
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  lowerCAmelCase__   (       lowercase__       ):



  '''simple docstring'''






  _UpperCamelCase						= CONFIG_NAME

  @register_to_config
  def __init__(self  : Union[str, Any]	, lowerCAmelCase_  : bool = True	, lowerCAmelCase_  : int = 8	, lowerCAmelCase_  : str = "lanczos"	, lowerCAmelCase_  : bool = True	, lowerCAmelCase_  : bool = False	, )   ->       int:
        super().__init__()

  @staticmethod
  def 		_lowerCamelCase	(lowerCAmelCase_  : np.ndarray    )   ->       PIL.Image.Image:
        if images.ndim == 3:
              snake_case_						       =							images[None, ...]
        snake_case_						       =							(images * 2_55).round().astype("uint8"    )
        if images.shape[-1] == 1:
              # special case for grayscale (single channel) images
              snake_case_						       =							[Image.fromarray(image.squeeze()	, mode="L"    ) for image in images]
        else:
              snake_case_						       =							[Image.fromarray(_A    ) for image in images]

        return pil_images

  @staticmethod
  def 		_lowerCamelCase	(lowerCAmelCase_  : Union[List[PIL.Image.Image], PIL.Image.Image]    )   ->       np.ndarray:
        if not isinstance(_A	, _A    ):
              snake_case_						       =							[images]
        snake_case_						       =							[np.array(_A    ).astype(np.floataa    ) / 255.0 for image in images]
        snake_case_						       =							np.stack(_A	, axis=0    )

        return images

  @staticmethod
  def 		_lowerCamelCase	(lowerCAmelCase_  : np.ndarray    )   ->       torch.FloatTensor:
        if images.ndim == 3:
              snake_case_						       =							images[..., None]

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

  @staticmethod
  def 		_lowerCamelCase	(lowerCAmelCase_  : torch.FloatTensor    )   ->       np.ndarray:
        snake_case_						       =							images.cpu().permute(0	, 2	, 3	, 1    ).float().numpy()
        return images

  @staticmethod
  def 		_lowerCamelCase	(lowerCAmelCase_  : str    )   ->       Union[str, Any]:
        return 2.0 * images - 1.0

  @staticmethod
  def 		_lowerCamelCase	(lowerCAmelCase_  : Any    )   ->       Union[str, Any]:
        return (images / 2 + 0.5).clamp(0	, 1    )

  @staticmethod
  def 		_lowerCamelCase	(lowerCAmelCase_  : PIL.Image.Image    )   ->       PIL.Image.Image:
        snake_case_						       =							image.convert("RGB"    )
        return image

  def 		_lowerCamelCase	(self  : List[Any]	, lowerCAmelCase_  : PIL.Image.Image	, lowerCAmelCase_  : Optional[int] = None	, lowerCAmelCase_  : Optional[int] = None	, )   ->       PIL.Image.Image:
        if height is None:
              snake_case_						       =							image.height
        if width is None:
              snake_case_						       =							image.width

        snake_case_						       =							(
            x - x % self.config.vae_scale_factor for x in (width, height)
        )  # resize to integer multiple of vae_scale_factor
        snake_case_						       =							image.resize((width, height)	, resample=PIL_INTERPOLATION[self.config.resample]    )
        return image

  def 		_lowerCamelCase	(self  : Optional[Any]	, lowerCAmelCase_  : Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]	, lowerCAmelCase_  : Optional[int] = None	, lowerCAmelCase_  : Optional[int] = None	, )   ->       torch.Tensor:
        snake_case_						       =							(PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(_A	, _A    ):
              snake_case_						       =							[image]
        elif not (isinstance(_A	, _A    ) and all(isinstance(_A	, _A    ) for i in image    )):
              raise ValueError(
                  f'''Input is in incorrect format: {[type(_A    ) for i in image]}. Currently, we only support {', '.join(_A    )}'''    )

