





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(						_a):

  """simple docstring"""







  lowerCamelCase__      					=   CONFIG_NAME

  @register_to_config
  def __init__( self  ,    UpperCAmelCase = True  ,    UpperCAmelCase = 8  ,    UpperCAmelCase = "lanczos"  ,    UpperCAmelCase = True  ,    UpperCAmelCase = False  ,    )-> Optional[Any]:
     super().__init__()

  @staticmethod
  def 		SCREAMING_SNAKE_CASE__	( UpperCAmelCase     )-> PIL.Image.Image:
     if images.ndim == 3:
        __A				 =       images[None, ...]
     __A				 =       (images * 2_55).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(UpperCAmelCase     ) for image in images]

     return pil_images

  @staticmethod
  def 		SCREAMING_SNAKE_CASE__	( UpperCAmelCase     )-> np.ndarray:
     if not isinstance(UpperCAmelCase  ,    UpperCAmelCase     ):
        __A				 =       [images]
     __A				 =       [np.array(UpperCAmelCase     ).astype(np.floataa     ) / 255.0 for image in images]
     __A				 =       np.stack(UpperCAmelCase  ,    axis=0     )

     return images

  @staticmethod
  def 		SCREAMING_SNAKE_CASE__	( UpperCAmelCase     )-> torch.FloatTensor:
     if images.ndim == 3:
        __A				 =       images[..., None]

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

  @staticmethod
  def 		SCREAMING_SNAKE_CASE__	( UpperCAmelCase     )-> np.ndarray:
     __A				 =       images.cpu().permute(0  ,    2  ,    3  ,    1     ).float().numpy()
     return images

  @staticmethod
  def 		SCREAMING_SNAKE_CASE__	( UpperCAmelCase     )-> Optional[int]:
     return 2.0 * images - 1.0

  @staticmethod
  def 		SCREAMING_SNAKE_CASE__	( UpperCAmelCase     )-> Optional[Any]:
     return (images / 2 + 0.5).clamp(0  ,    1     )

  @staticmethod
  def 		SCREAMING_SNAKE_CASE__	( UpperCAmelCase     )-> PIL.Image.Image:
     __A				 =       image.convert('''RGB'''     )
     return image

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase = None  ,    UpperCAmelCase = None  ,    )-> PIL.Image.Image:
     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 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase = None  ,    UpperCAmelCase = None  ,    )-> torch.Tensor:
     __A				 =       (PIL.Image.Image, np.ndarray, torch.Tensor)
     if isinstance(UpperCAmelCase  ,    UpperCAmelCase     ):
        __A				 =       [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:
           __A				 =       [self.convert_to_rgb(UpperCAmelCase     ) for i in image]
        if self.config.do_resize:
           __A				 =       [self.resize(UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase     ) for i in image]
        __A				 =       self.pil_to_numpy(UpperCAmelCase     )  # to np
        __A				 =       self.numpy_to_pt(UpperCAmelCase     )  # to pt

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

     if do_normalize:
        __A				 =       self.normalize(UpperCAmelCase     )

     return image





  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase = "pil"  ,    UpperCAmelCase = None  ,    )-> Any:
     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"]:
        __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'''  ,    UpperCAmelCase  ,    standard_warn=UpperCAmelCase     )
        __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(UpperCAmelCase     )

     if output_type == "np":
        return image

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







class 						_lowerCAmelCase(						_a):

  """simple docstring"""







  lowerCamelCase__      					=   CONFIG_NAME

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

  @staticmethod
  def 		SCREAMING_SNAKE_CASE__	( UpperCAmelCase     )-> int:
     if images.ndim == 3:
        __A				 =       images[None, ...]
     __A				 =       (images * 2_55).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 		SCREAMING_SNAKE_CASE__	( UpperCAmelCase     )-> Tuple:
     return image[:, :, 1] * 2**8 + image[:, :, 2]

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> int:
     if images.ndim == 3:
        __A				 =       images[None, ...]
     __A				 =       images[:, :, :, 3:]
     if images.shape[-1] == 6:
        __A				 =       (images_depth * 2_55).round().astype('''uint8'''     )
        __A				 =       [
            Image.fromarray(self.rgblike_to_depthmap(UpperCAmelCase     )  ,    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(UpperCAmelCase  ,    mode='''I;16'''     ) for image_depth in images_depth]
     else:
        raise Exception('''Not supported'''     )

     return pil_images





  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase = "pil"  ,    UpperCAmelCase = None  ,    )-> str:
     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"]:
        __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'''  ,    UpperCAmelCase  ,    standard_warn=UpperCAmelCase     )
        __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(UpperCAmelCase     )

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








from __future__ import annotations
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						list:


   '''simple docstring'''







   __A				 =       [None] * (len(snake_case     ) + len(snake_case     ))

   __A				 =       0  # pointer to current index for left Half
   __A				 =       0  # pointer to current index for the right Half
   __A				 =       0  # pointer to current index for the sorted array Half

   while pointera < len(snake_case     ) and pointera < len(snake_case     ):
      if left_half[pointera] < right_half[pointera]:
         __A				 =       left_half[pointera]
         pointera += 1
         index += 1
      else:
         __A				 =       right_half[pointera]
         pointera += 1
         index += 1
   while pointera < len(snake_case     ):
      __A				 =       left_half[pointera]
      pointera += 1
      index += 1

   while pointera < len(snake_case     ):
      __A				 =       right_half[pointera]
      pointera += 1
      index += 1

   return sorted_array
def 				__UpperCamelCase  (		snake_case     )  ->						list:


   '''simple docstring'''







   if len(snake_case     ) <= 1:
      return array
   # the actual formula to calculate the middle element = left + (right - left) // 2
   # this avoids integer overflow in case of large N
   __A				 =       0 + (len(snake_case     ) - 0) // 2

   # Split the array into halves till the array length becomes equal to One
   # merge the arrays of single length returned by mergeSort function and
   # pass them into the merge arrays function which merges the array
   __A				 =       array[:middle]
   __A				 =       array[middle:]

   return merge(merge_sort(snake_case     )    ,	merge_sort(snake_case     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

