preprocess(__lowerCamelCase)

            _A    ,			_A      :       Union[str, Any]				    = image.shape[-2:]

            # in_channels should be 6: 3 for latents, 3 for low resolution image
            _A      :       Optional[Any]				    = (batch_size, self.unet.config.in_channels // 2, height, width)
            _A      :       str				    = next(self.unet.parameters()).dtype

            _A      :       Union[str, Any]				    = randn_tensor(__lowerCamelCase ,						generator=__lowerCamelCase ,						device=self.device ,						dtype=__lowerCamelCase)

            _A      :       List[Any]				    = image.to(device=self.device ,						dtype=__lowerCamelCase)

            # set timesteps and move to the correct device
            self.scheduler.set_timesteps(__lowerCamelCase ,						device=self.device)
            _A      :       Any				    = self.scheduler.timesteps

            # scale the initial noise by the standard deviation required by the scheduler
            _A      :       List[str]				    = latents * self.scheduler.init_noise_sigma

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            _A      :       str				    = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
            _A      :       Optional[int]				    = {}
            if accepts_eta:
                   _A      :       List[Any]				    = eta

            for t in self.progress_bar(__lowerCamelCase):
                   # concat latents and low resolution image in the channel dimension.
                   _A      :       List[Any]				    = torch.cat([latents, image] ,						dim=1)
                   _A      :       str				    = self.scheduler.scale_model_input(__lowerCamelCase ,						__lowerCamelCase)
                   # predict the noise residual
                   _A      :       Any				    = self.unet(__lowerCamelCase ,						__lowerCamelCase).sample
                   # compute the previous noisy sample x_t -> x_t-1
                   _A      :       Optional[int]				    = self.scheduler.step(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase).prev_sample

            # decode the image latents with the VQVAE
            _A      :       Union[str, Any]				    = self.vqvae.decode(__lowerCamelCase).sample
            _A      :       Dict				    = torch.clamp(__lowerCamelCase ,						-1.0 ,						1.0)
            _A      :       Tuple				    = image / 2 + 0.5
            _A      :       int				    = image.cpu().permute(0 ,						2 ,						3 ,						1).numpy()

            if output_type == "pil":
                   _A      :       Optional[int]				    = self.numpy_to_pil(__lowerCamelCase)

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=__lowerCamelCase)
