
"""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  lowercase				(   UpperCAmelCase_		):
			lowercase			     =    CONFIG_NAME






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





				"""simple docstring"""


				super().__init__()






			@staticmethod
			def 				UpperCAmelCase       (SCREAMING_SNAKE_CASE_     :  Dict )					->  PIL.Image.Image:





				"""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(SCREAMING_SNAKE_CASE_ ) for image in images]

				return pil_images






			@staticmethod
			def 				UpperCAmelCase       (SCREAMING_SNAKE_CASE_     :  Tuple )					->  np.ndarray:





				"""simple docstring"""


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

				return images






			@staticmethod
			def 				UpperCAmelCase       (SCREAMING_SNAKE_CASE_     :  Union[str, Any] )					->  torch.FloatTensor:





				"""simple docstring"""


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

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






			@staticmethod
			def 				UpperCAmelCase       (SCREAMING_SNAKE_CASE_     :  int )					->  np.ndarray:





				"""simple docstring"""


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






			@staticmethod
			def 				UpperCAmelCase       (SCREAMING_SNAKE_CASE_     :  Optional[int] )					->  Union[str, Any]:





				"""simple docstring"""


				return 2.0 * images - 1.0






			@staticmethod
			def 				UpperCAmelCase       (SCREAMING_SNAKE_CASE_     :  Any )					->  Any:





				"""simple docstring"""


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






			@staticmethod
			def 				UpperCAmelCase       (SCREAMING_SNAKE_CASE_     :  Tuple )					->  PIL.Image.Image:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  int			,SCREAMING_SNAKE_CASE_     :  Optional[int]			,SCREAMING_SNAKE_CASE_     :  Tuple = None			,SCREAMING_SNAKE_CASE_     :  Optional[Any] = None			,)					->  PIL.Image.Image:





				"""simple docstring"""


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

				lowerCAmelCase       ,					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 				UpperCAmelCase       (self     :  Dict			,SCREAMING_SNAKE_CASE_     :  List[Any]			,SCREAMING_SNAKE_CASE_     :  Any = None			,SCREAMING_SNAKE_CASE_     :  List[str] = None			,)					->  torch.Tensor:





				"""simple docstring"""


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

				if isinstance(image[0]			,PIL.Image.Image ):
					if self.config.do_convert_rgb:
						lowerCAmelCase				       =     [self.convert_to_rgb(SCREAMING_SNAKE_CASE_ ) for i in image]
					if self.config.do_resize:
						lowerCAmelCase				       =     [self.resize(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ ) for i in image]
					lowerCAmelCase				       =     self.pil_to_numpy(SCREAMING_SNAKE_CASE_ )  # to np
					lowerCAmelCase				       =     self.numpy_to_pt(SCREAMING_SNAKE_CASE_ )  # to pt

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

				if do_normalize:
					lowerCAmelCase				       =     self.normalize(SCREAMING_SNAKE_CASE_ )

				return image






			def 				UpperCAmelCase       (self     :  str			,SCREAMING_SNAKE_CASE_     :  List[str]			,SCREAMING_SNAKE_CASE_     :  Tuple = "pil"			,SCREAMING_SNAKE_CASE_     :  Optional[int] = None			,)					->  Optional[Any]:





				"""simple docstring"""


				if not isinstance(SCREAMING_SNAKE_CASE_			,torch.Tensor ):
					raise ValueError(
					    F"""Input for postprocessing is in incorrect format: {type(SCREAMING_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'''			,SCREAMING_SNAKE_CASE_			,standard_warn=SCREAMING_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(SCREAMING_SNAKE_CASE_ )

				if output_type == "np":
					return image

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





class  lowercase				(   UpperCAmelCase_		):
			lowercase			     =    CONFIG_NAME






			@register_to_config
			def __init__(self     :  Union[str, Any]			,SCREAMING_SNAKE_CASE_     :  str = True			,SCREAMING_SNAKE_CASE_     :  int = 8			,SCREAMING_SNAKE_CASE_     :  Tuple = "lanczos"			,SCREAMING_SNAKE_CASE_     :  str = True			,)					->  Union[str, Any]:





				"""simple docstring"""


				super().__init__()






			@staticmethod
			def 				UpperCAmelCase       (SCREAMING_SNAKE_CASE_     :  Optional[Any] )					->  Any:





				"""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 				UpperCAmelCase       (SCREAMING_SNAKE_CASE_     :  Optional[Any] )					->  Tuple:





				"""simple docstring"""


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






			def 				UpperCAmelCase       (self     :  Union[str, Any]			,SCREAMING_SNAKE_CASE_     :  List[Any] )					->  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(SCREAMING_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(SCREAMING_SNAKE_CASE_			,mode='''I;16''' ) for image_depth in images_depth]
				else:
					raise Exception('''Not supported''' )

				return pil_images






			def 				UpperCAmelCase       (self     :  List[str]			,SCREAMING_SNAKE_CASE_     :  Optional[int]			,SCREAMING_SNAKE_CASE_     :  Any = "pil"			,SCREAMING_SNAKE_CASE_     :  Optional[Any] = None			,)					->  List[Any]:





				"""simple docstring"""


				if not isinstance(SCREAMING_SNAKE_CASE_			,torch.Tensor ):
					raise ValueError(
					    F"""Input for postprocessing is in incorrect format: {type(SCREAMING_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'''			,SCREAMING_SNAKE_CASE_			,standard_warn=SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE_ ), self.numpy_to_depth(SCREAMING_SNAKE_CASE_ )
				else:
					raise Exception(F"""This type {output_type} is not supported""" )




"""simple docstring"""






def __magic_name__  (							_lowerCamelCase:		list[int] )				->	int:

	'''simple docstring'''




	lowerCAmelCase				       =     len(_lowerCamelCase )
	if n in (0, 1):
		return 0
	return sum(query * (n - i - 1) for i, query in enumerate(sorted(_lowerCamelCase ) ) )


if __name__ == "__main__":
					import doctest

					doctest.testmod()


