

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 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Union[str, Any]        =  CONFIG_NAME



   @register_to_config
   def __init__(							self,       lowercase_ = True,       lowercase_ = 8,       lowercase_ = "lanczos",       lowercase_ = True,       lowercase_ = False,       )				->       Tuple:






     """simple docstring"""






     super().__init__()



   @staticmethod
   def 					_UpperCAmelCase					(							lowercase_			)				->       PIL.Image.Image:






     """simple docstring"""






     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(lowercase_			) for image in images]

     return pil_images



   @staticmethod
   def 					_UpperCAmelCase					(							lowercase_			)				->       np.ndarray:






     """simple docstring"""






     if not isinstance(lowercase_,       lowercase_			):
       a__    =[images]
     a__    =[np.array(lowercase_			).astype(np.floataa			) / 255.0 for image in images]
     a__    =np.stack(lowercase_,       axis=0			)

     return images



   @staticmethod
   def 					_UpperCAmelCase					(							lowercase_			)				->       torch.FloatTensor:






     """simple docstring"""






     if images.ndim == 3:
       a__    =images[..., None]

     a__    =torch.from_numpy(images.transpose(0,       3,       1,       2			)			)
     return images



   @staticmethod
   def 					_UpperCAmelCase					(							lowercase_			)				->       np.ndarray:






     """simple docstring"""






     a__    =images.cpu().permute(0,       2,       3,       1			).float().numpy()
     return images



   @staticmethod
   def 					_UpperCAmelCase					(							lowercase_			)				->       str:






     """simple docstring"""






     return 2.0 * images - 1.0



   @staticmethod
   def 					_UpperCAmelCase					(							lowercase_			)				->       List[str]:






     """simple docstring"""






     return (images / 2 + 0.5).clamp(0,       1			)



   @staticmethod
   def 					_UpperCAmelCase					(							lowercase_			)				->       PIL.Image.Image:






     """simple docstring"""






     a__    =image.convert('''RGB'''			)
     return image



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = None,       lowercase_ = None,       )				->       PIL.Image.Image:






     """simple docstring"""






     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 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = None,       lowercase_ = None,       )				->       torch.Tensor:






     """simple docstring"""






     a__    =(PIL.Image.Image, np.ndarray, torch.Tensor)
     if isinstance(lowercase_,       lowercase_			):
       a__    =[image]
     elif not (isinstance(lowercase_,       lowercase_			) and all(isinstance(lowercase_,       lowercase_			) for i in image			)):
       raise ValueError(
           F"""Input is in incorrect format: {[type(lowercase_			) for i in image]}. Currently, we only support {", ".join(lowercase_			)}"""			)

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

     elif isinstance(image[0],       np.ndarray			):
       a__    =np.concatenate(lowercase_,       axis=0			) if image[0].ndim == 4 else np.stack(lowercase_,       axis=0			)
       a__    =self.numpy_to_pt(lowercase_			)
       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(lowercase_,       axis=0			) if image[0].ndim == 4 else torch.stack(lowercase_,       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()}]""",       lowercase_,       )
       a__    =False

     if do_normalize:
       a__    =self.normalize(lowercase_			)

     return image



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = "pil",       lowercase_ = None,       )				->       List[Any]:






     """simple docstring"""






     if not isinstance(lowercase_,       torch.Tensor			):
       raise ValueError(
           F"""Input for postprocessing is in incorrect format: {type(lowercase_			)}. 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''',       lowercase_,       standard_warn=lowercase_			)
       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(lowercase_			)

     if output_type == "np":
       return image

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







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Optional[Any]        =  CONFIG_NAME



   @register_to_config
   def __init__(							self,       lowercase_ = True,       lowercase_ = 8,       lowercase_ = "lanczos",       lowercase_ = True,       )				->       Any:






     """simple docstring"""






     super().__init__()



   @staticmethod
   def 					_UpperCAmelCase					(							lowercase_			)				->       Tuple:






     """simple docstring"""






     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 					_UpperCAmelCase					(							lowercase_			)				->       Optional[int]:






     """simple docstring"""






     return image[:, :, 1] * 2**8 + image[:, :, 2]



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       str:






     """simple docstring"""






     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(lowercase_			),       mode='''I;16'''			) for image_depth in images_depth
       ]
     elif images.shape[-1] == 4:
       a__    =(images_depth * 65535.0).astype(np.uintaa			)
       a__    =[Image.fromarray(lowercase_,       mode='''I;16'''			) for image_depth in images_depth]
     else:
       raise Exception('''Not supported'''			)

     return pil_images



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_ = "pil",       lowercase_ = None,       )				->       Union[str, Any]:






     """simple docstring"""






     if not isinstance(lowercase_,       torch.Tensor			):
       raise ValueError(
           F"""Input for postprocessing is in incorrect format: {type(lowercase_			)}. 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''',       lowercase_,       standard_warn=lowercase_			)
       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(lowercase_			)

     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(lowercase_			), self.numpy_to_depth(lowercase_			)
     else:
       raise Exception(F"""This type {output_type} is not supported"""			)



import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   @property
   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     a__    =1
     a__    =3
     a__    =8
     a__    =8

     a__    =torch.rand((batch_size, num_channels, height, width)			)

     return sample



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       int:






     """simple docstring"""






     if isinstance(image[0],       PIL.Image.Image			):
       return np.stack([np.array(lowercase_			) for i in image],       axis=0			)
     elif isinstance(lowercase_,       torch.Tensor			):
       return image.cpu().numpy().transpose(0,       2,       3,       1			)
     return image



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =self.to_np(lowercase_			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(
           image_processor.preprocess(lowercase_			),       output_type=lowercase_,       )
       a__    =self.to_np(lowercase_			)
       a__    =(input_np * 255).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np			).max() < 1E-6
       ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)
     a__    =self.dummy_sample.cpu().numpy().transpose(0,       2,       3,       1			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(image_processor.preprocess(lowercase_			),       output_type=lowercase_			)

       a__    =self.to_np(lowercase_			)
       a__    =(input_np * 255).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np			).max() < 1E-6
       ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample.cpu().numpy().transpose(0,       2,       3,       1			)
     a__    =image_processor.numpy_to_pil(lowercase_			)

     for output_type in ["pt", "np", "pil"]:
       a__    =image_processor.postprocess(image_processor.preprocess(lowercase_			),       output_type=lowercase_			)
       for i, o in zip(lowercase_,       lowercase_			):
         a__    =np.array(lowercase_			)
         a__    =self.to_np(lowercase_			) if output_type == '''pil''' else (self.to_np(lowercase_			) * 255).round()
         assert (
             np.abs(in_np - out_np			).max() < 1E-6
         ), F"""decoded output does not match input for output_type {output_type}"""



   def 					_UpperCAmelCase					(							self			)				->       Union[str, Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =input_pt_ad.squeeze(0			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )
     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =self.to_np(self.dummy_sample			)
     a__    =input_np_ad.squeeze(0			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )
     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     assert np.abs(out_pt_ad - out_pt_ad			).max() < 1E-6
     assert np.abs(out_np_ad - out_np_ad			).max() < 1E-6



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =VaeImageProcessor(do_resize=lowercase_,       do_normalize=lowercase_			)

     a__    =self.dummy_sample
     a__    =list(lowercase_			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =self.to_np(self.dummy_sample			)
     list(lowercase_			)

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     a__    =image_processor.postprocess(
         image_processor.preprocess(lowercase_			),       output_type='''np''',       )

     assert np.abs(out_pt_ad - out_pt_list			).max() < 1E-6
     assert np.abs(out_np_ad - out_np_list			).max() < 1E-6
