

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 							a__							( UpperCAmelCase							):







  """simple docstring"""




  UpperCAmelCase__   :			Optional[Any]      							=CONFIG_NAME


  @register_to_config
  def __init__(      self     :			str					,					UpperCAmelCase__     :			bool = True					,					UpperCAmelCase__     :			int = 8					,					UpperCAmelCase__     :			str = "lanczos"					,					UpperCAmelCase__     :			bool = True					,					UpperCAmelCase__     :			bool = False					,					)       ->int:
        """simple docstring"""







        super().__init__()


  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			np.ndarray		)       ->PIL.Image.Image:
        """simple docstring"""







        if images.ndim == 3:
              SCREAMING_SNAKE_CASE			:   str	  = images[None, ...]
        SCREAMING_SNAKE_CASE			:   str	  = (images * 2_5_5).round().astype("""uint8"""		)
        if images.shape[-1] == 1:
              # special case for grayscale (single channel) images
              SCREAMING_SNAKE_CASE			:   List[Any]	  = [Image.fromarray(image.squeeze()					,					mode="""L"""		) for image in images]
        else:
              SCREAMING_SNAKE_CASE			:   Dict	  = [Image.fromarray(UpperCAmelCase__		) for image in images]

        return pil_images


  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			Union[List[PIL.Image.Image], PIL.Image.Image]		)       ->np.ndarray:
        """simple docstring"""







        if not isinstance(UpperCAmelCase__					,					UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE			:   int	  = [images]
        SCREAMING_SNAKE_CASE			:   List[str]	  = [np.array(UpperCAmelCase__		).astype(np.floataa		) / 2_55.0 for image in images]
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = np.stack(UpperCAmelCase__					,					axis=0		)

        return images


  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			np.ndarray		)       ->torch.FloatTensor:
        """simple docstring"""







        if images.ndim == 3:
              SCREAMING_SNAKE_CASE			:   Dict	  = images[..., None]

        SCREAMING_SNAKE_CASE			:   int	  = torch.from_numpy(images.transpose(0					,					3					,					1					,					2		)		)
        return images


  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			torch.FloatTensor		)       ->np.ndarray:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = images.cpu().permute(0					,					2					,					3					,					1		).float().numpy()
        return images


  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			Union[str, Any]		)       ->Union[str, Any]:
        """simple docstring"""







        return 2.0 * images - 1.0


  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			Union[str, Any]		)       ->Optional[Any]:
        """simple docstring"""







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


  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			PIL.Image.Image		)       ->PIL.Image.Image:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[Any]	  = image.convert("""RGB"""		)
        return image


  def 			_lowercase   (      self     :			Union[str, Any]					,					UpperCAmelCase__     :			PIL.Image.Image					,					UpperCAmelCase__     :			Optional[int] = None					,					UpperCAmelCase__     :			Optional[int] = None					,					)       ->PIL.Image.Image:
        """simple docstring"""







        if height is None:
              SCREAMING_SNAKE_CASE			:   str	  = image.height
        if width is None:
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = image.width

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


  def 			_lowercase   (      self     :			Optional[Any]					,					UpperCAmelCase__     :			Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]					,					UpperCAmelCase__     :			Optional[int] = None					,					UpperCAmelCase__     :			Optional[int] = None					,					)       ->torch.Tensor:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[int]	  = (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(UpperCAmelCase__					,					UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE			:   Any	  = [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:
                    SCREAMING_SNAKE_CASE			:   Optional[int]	  = [self.convert_to_rgb(UpperCAmelCase__		) for i in image]
              if self.config.do_resize:
                    SCREAMING_SNAKE_CASE			:   str	  = [self.resize(UpperCAmelCase__					,					UpperCAmelCase__					,					UpperCAmelCase__		) for i in image]
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = self.pil_to_numpy(UpperCAmelCase__		)  # to np
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = self.numpy_to_pt(UpperCAmelCase__		)  # to pt

        elif isinstance(image[0]					,					np.ndarray		):
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = np.concatenate(UpperCAmelCase__					,					axis=0		) if image[0].ndim == 4 else np.stack(UpperCAmelCase__					,					axis=0		)
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = self.numpy_to_pt(UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   int	  = 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		):
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = torch.cat(UpperCAmelCase__					,					axis=0		) if image[0].ndim == 4 else torch.stack(UpperCAmelCase__					,					axis=0		)
              SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Optional[Any]	  = 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]
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = 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__					,					)
              SCREAMING_SNAKE_CASE			:   List[str]	  = False

        if do_normalize:
              SCREAMING_SNAKE_CASE			:   Dict	  = self.normalize(UpperCAmelCase__		)

        return image




  def 			_lowercase   (      self     :			Any					,					UpperCAmelCase__     :			torch.FloatTensor					,					UpperCAmelCase__     :			str = "pil"					,					UpperCAmelCase__     :			Optional[List[bool]] = None					,					)       ->str:
        """simple docstring"""







