




import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate


class        UpperCAmelCase__				(							a__				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Optional[Any]     					=    CONFIG_NAME
     @register_to_config
     def __init__(				self ,    _lowerCAmelCase = True ,    _lowerCAmelCase = 8 ,    _lowerCAmelCase = "lanczos" ,    _lowerCAmelCase = True ,    _lowerCAmelCase = False ,    ):
            super().__init__()
     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase	):
            if images.ndim == 3:
                   a			   =images[None, ...]
            a			   =(images * 255).round().astype("""uint8"""	)
            if images.shape[-1] == 1:
                   # special case for grayscale (single channel) images
                   a			   =[Image.fromarray(image.squeeze() ,    mode="""L"""	) for image in images]
            else:
                   a			   =[Image.fromarray(__UpperCAmelCase	) for image in images]

            return pil_images
     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase	):
            if not isinstance(__UpperCAmelCase ,    __UpperCAmelCase	):
                   a			   =[images]
            a			   =[np.array(__UpperCAmelCase	).astype(np.floataa	) / 2_55.0 for image in images]
            a			   =np.stack(__UpperCAmelCase ,    axis=0	)

            return images
     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase	):
            if images.ndim == 3:
                   a			   =images[..., None]

            a			   =torch.from_numpy(images.transpose(0 ,    3 ,    1 ,    2	)	)
            return images
     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase	):
            a			   =images.cpu().permute(0 ,    2 ,    3 ,    1	).float().numpy()
            return images
     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase	):
            return 2.0 * images - 1.0
     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase	):
            return (images / 2 + 0.5).clamp(0 ,    1	)
     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase	):
            a			   =image.convert("""RGB"""	)
            return image
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase = None ,    _lowerCAmelCase = None ,    ):
            if height is None:
                   a			   =image.height
            if width is None:
                   a			   =image.width

            a   ,      a			   =(
                x - x % self.config.vae_scale_factor for x in (width, height)
            )  # resize to integer multiple of vae_scale_factor
            a			   =image.resize((width, height) ,    resample=PIL_INTERPOLATION[self.config.resample]	)
            return image
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase = None ,    _lowerCAmelCase = None ,    ):
            a			   =(PIL.Image.Image, np.ndarray, torch.Tensor)
            if isinstance(__UpperCAmelCase ,    __UpperCAmelCase	):
                   a			   =[image]
            elif not (isinstance(__UpperCAmelCase ,    __UpperCAmelCase	) and all(isinstance(__UpperCAmelCase ,    __UpperCAmelCase	) for i in image	)):
                   raise ValueError(
                       F'''Input is in incorrect format: {[type(__UpperCAmelCase	) for i in image]}. Currently, we only support {', '.join(__UpperCAmelCase	)}'''	)

            if isinstance(image[0] ,    PIL.Image.Image	):
                   if self.config.do_convert_rgb:
                          a			   =[self.convert_to_rgb(__UpperCAmelCase	) for i in image]
                   if self.config.do_resize:
                          a			   =[self.resize(__UpperCAmelCase ,    __UpperCAmelCase ,    __UpperCAmelCase	) for i in image]
                   a			   =self.pil_to_numpy(__UpperCAmelCase	)  # to np
                   a			   =self.numpy_to_pt(__UpperCAmelCase	)  # to pt

            elif isinstance(image[0] ,    np.ndarray	):
                   a			   =np.concatenate(__UpperCAmelCase ,    axis=0	) if image[0].ndim == 4 else np.stack(__UpperCAmelCase ,    axis=0	)
                   a			   =self.numpy_to_pt(__UpperCAmelCase	)
                   a   ,      a   ,      a   ,      a			   =image.shape
                   if self.config.do_resize and (
                       height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                   ):
                          raise ValueError(
                              F'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
                              F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''	)

            elif isinstance(image[0] ,    torch.Tensor	):
                   a			   =torch.cat(__UpperCAmelCase ,    axis=0	) if image[0].ndim == 4 else torch.stack(__UpperCAmelCase ,    axis=0	)
                   a   ,      a   ,      a   ,      a			   =image.shape

                   # don't need any preprocess if the image is latents
                   if channel == 4:
                          return image

                   if self.config.do_resize and (
                       height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                   ):
                          raise ValueError(
                              F'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
                              F'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''	)

        # expected range [0,1], normalize to [-1,1]
            a			   =self.config.do_normalize
            if image.min() < 0:
                   warnings.warn(
                       """Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] """
                       F'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]''' ,    __UpperCAmelCase ,    )
                   a			   =False

            if do_normalize:
                   a			   =self.normalize(__UpperCAmelCase	)

            return image






     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase = "pil" ,    _lowerCAmelCase = None ,    ):
            if not isinstance(__UpperCAmelCase ,    torch.Tensor	):
                   raise ValueError(
                       F'''Input for postprocessing is in incorrect format: {type(__UpperCAmelCase	)}. We only support pytorch tensor'''	)
            if output_type not in ["latent", "pt", "np", "pil"]:
                   a			   =(
                       F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
                       """`pil`, `np`, `pt`, `latent`"""
                   )
                   deprecate("""Unsupported output_type""" ,    """1.0.0""" ,    __UpperCAmelCase ,    standard_warn=__UpperCAmelCase	)
                   a			   ="""np"""

