





"""simple docstring"""


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

 '''simple docstring'''






 return int(_UpperCAmelCase						) if x - int(_UpperCAmelCase						) <= 0 else int(_UpperCAmelCase						) + 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()








"""simple docstring"""




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    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					Union[str, Any]             =  CONFIG_NAME

      @register_to_config
      def __init__(	self   , snake_case_ = True   , snake_case_ = 8   , snake_case_ = "lanczos"   , snake_case_ = True   , snake_case_ = False   , ):

        """simple docstring"""

        super().__init__()

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

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

        return pil_images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if not isinstance(snake_case_   , snake_case_    ):
          A_    :			int									=    [images]
        A_    :			int									=    [np.array(snake_case_    ).astype(np.floataa    ) / 2_55.0 for image in images]
        A_    :			Tuple									=    np.stack(snake_case_   , axis=0    )

        return images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			Any									=    images[..., None]

        A_    :			Dict									=    torch.from_numpy(images.transpose(0   , 3   , 1   , 2    )    )
        return images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    images.cpu().permute(0   , 2   , 3   , 1    ).float().numpy()
        return images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        return 2.0 * images - 1.0

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

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

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        A_    :			List[Any]									=    image.convert('RGB'    )
        return image

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , ):

        """simple docstring"""

        if height is None:
          A_    :			int									=    image.height
        if width is None:
          A_    :			List[Any]									=    image.width

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , ):

        """simple docstring"""

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

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

        elif isinstance(image[0]   , np.ndarray    ):
          A_    :			Optional[Any]									=    np.concatenate(snake_case_   , axis=0    ) if image[0].ndim == 4 else np.stack(snake_case_   , axis=0    )
          A_    :			Optional[Any]									=    self.numpy_to_pt(snake_case_    )
          A_		,			A_		,			A_		,			A_    :			Tuple									=    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_    :			int									=    torch.cat(snake_case_   , axis=0    ) if image[0].ndim == 4 else torch.stack(snake_case_   , axis=0    )
          A_		,			A_		,			A_		,			A_    :			str									=    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_    :			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()}]"""   , snake_case_   , )
          A_    :			Optional[int]									=    False

        if do_normalize:
          A_    :			Any									=    self.normalize(snake_case_    )

        return image

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = "pil"   , snake_case_ = None   , ):

        """simple docstring"""

        if not isinstance(snake_case_   , torch.Tensor    ):
          raise ValueError(
              F"""Input for postprocessing is in incorrect format: {type(snake_case_    )}. We only support pytorch tensor"""    )
        if output_type not in ["latent", "pt", "np", "pil"]:
          A_    :			Optional[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'   , snake_case_   , standard_warn=snake_case_    )
          A_    :			Dict									=    'np'

        if output_type == "latent":
          return image

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

        A_    :			List[Any]									=    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_    :			Optional[Any]									=    self.pt_to_numpy(snake_case_    )

        if output_type == "np":
          return image

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


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					str             =  CONFIG_NAME

      @register_to_config
      def __init__(	self   , snake_case_ = True   , snake_case_ = 8   , snake_case_ = "lanczos"   , snake_case_ = True   , ):

        """simple docstring"""

        super().__init__()

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

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

        return pil_images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

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

        return pil_images

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = "pil"   , snake_case_ = None   , ):

        """simple docstring"""

        if not isinstance(snake_case_   , torch.Tensor    ):
          raise ValueError(
              F"""Input for postprocessing is in incorrect format: {type(snake_case_    )}. We only support pytorch tensor"""    )
        if output_type not in ["latent", "pt", "np", "pil"]:
          A_    :			Optional[int]									=    (
              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'   , snake_case_   , standard_warn=snake_case_    )
          A_    :			Any									=    'np'

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

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

        A_    :			Optional[Any]									=    self.pt_to_numpy(snake_case_    )

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

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