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

        elif isinstance(image[0]	, np.ndarray    ):
              snake_case_						       =							np.concatenate(_A	, axis=0    ) if image[0].ndim == 4 else np.stack(_A	, axis=0    )
              snake_case_						       =							self.numpy_to_pt(_A    )
              snake_case_						       =							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    ):
              snake_case_						       =							torch.cat(_A	, axis=0    ) if image[0].ndim == 4 else torch.stack(_A	, axis=0    )
              snake_case_						       =							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]
        snake_case_						       =							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()}]'''	, _A	, )
              snake_case_						       =							False

        if do_normalize:
              snake_case_						       =							self.normalize(_A    )

        return image


  def 		_lowerCamelCase	(self  : Union[str, Any]	, lowerCAmelCase_  : torch.FloatTensor	, lowerCAmelCase_  : str = "pil"	, lowerCAmelCase_  : Optional[List[bool]] = None	, )   ->       Optional[Any]:
        if not isinstance(_A	, torch.Tensor    ):
              raise ValueError(
                  f'''Input for postprocessing is in incorrect format: {type(_A    )}. We only support pytorch tensor'''    )
        if output_type not in ["latent", "pt", "np", "pil"]:
              snake_case_						       =							(
                  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"	, _A	, standard_warn=_A    )
              snake_case_						       =							'np'

        if output_type == "latent":
              return image

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

        snake_case_						       =							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

        snake_case_						       =							self.pt_to_numpy(_A    )

        if output_type == "np":
              return image

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



class  lowerCAmelCase__   (       lowercase__       ):



  '''simple docstring'''






  _UpperCamelCase						= CONFIG_NAME

  @register_to_config
  def __init__(self  : List[str]	, lowerCAmelCase_  : bool = True	, lowerCAmelCase_  : int = 8	, lowerCAmelCase_  : str = "lanczos"	, lowerCAmelCase_  : bool = True	, )   ->       Tuple:
        super().__init__()

  @staticmethod
  def 		_lowerCamelCase	(lowerCAmelCase_  : Union[str, Any]    )   ->       Optional[int]:
        if images.ndim == 3:
              snake_case_						       =							images[None, ...]
        snake_case_						       =							(images * 2_55).round().astype("uint8"    )
        if images.shape[-1] == 1:
              # special case for grayscale (single channel) images
              snake_case_						       =							[Image.fromarray(image.squeeze()	, mode="L"    ) for image in images]
        else:
              snake_case_						       =							[Image.fromarray(image[:, :, :3]    ) for image in images]

        return pil_images

  @staticmethod
  def 		_lowerCamelCase	(lowerCAmelCase_  : Optional[int]    )   ->       Optional[Any]:
        return image[:, :, 1] * 2**8 + image[:, :, 2]

  def 		_lowerCamelCase	(self  : Union[str, Any]	, lowerCAmelCase_  : Dict    )   ->       Dict:
        if images.ndim == 3:
              snake_case_						       =							images[None, ...]
        snake_case_						       =							images[:, :, :, 3:]
        if images.shape[-1] == 6:
              snake_case_						       =							(images_depth * 2_55).round().astype("uint8"    )
              snake_case_						       =							[
                  Image.fromarray(self.rgblike_to_depthmap(_A    )	, mode="I;16"    ) for image_depth in images_depth
              ]
        elif images.shape[-1] == 4:
              snake_case_						       =							(images_depth * 6_55_35.0).astype(np.uintaa    )
              snake_case_						       =							[Image.fromarray(_A	, mode="I;16"    ) for image_depth in images_depth]
        else:
              raise Exception("Not supported"    )

        return pil_images


  def 		_lowerCamelCase	(self  : List[str]	, lowerCAmelCase_  : torch.FloatTensor	, lowerCAmelCase_  : str = "pil"	, lowerCAmelCase_  : Optional[List[bool]] = None	, )   ->       int:
        if not isinstance(_A	, torch.Tensor    ):
              raise ValueError(
                  f'''Input for postprocessing is in incorrect format: {type(_A    )}. We only support pytorch tensor'''    )
        if output_type not in ["latent", "pt", "np", "pil"]:
              snake_case_						       =							(
                  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"	, _A	, standard_warn=_A    )
              snake_case_						       =							'np'

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

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

        snake_case_						       =							self.pt_to_numpy(_A    )

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

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



'''simple docstring'''





def   snake_case_       (							_a     :							int = 1_000			):
      return sum(i for i in range(_a			) if i % 3 == 0 or i % 5 == 0			)


if __name__ == "__main__":
     print(F"""{solution() = }""")