            if output_type == "latent":
                   return image

            if do_denormalize is None:
                   a			   =[self.config.do_normalize] * image.shape[0]

            a			   =torch.stack(
                [self.denormalize(image[i]	) if do_denormalize[i] else image[i] for i in range(image.shape[0]	)]	)

            if output_type == "pt":
                   return image

            a			   =self.pt_to_numpy(__UpperCAmelCase	)

            if output_type == "np":
                   return image

            if output_type == "pil":
                   return self.numpy_to_pil(__UpperCAmelCase	)



class        UpperCAmelCase__				(							a__				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Tuple     					=    CONFIG_NAME
     @register_to_config
     def __init__(				self ,    _lowerCAmelCase = True ,    _lowerCAmelCase = 8 ,    _lowerCAmelCase = "lanczos" ,    _lowerCAmelCase = True ,    ):
            super().__init__()
     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase	):
            if images.ndim == 3:
                   a			   =images[None, ...]
            a			   =(images * 255).round().astype("""uint8"""	)
            if images.shape[-1] == 1:
                   # special case for grayscale (single channel) images
                   a			   =[Image.fromarray(image.squeeze() ,    mode="""L"""	) for image in images]
            else:
                   a			   =[Image.fromarray(image[:, :, :3]	) for image in images]

            return pil_images
     @staticmethod
     def        lowerCAmelCase__  (				_lowerCAmelCase	):
            return image[:, :, 1] * 2**8 + image[:, :, 2]
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            if images.ndim == 3:
                   a			   =images[None, ...]
            a			   =images[:, :, :, 3:]
            if images.shape[-1] == 6:
                   a			   =(images_depth * 255).round().astype("""uint8"""	)
                   a			   =[
                       Image.fromarray(self.rgblike_to_depthmap(__UpperCAmelCase	) ,    mode="""I;16"""	) for image_depth in images_depth
                   ]
            elif images.shape[-1] == 4:
                   a			   =(images_depth * 6_55_35.0).astype(np.uintaa	)
                   a			   =[Image.fromarray(__UpperCAmelCase ,    mode="""I;16"""	) for image_depth in images_depth]
            else:
                   raise Exception("""Not supported"""	)

            return pil_images






     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase = "pil" ,    _lowerCAmelCase = None ,    ):
            if not isinstance(__UpperCAmelCase ,    torch.Tensor	):
                   raise ValueError(
                       F'''Input for postprocessing is in incorrect format: {type(__UpperCAmelCase	)}. We only support pytorch tensor'''	)
            if output_type not in ["latent", "pt", "np", "pil"]:
                   a			   =(
                       F'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
                       """`pil`, `np`, `pt`, `latent`"""
                   )
                   deprecate("""Unsupported output_type""" ,    """1.0.0""" ,    __UpperCAmelCase ,    standard_warn=__UpperCAmelCase	)
                   a			   ="""np"""

            if do_denormalize is None:
                   a			   =[self.config.do_normalize] * image.shape[0]

            a			   =torch.stack(
                [self.denormalize(image[i]	) if do_denormalize[i] else image[i] for i in range(image.shape[0]	)]	)

            a			   =self.pt_to_numpy(__UpperCAmelCase	)

            if output_type == "np":
                   if image.shape[-1] == 6:
                          a			   =np.stack([self.rgblike_to_depthmap(im[:, :, 3:]	) for im in image] ,    axis=0	)
                   else:
                          a			   =image[:, :, :, 3:]
                   return image[:, :, :, :3], image_depth

            if output_type == "pil":
                   return self.numpy_to_pil(__UpperCAmelCase	), self.numpy_to_depth(__UpperCAmelCase	)
            else:
                   raise Exception(F'''This type {output_type} is not supported'''	)




"""simple docstring"""






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


__snake_case												=							False


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

     '''simple docstring'''


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCAmelCase				)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

          _a     						=					load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

          _a     						=					torch.manual_seed(0				)
          _a     						=					pipe(
              prompt='''first prompt'''     ,					image=__UpperCAmelCase     ,					text_to_image_strength=0.75     ,					generator=__UpperCAmelCase     ,					guidance_scale=7.5     ,					num_inference_steps=2     ,					output_type='''numpy'''     ,					).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCAmelCase				)
               _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCAmelCase				)

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

          _a     						=					generator.manual_seed(0				)
          _a     						=					pipe(
              prompt='''first prompt'''     ,					image=__UpperCAmelCase     ,					text_to_image_strength=0.75     ,					generator=__UpperCAmelCase     ,					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				)							->				Dict:
          _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
          pipe.remove_unused_weights()
          pipe.to(__UpperCAmelCase				)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

          _a     						=					'''cyberpunk 2077'''
          _a     						=					load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
          _a     						=					torch.manual_seed(0				)
          _a     						=					pipe(
              prompt=__UpperCAmelCase     ,					image=__UpperCAmelCase     ,					text_to_image_strength=0.75     ,					generator=__UpperCAmelCase     ,					guidance_scale=7.5     ,					num_inference_steps=50     ,					output_type='''numpy'''     ,					).images

          _a     						=					image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a     						=					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