



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 (				SCREAMING_SNAKE_CASE):







		'''simple docstring'''






		UpperCamelCase__			: Tuple             =   CONFIG_NAME


		@register_to_config
		def __init__(							self					,					a_ = True					,					a_ = 8					,					a_ = "lanczos"					,					a_ = True					,					a_ = False					,					):
						super().__init__()


		@staticmethod
		def _a (							a_						):





						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 (							a_						):





						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 (							a_						):





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

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


		@staticmethod
		def _a (							a_						):
						a__			 =						images.cpu().permute(0					,					2					,					3					,					1						).float().numpy()
						return images


		@staticmethod
		def _a (							a_						):
						return 2.0 * images - 1.0


		@staticmethod
		def _a (							a_						):
						return (images / 2 + 0.5).clamp(0					,					1						)


		@staticmethod
		def _a (							a_						):
						a__			 =						image.convert("""RGB"""						)
						return image


		def _a (							self					,					a_					,					a_ = None					,					a_ = None					,					):





						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					,					a_					,					a_ = None					,					a_ = None					,					):
						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					,					a_					,					a_ = "pil"					,					a_ = None					,					):





						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 	__snake_case (				SCREAMING_SNAKE_CASE):







		'''simple docstring'''






		UpperCamelCase__			: Dict             =   CONFIG_NAME


		@register_to_config
		def __init__(							self					,					a_ = True					,					a_ = 8					,					a_ = "lanczos"					,					a_ = True					,					):
						super().__init__()


		@staticmethod
		def _a (							a_						):





						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 (							a_						):
						return image[:, :, 1] * 2**8 + image[:, :, 2]


		def _a (							self					,					a_						):





						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 * 65_535.0).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					,					a_					,					a_ = "pil"					,					a_ = None					,					):





						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      A_ (		__a       :			float						):

				"""simple docstring"""
				return int(__a						) if x - int(__a						) >= 0 else int(__a						) - 1


if __name__ == "__main__":
	import doctest

	doctest.testmod()

