




'''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 				_a   (a__  ):





     '''simple docstring'''



     lowerCAmelCase_     :    int									=				CONFIG_NAME







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







     @staticmethod
     def  snake_case_			(							__a       )			->							PIL.Image.Image:
          if images.ndim == 3:
               snake_case :			Union[str, Any]					   = images[None, ...]
          snake_case :			List[str]					   = (images * 255).round().astype("""uint8"""       )
          if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               snake_case :			Optional[int]					   = [Image.fromarray(image.squeeze()       ,mode="""L"""       ) for image in images]
          else:
               snake_case :			Any					   = [Image.fromarray(__a       ) for image in images]

          return pil_images







     @staticmethod
     def  snake_case_			(							__a       )			->							np.ndarray:
          if not isinstance(__a       ,__a       ):
               snake_case :			List[Any]					   = [images]
          snake_case :			int					   = [np.array(__a       ).astype(np.floataa       ) / 255.0 for image in images]
          snake_case :			Optional[Any]					   = np.stack(__a       ,axis=0       )

          return images







     @staticmethod
     def  snake_case_			(							__a       )			->							torch.FloatTensor:
          if images.ndim == 3:
               snake_case :			str					   = images[..., None]

          snake_case :			Tuple					   = torch.from_numpy(images.transpose(0       ,3       ,1       ,2       )       )
          return images







     @staticmethod
     def  snake_case_			(							__a       )			->							np.ndarray:
          snake_case :			List[str]					   = images.cpu().permute(0       ,2       ,3       ,1       ).float().numpy()
          return images







     @staticmethod
     def  snake_case_			(							__a       )			->							List[str]:
          return 2.0 * images - 1.0







     @staticmethod
     def  snake_case_			(							__a       )			->							Any:
          return (images / 2 + 0.5).clamp(0       ,1       )







     @staticmethod
     def  snake_case_			(							__a       )			->							PIL.Image.Image:
          snake_case :			str					   = image.convert("""RGB"""       )
          return image







     def  snake_case_			(							self       ,__a       ,__a = None       ,__a = None       ,)			->							PIL.Image.Image:
          if height is None:
               snake_case :			Tuple					   = image.height
          if width is None:
               snake_case :			Tuple					   = image.width

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







     def  snake_case_			(							self       ,__a       ,__a = None       ,__a = None       ,)			->							torch.Tensor:
          snake_case :			Any					   = (PIL.Image.Image, np.ndarray, torch.Tensor)
          if isinstance(__a       ,__a       ):
               snake_case :			Dict					   = [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:
                    snake_case :			str					   = [self.convert_to_rgb(__a       ) for i in image]
               if self.config.do_resize:
                    snake_case :			str					   = [self.resize(__a       ,__a       ,__a       ) for i in image]
               snake_case :			Optional[Any]					   = self.pil_to_numpy(__a       )  # to np
               snake_case :			Union[str, Any]					   = self.numpy_to_pt(__a       )  # to pt

          elif isinstance(image[0]       ,np.ndarray       ):
               snake_case :			Optional[Any]					   = np.concatenate(__a       ,axis=0       ) if image[0].ndim == 4 else np.stack(__a       ,axis=0       )
               snake_case :			str					   = self.numpy_to_pt(__a       )
               snake_case :			Any					   = 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       ):
               snake_case :			List[str]					   = torch.cat(__a       ,axis=0       ) if image[0].ndim == 4 else torch.stack(__a       ,axis=0       )
               snake_case :			int					   = 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]
          snake_case :			Optional[Any]					   = 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       ,)
               snake_case :			Dict					   = False

          if do_normalize:
               snake_case :			List[str]					   = self.normalize(__a       )

          return image







     def  snake_case_			(							self       ,__a       ,__a = "pil"       ,__a = None       ,)			->							str:
          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"]:
               snake_case :			Optional[int]					   = (
                   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       )
               snake_case :			Optional[int]					   = """np"""

          if output_type == "latent":
               return image

          if do_denormalize is None:
               snake_case :			Dict					   = [self.config.do_normalize] * image.shape[0]

          snake_case :			Any					   = 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

          snake_case :			Dict					   = self.pt_to_numpy(__a       )

          if output_type == "np":
               return image

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







class 				_a   (a__  ):





     '''simple docstring'''



     lowerCAmelCase_     :    List[str]									=				CONFIG_NAME







     @register_to_config
     def __init__(							self       ,__a = True       ,__a = 8       ,__a = "lanczos"       ,__a = True       ,)			->							Any:
          super().__init__()







