




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 		A__				(   __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: Any				    =CONFIG_NAME






	@register_to_config
	def __init__(	self				,     lowerCamelCase = True				,     lowerCamelCase = 8				,     lowerCamelCase = "lanczos"				,     lowerCamelCase = True				,     lowerCamelCase = False				,     )      ->   Any:




						"""simple docstring"""






						super().__init__()






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   PIL.Image.Image:




						"""simple docstring"""






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

						return pil_images






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   np.ndarray:




						"""simple docstring"""






						if not isinstance(lowerCamelCase				,     lowerCamelCase						):
											__magic_name__					:							List[str]		   =							[images]
						__magic_name__					:							Optional[Any]		   =							[np.array(lowerCamelCase						).astype(np.floataa						) / 2_5_5.0 for image in images]
						__magic_name__					:							List[str]		   =							np.stack(lowerCamelCase				,     axis=0						)

						return images






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   torch.FloatTensor:




						"""simple docstring"""






						if images.ndim == 3:
											__magic_name__					:							List[str]		   =							images[..., None]

						__magic_name__					:							Any		   =							torch.from_numpy(images.transpose(0				,     3				,     1				,     2						)						)
						return images






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   np.ndarray:




						"""simple docstring"""






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






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   Tuple:




						"""simple docstring"""






						return 2.0 * images - 1.0






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   Tuple:




						"""simple docstring"""






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






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   PIL.Image.Image:




						"""simple docstring"""






						__magic_name__					:							Dict		   =							image.convert('''RGB'''						)
						return image






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase = None				,     lowerCamelCase = None				,     )      ->   PIL.Image.Image:




						"""simple docstring"""






						if height is None:
											__magic_name__					:							Dict		   =							image.height
						if width is None:
											__magic_name__					:							str		   =							image.width

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






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase = None				,     lowerCamelCase = None				,     )      ->   torch.Tensor:




						"""simple docstring"""






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

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

						elif isinstance(image[0]				,     np.ndarray						):
											__magic_name__					:							Optional[int]		   =							np.concatenate(lowerCamelCase				,     axis=0						) if image[0].ndim == 4 else np.stack(lowerCamelCase				,     axis=0						)
											__magic_name__					:							Union[str, Any]		   =							self.numpy_to_pt(lowerCamelCase						)
											__magic_name__   , __magic_name__   , __magic_name__   , __magic_name__					:							List[str]		   =							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						):
											__magic_name__					:							Optional[int]		   =							torch.cat(lowerCamelCase				,     axis=0						) if image[0].ndim == 4 else torch.stack(lowerCamelCase				,     axis=0						)
											__magic_name__   , __magic_name__   , __magic_name__   , __magic_name__					:							Optional[Any]		   =							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]
						__magic_name__					:							List[str]		   =							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()}]'''				,     lowerCamelCase				,     )
											__magic_name__					:							List[Any]		   =							False

						if do_normalize:
											__magic_name__					:							Dict		   =							self.normalize(lowerCamelCase						)

						return image






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase = "pil"				,     lowerCamelCase = None				,     )      ->   Any:




						"""simple docstring"""






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

						if output_type == "latent":
											return image

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

						__magic_name__					:							str		   =							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

						__magic_name__					:							int		   =							self.pt_to_numpy(lowerCamelCase						)

						if output_type == "np":
											return image

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





class 		A__				(   __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: Optional[int]				    =CONFIG_NAME






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




						"""simple docstring"""






						super().__init__()






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   Tuple:




						"""simple docstring"""






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

						return pil_images






	@staticmethod
	def  lowercase      (	lowerCamelCase						)      ->   Any:




						"""simple docstring"""






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






	def  lowercase      (	self				,     lowerCamelCase						)      ->   str:




						"""simple docstring"""






						if images.ndim == 3:
											__magic_name__					:							Tuple		   =							images[None, ...]
						__magic_name__					:							Any		   =							images[:, :, :, 3:]
						if images.shape[-1] == 6:
											__magic_name__					:							Dict		   =							(images_depth * 255).round().astype('''uint8'''						)
											__magic_name__					:							Optional[int]		   =							[
											    Image.fromarray(self.rgblike_to_depthmap(lowerCamelCase						)				,     mode='''I;16'''						) for image_depth in images_depth
											]
						elif images.shape[-1] == 4:
											__magic_name__					:							Any		   =							(images_depth * 6_5_5_3_5.0).astype(np.uintaa						)
											__magic_name__					:							Tuple		   =							[Image.fromarray(lowerCamelCase				,     mode='''I;16'''						) for image_depth in images_depth]
						else:
											raise Exception('''Not supported'''						)

						return pil_images






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase = "pil"				,     lowerCamelCase = None				,     )      ->   List[Any]:




						"""simple docstring"""






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

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

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

						__magic_name__					:							Any		   =							self.pt_to_numpy(lowerCamelCase						)

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

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









def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		int:

    """simple docstring"""


    __UpperCamelCase     						=  0
    for ele in arr:
        total_sum += product_sum(lowercase_    ,   depth + 1					) if isinstance(lowercase_    ,   lowercase_					) else ele
    return total_sum * depth




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


    return product_sum(lowercase_    ,   1					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