        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"]:
              SCREAMING_SNAKE_CASE			:   List[str]	  = (
                  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__		)
              SCREAMING_SNAKE_CASE			:   Dict	  = """np"""

        if output_type == "latent":
              return image

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

        SCREAMING_SNAKE_CASE			:   Dict	  = 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

        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = self.pt_to_numpy(UpperCAmelCase__		)

        if output_type == "np":
              return image

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







class 							a__							( UpperCAmelCase							):







  """simple docstring"""




  UpperCAmelCase__   :			Union[str, Any]      							=CONFIG_NAME


  @register_to_config
  def __init__(      self     :			Optional[Any]					,					UpperCAmelCase__     :			bool = True					,					UpperCAmelCase__     :			int = 8					,					UpperCAmelCase__     :			str = "lanczos"					,					UpperCAmelCase__     :			bool = True					,					)       ->List[str]:
        """simple docstring"""







        super().__init__()


  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			Any		)       ->str:
        """simple docstring"""







        if images.ndim == 3:
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = images[None, ...]
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = (images * 2_5_5).round().astype("""uint8"""		)
        if images.shape[-1] == 1:
              # special case for grayscale (single channel) images
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = [Image.fromarray(image.squeeze()					,					mode="""L"""		) for image in images]
        else:
              SCREAMING_SNAKE_CASE			:   List[Any]	  = [Image.fromarray(image[:, :, :3]		) for image in images]

        return pil_images


  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			Dict		)       ->int:
        """simple docstring"""







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


  def 			_lowercase   (      self     :			int					,					UpperCAmelCase__     :			str		)       ->int:
        """simple docstring"""







        if images.ndim == 3:
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = images[None, ...]
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = images[:, :, :, 3:]
        if images.shape[-1] == 6:
              SCREAMING_SNAKE_CASE			:   Dict	  = (images_depth * 2_5_5).round().astype("""uint8"""		)
              SCREAMING_SNAKE_CASE			:   Any	  = [
                  Image.fromarray(self.rgblike_to_depthmap(UpperCAmelCase__		)					,					mode="""I;16"""		) for image_depth in images_depth
              ]
        elif images.shape[-1] == 4:
              SCREAMING_SNAKE_CASE			:   List[Any]	  = (images_depth * 6_55_35.0).astype(np.uintaa		)
              SCREAMING_SNAKE_CASE			:   List[str]	  = [Image.fromarray(UpperCAmelCase__					,					mode="""I;16"""		) for image_depth in images_depth]
        else:
              raise Exception("""Not supported"""		)

        return pil_images




  def 			_lowercase   (      self     :			str					,					UpperCAmelCase__     :			torch.FloatTensor					,					UpperCAmelCase__     :			str = "pil"					,					UpperCAmelCase__     :			Optional[List[bool]] = None					,					)       ->Optional[int]:
        """simple docstring"""







        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"]:
              SCREAMING_SNAKE_CASE			:   List[Any]	  = (
                  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__		)
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = """np"""

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

        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = torch.stack(
            [self.denormalize(image[i]		) if do_denormalize[i] else image[i] for i in range(image.shape[0]		)]		)

        SCREAMING_SNAKE_CASE			:   Optional[int]	  = self.pt_to_numpy(UpperCAmelCase__		)

        if output_type == "np":
              if image.shape[-1] == 6:
                    SCREAMING_SNAKE_CASE			:   int	  = np.stack([self.rgblike_to_depthmap(im[:, :, 3:]		) for im in image]					,					axis=0		)
              else:
                    SCREAMING_SNAKE_CASE			:   List[Any]	  = 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"		)







import itertools






def        __lowercase   (  _A = 1000000      )  ->    int:
      SCREAMING_SNAKE_CASE			:   Tuple	  = [1]

      for i in itertools.count(len(_A      )      ):
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = 0
            for j in itertools.count(1      ):
                  SCREAMING_SNAKE_CASE			:   str	  = -1 if j % 2 == 0 else +1
                  SCREAMING_SNAKE_CASE			:   Any	  = (j * j * 3 - j) // 2
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number
                  index += j
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number

            if item == 0:
                  return i
            partitions.append(_A      )

      return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")