     @staticmethod
     def  snake_case_			(							__a       )			->							int:
          if images.ndim == 3:
               snake_case :			Union[str, Any]					   = images[None, ...]
          snake_case :			List[str]					   = (images * 255).round().astype("""uint8"""       )
          if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               snake_case :			int					   = [Image.fromarray(image.squeeze()       ,mode="""L"""       ) for image in images]
          else:
               snake_case :			Optional[Any]					   = [Image.fromarray(image[:, :, :3]       ) for image in images]

          return pil_images







     @staticmethod
     def  snake_case_			(							__a       )			->							Tuple:
          return image[:, :, 1] * 2**8 + image[:, :, 2]







     def  snake_case_			(							self       ,__a       )			->							Optional[Any]:
          if images.ndim == 3:
               snake_case :			Union[str, Any]					   = images[None, ...]
          snake_case :			Any					   = images[:, :, :, 3:]
          if images.shape[-1] == 6:
               snake_case :			Optional[Any]					   = (images_depth * 255).round().astype("""uint8"""       )
               snake_case :			Tuple					   = [
                   Image.fromarray(self.rgblike_to_depthmap(__a       )       ,mode="""I;16"""       ) for image_depth in images_depth
               ]
          elif images.shape[-1] == 4:
               snake_case :			Tuple					   = (images_depth * 65_535.0).astype(np.uintaa       )
               snake_case :			Dict					   = [Image.fromarray(__a       ,mode="""I;16"""       ) for image_depth in images_depth]
          else:
               raise Exception("""Not supported"""       )

          return pil_images







     def  snake_case_			(							self       ,__a       ,__a = "pil"       ,__a = None       ,)			->							Optional[int]:
          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"]:
               snake_case :			List[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"""       ,__a       ,standard_warn=__a       )
               snake_case :			Union[str, Any]					   = """np"""

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

          snake_case :			List[str]					   = torch.stack(
              [self.denormalize(image[i]       ) if do_denormalize[i] else image[i] for i in range(image.shape[0]       )]       )

          snake_case :			Optional[int]					   = self.pt_to_numpy(__a       )

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









'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

lowercase			:      Union[str, Any]			=					TypeVar("""T""", bound=bool)







class 				_a   (Generic[T]  ):





     '''simple docstring'''



     def __init__(							self       ,__a       )			->							None:
          snake_case :			T					   = value
          snake_case :			SkewNode[T] | None					   = None
          snake_case :			SkewNode[T] | None					   = None







     @property
     def  snake_case_			(							self       )			->							T:
          return self._value







     @staticmethod
     def  snake_case_			(							__a       ,__a       )			->							SkewNode[T] | None:
          if not roota:
               return roota

          if not roota:
               return roota

          if roota.value > roota.value:
               snake_case   ,		snake_case :			Tuple					   = roota, roota

          snake_case :			Tuple					   = roota
          snake_case :			Union[str, Any]					   = roota.right
          snake_case :			Any					   = roota.left
          snake_case :			Union[str, Any]					   = SkewNode.merge(__a       ,__a       )

          return result







class 				_a   (Generic[T]  ):





     '''simple docstring'''



     def __init__(							self       ,__a = ()       )			->							None:
          snake_case :			SkewNode[T] | None					   = None
          if data:
               for item in data:
                    self.insert(__a       )







     def __bool__(							self       )			->							bool:
          return self._root is not None







     def __iter__(							self       )			->							Iterator[T]:
          snake_case :			list[Any]					   = []
          while self:
               result.append(self.pop()       )

          # Pushing items back to the heap not to clear it.
          for item in result:
               self.insert(__a       )

          return iter(__a       )







     def  snake_case_			(							self       ,__a       )			->							None:
          snake_case :			List[Any]					   = SkewNode.merge(self._root       ,SkewNode(__a       )       )







     def  snake_case_			(							self       )			->							T | None:
          snake_case :			Tuple					   = self.top()
          snake_case :			int					   = (
              SkewNode.merge(self._root.left       ,self._root.right       ) if self._root else None
          )

          return result







     def  snake_case_			(							self       )			->							T:
          if not self._root:
               raise IndexError("""Can't get top element for the empty heap."""       )
          return self._root.value







     def  snake_case_			(							self       )			->							None:
          snake_case :			Any					   = None


if __name__ == "__main__":
      import doctest

      doctest.testmod()



