





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   __snake_case				(			snake_case__   ):






    '''simple docstring'''
    _lowerCAmelCase     =	CONFIG_NAME





    @register_to_config
    def __init__(   self						:		str			,						snake_case						:		bool = True			,						snake_case						:		int = 8			,						snake_case						:		str = "lanczos"			,						snake_case						:		bool = True			,						snake_case						:		bool = False			,						):

      """simple docstring"""





      super().__init__()





    @staticmethod
    def 	A		(   snake_case						:		np.ndarray							):

      """simple docstring"""





      if images.ndim == 3:
        __lowerCAmelCase			=				images[None, ...]
      __lowerCAmelCase			=				(images * 255).round().astype('''uint8'''							)
      if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        __lowerCAmelCase			=				[Image.fromarray(image.squeeze()			,						mode='''L'''							) for image in images]
      else:
        __lowerCAmelCase			=				[Image.fromarray(snake_case							) for image in images]

      return pil_images





    @staticmethod
    def 	A		(   snake_case						:		Union[List[PIL.Image.Image], PIL.Image.Image]							):

      """simple docstring"""





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

      return images





    @staticmethod
    def 	A		(   snake_case						:		np.ndarray							):

      """simple docstring"""





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

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





    @staticmethod
    def 	A		(   snake_case						:		torch.FloatTensor							):

      """simple docstring"""





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





    @staticmethod
    def 	A		(   snake_case						:		Union[str, Any]							):

      """simple docstring"""





      return 2.0 * images - 1.0





    @staticmethod
    def 	A		(   snake_case						:		List[str]							):

      """simple docstring"""





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





    @staticmethod
    def 	A		(   snake_case						:		PIL.Image.Image							):

      """simple docstring"""





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





    def 	A		(   self						:		List[str]			,						snake_case						:		PIL.Image.Image			,						snake_case						:		Optional[int] = None			,						snake_case						:		Optional[int] = None			,						):

      """simple docstring"""





      if height is None:
        __lowerCAmelCase			=				image.height
      if width is None:
        __lowerCAmelCase			=				image.width

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





    def 	A		(   self						:		Any			,						snake_case						:		Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]			,						snake_case						:		Optional[int] = None			,						snake_case						:		Optional[int] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				(PIL.Image.Image, np.ndarray, torch.Tensor)
      if isinstance(snake_case			,						snake_case							):
        __lowerCAmelCase			=				[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:
          __lowerCAmelCase			=				[self.convert_to_rgb(snake_case							) for i in image]
        if self.config.do_resize:
          __lowerCAmelCase			=				[self.resize(snake_case			,						snake_case			,						snake_case							) for i in image]
        __lowerCAmelCase			=				self.pil_to_numpy(snake_case							)  # to np
        __lowerCAmelCase			=				self.numpy_to_pt(snake_case							)  # to pt

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

      if do_normalize:
        __lowerCAmelCase			=				self.normalize(snake_case							)

      return image





    def 	A		(   self						:		Any			,						snake_case						:		torch.FloatTensor			,						snake_case						:		str = "pil"			,						snake_case						:		Optional[List[bool]] = 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"]:
        __lowerCAmelCase			=				(
            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							)
        __lowerCAmelCase			=				'np'

      if output_type == "latent":
        return image

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

      __lowerCAmelCase			=				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

      __lowerCAmelCase			=				self.pt_to_numpy(snake_case							)

      if output_type == "np":
        return image

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


class   __snake_case				(			snake_case__   ):






    '''simple docstring'''
    _lowerCAmelCase     =	CONFIG_NAME





    @register_to_config
    def __init__(   self						:		List[Any]			,						snake_case						:		bool = True			,						snake_case						:		int = 8			,						snake_case						:		str = "lanczos"			,						snake_case						:		bool = True			,						):

      """simple docstring"""





      super().__init__()





    @staticmethod
    def 	A		(   snake_case						:		str							):

      """simple docstring"""





      if images.ndim == 3:
        __lowerCAmelCase			=				images[None, ...]
      __lowerCAmelCase			=				(images * 255).round().astype('''uint8'''							)
      if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        __lowerCAmelCase			=				[Image.fromarray(image.squeeze()			,						mode='''L'''							) for image in images]
      else:
        __lowerCAmelCase			=				[Image.fromarray(image[:, :, :3]							) for image in images]

      return pil_images





    @staticmethod
    def 	A		(   snake_case						:		Tuple							):

      """simple docstring"""





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





    def 	A		(   self						:		Dict			,						snake_case						:		Optional[int]							):

      """simple docstring"""





      if images.ndim == 3:
        __lowerCAmelCase			=				images[None, ...]
      __lowerCAmelCase			=				images[:, :, :, 3:]
      if images.shape[-1] == 6:
        __lowerCAmelCase			=				(images_depth * 255).round().astype('''uint8'''							)
        __lowerCAmelCase			=				[
            Image.fromarray(self.rgblike_to_depthmap(snake_case							)			,						mode='''I;16'''							) for image_depth in images_depth
        ]
      elif images.shape[-1] == 4:
        __lowerCAmelCase			=				(images_depth * 65_535.0).astype(np.uintaa							)
        __lowerCAmelCase			=				[Image.fromarray(snake_case			,						mode='''I;16'''							) for image_depth in images_depth]
      else:
        raise Exception('''Not supported'''							)

      return pil_images





    def 	A		(   self						:		List[Any]			,						snake_case						:		torch.FloatTensor			,						snake_case						:		str = "pil"			,						snake_case						:		Optional[List[bool]] = 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"]:
        __lowerCAmelCase			=				(
            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							)
        __lowerCAmelCase			=				'np'

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

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

      __lowerCAmelCase			=				self.pt_to_numpy(snake_case							)

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











def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		list):



  '''simple docstring'''






  if len(__lowercase) <= 1:
    return data
  else:
    return [
        *quick_sort([e for e in data[1:] if e <= data[0]]),
        data[0],
        *quick_sort([e for e in data[1:] if e > data[0]]),
    ]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
