



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 		_lowercase		(							snake_case_						):
   _UpperCAmelCase  						=      CONFIG_NAME

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


    """simple docstring"""






    super().__init__()

   @staticmethod
   def 		A				(   __lowerCAmelCase      :							np.ndarray				)							->					PIL.Image.Image:


    """simple docstring"""






    if images.ndim == 3:
     a							=		images[None, ...]
    a							=		(images * 255).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(_A				) for image in images]

    return pil_images

   @staticmethod
   def 		A				(   __lowerCAmelCase      :							Union[List[PIL.Image.Image], PIL.Image.Image]				)							->					np.ndarray:


    """simple docstring"""






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

    return images

   @staticmethod
   def 		A				(   __lowerCAmelCase      :							np.ndarray				)							->					torch.FloatTensor:


    """simple docstring"""






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

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

   @staticmethod
   def 		A				(   __lowerCAmelCase      :							torch.FloatTensor				)							->					np.ndarray:


    """simple docstring"""






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

   @staticmethod
   def 		A				(   __lowerCAmelCase      :							Optional[int]				)							->					Optional[Any]:


    """simple docstring"""






    return 2.0 * images - 1.0

   @staticmethod
   def 		A				(   __lowerCAmelCase      :							Optional[int]				)							->					Tuple:


    """simple docstring"""






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

   @staticmethod
   def 		A				(   __lowerCAmelCase      :							PIL.Image.Image				)							->					PIL.Image.Image:


    """simple docstring"""






    a							=		image.convert("RGB"				)
    return image

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


    """simple docstring"""






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

    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 		A				(   self      :							Dict				, __lowerCAmelCase      :							Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]				, __lowerCAmelCase      :							Optional[int] = None				, __lowerCAmelCase      :							Optional[int] = None				, )							->					torch.Tensor:


    """simple docstring"""






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

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

    if do_normalize:
     a							=		self.normalize(_A				)

    return image




   def 		A				(   self      :							Any				, __lowerCAmelCase      :							torch.FloatTensor				, __lowerCAmelCase      :							str = "pil"				, __lowerCAmelCase      :							Optional[List[bool]] = None				, )							->					int:


    """simple docstring"""






    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"]:
     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"				, _A				, standard_warn=_A				)
     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(_A				)

    if output_type == "np":
     return image

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



class 		_lowercase		(							snake_case_						):
   _UpperCAmelCase  						=      CONFIG_NAME

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


    """simple docstring"""






    super().__init__()

   @staticmethod
   def 		A				(   __lowerCAmelCase      :							int				)							->					Dict:


    """simple docstring"""






    if images.ndim == 3:
     a							=		images[None, ...]
    a							=		(images * 255).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 		A				(   __lowerCAmelCase      :							Optional[Any]				)							->					Optional[int]:


    """simple docstring"""






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

   def 		A				(   self      :							List[Any]				, __lowerCAmelCase      :							Optional[int]				)							->					Any:


    """simple docstring"""






    if images.ndim == 3:
     a							=		images[None, ...]
    a							=		images[:, :, :, 3:]
    if images.shape[-1] == 6:
     a							=		(images_depth * 255).round().astype("uint8"				)
     a							=		[
         Image.fromarray(self.rgblike_to_depthmap(_A				)				, mode="I;16"				) for image_depth in images_depth
     ]
    elif images.shape[-1] == 4:
     a							=		(images_depth * 6_5535.0).astype(np.uintaa				)
     a							=		[Image.fromarray(_A				, mode="I;16"				) for image_depth in images_depth]
    else:
     raise Exception("Not supported"				)

    return pil_images




   def 		A				(   self      :							Any				, __lowerCAmelCase      :							torch.FloatTensor				, __lowerCAmelCase      :							str = "pil"				, __lowerCAmelCase      :							Optional[List[bool]] = None				, )							->					Dict:


    """simple docstring"""






    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"]:
     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"				, _A				, standard_warn=_A				)
     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(_A				)

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








def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	if len(str(__a ) ) == 1 or len(str(__a ) ) == 1:
		return a * b

	snake_case_		:      Dict           =						max(len(str(__a ) ) ,			len(str(__a ) ) )
	snake_case_		:      Tuple           =						ma // 2

	snake_case_    ,snake_case_		:      Tuple           =						divmod(__a ,			10**ma )
	snake_case_    ,snake_case_		:      int           =						divmod(__a ,			10**ma )

	snake_case_		:      str           =						karatsuba(__a ,			__a )
	snake_case_		:      List[str]           =						karatsuba((aa + aa) ,			(ba + ba) )
	snake_case_		:      Optional[Any]           =						karatsuba(__a ,			__a )

	return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)


def 				SCREAMING_SNAKE_CASE__    (     ):
	print(karatsuba(1_54_63 ,			2_34_89 ) )


if __name__ == "__main__":
						main()

