


'''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 				__lowerCamelCase				( a_ ):
    """simple docstring"""
    a									=					CONFIG_NAME


    @register_to_config
    def __init__(						self    :							int   ,							SCREAMING_SNAKE_CASE    :							bool = True   ,							SCREAMING_SNAKE_CASE    :							int = 8   ,							SCREAMING_SNAKE_CASE    :							str = "lanczos"   ,							SCREAMING_SNAKE_CASE    :							bool = True   ,							SCREAMING_SNAKE_CASE    :							bool = False   ,							):
           super().__init__()


    @staticmethod
    def      A					(						SCREAMING_SNAKE_CASE    :							np.ndarray):
           if images.ndim == 3:
                  _A  :			str											=      images[None, ...]
           _A  :			Union[str, Any]											=      (images * 255).round().astype('uint8')
           if images.shape[-1] == 1:
                  # special case for grayscale (single channel) images
                  _A  :			List[str]											=      [Image.fromarray(image.squeeze()   ,							mode='L') for image in images]
           else:
                  _A  :			Union[str, Any]											=      [Image.fromarray(SCREAMING_SNAKE_CASE) for image in images]

           return pil_images


    @staticmethod
    def      A					(						SCREAMING_SNAKE_CASE    :							Union[List[PIL.Image.Image], PIL.Image.Image]):
           if not isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE):
                  _A  :			List[str]											=      [images]
           _A  :			int											=      [np.array(SCREAMING_SNAKE_CASE).astype(np.floataa) / 255.0 for image in images]
           _A  :			List[str]											=      np.stack(SCREAMING_SNAKE_CASE   ,							axis=0)

           return images


    @staticmethod
    def      A					(						SCREAMING_SNAKE_CASE    :							np.ndarray):
           if images.ndim == 3:
                  _A  :			List[Any]											=      images[..., None]

           _A  :			List[Any]											=      torch.from_numpy(images.transpose(0   ,							3   ,							1   ,							2))
           return images


    @staticmethod
    def      A					(						SCREAMING_SNAKE_CASE    :							torch.FloatTensor):
           _A  :			Dict											=      images.cpu().permute(0   ,							2   ,							3   ,							1).float().numpy()
           return images


    @staticmethod
    def      A					(						SCREAMING_SNAKE_CASE    :							List[str]):
           return 2.0 * images - 1.0


    @staticmethod
    def      A					(						SCREAMING_SNAKE_CASE    :							int):
           return (images / 2 + 0.5).clamp(0   ,							1)


    @staticmethod
    def      A					(						SCREAMING_SNAKE_CASE    :							PIL.Image.Image):
           _A  :			str											=      image.convert('RGB')
           return image


    def      A					(						self    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							PIL.Image.Image   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							):
           if height is None:
                  _A  :			int											=      image.height
           if width is None:
                  _A  :			List[Any]											=      image.width

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


    def      A					(						self    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							):
           _A  :			List[str]											=      (PIL.Image.Image, np.ndarray, torch.Tensor)
           if isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE):
                  _A  :			Dict											=      [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:
                         _A  :			Optional[int]											=      [self.convert_to_rgb(SCREAMING_SNAKE_CASE) for i in image]
                  if self.config.do_resize:
                         _A  :			Union[str, Any]											=      [self.resize(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE) for i in image]
                  _A  :			List[Any]											=      self.pil_to_numpy(SCREAMING_SNAKE_CASE)  # to np
                  _A  :			Optional[Any]											=      self.numpy_to_pt(SCREAMING_SNAKE_CASE)  # to pt

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

           if do_normalize:
                  _A  :			Optional[Any]											=      self.normalize(SCREAMING_SNAKE_CASE)

           return image


    def      A					(						self    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							torch.FloatTensor   ,							SCREAMING_SNAKE_CASE    :							str = "pil"   ,							SCREAMING_SNAKE_CASE    :							Optional[List[bool]] = None   ,							):
           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"]:
                  _A  :			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'   ,							SCREAMING_SNAKE_CASE   ,							standard_warn=SCREAMING_SNAKE_CASE)
                  _A  :			List[str]											=      'np'

           if output_type == "latent":
                  return image

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

           _A  :			List[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

           _A  :			Any											=      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 				__lowerCamelCase				( a_ ):
    """simple docstring"""
    a									=					CONFIG_NAME


    @register_to_config
    def __init__(						self    :							str   ,							SCREAMING_SNAKE_CASE    :							bool = True   ,							SCREAMING_SNAKE_CASE    :							int = 8   ,							SCREAMING_SNAKE_CASE    :							str = "lanczos"   ,							SCREAMING_SNAKE_CASE    :							bool = True   ,							):
           super().__init__()


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

           return pil_images


    @staticmethod
    def      A					(						SCREAMING_SNAKE_CASE    :							Optional[Any]):
           return image[:, :, 1] * 2**8 + image[:, :, 2]


    def      A					(						self    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							List[str]):
           if images.ndim == 3:
                  _A  :			List[str]											=      images[None, ...]
           _A  :			Dict											=      images[:, :, :, 3:]
           if images.shape[-1] == 6:
                  _A  :			str											=      (images_depth * 255).round().astype('uint8')
                  _A  :			Union[str, Any]											=      [
                      Image.fromarray(self.rgblike_to_depthmap(SCREAMING_SNAKE_CASE)   ,							mode='I;16') for image_depth in images_depth
                  ]
           elif images.shape[-1] == 4:
                  _A  :			Any											=      (images_depth * 65535.0).astype(np.uintaa)
                  _A  :			Union[str, Any]											=      [Image.fromarray(SCREAMING_SNAKE_CASE   ,							mode='I;16') for image_depth in images_depth]
           else:
                  raise Exception('Not supported')

           return pil_images


    def      A					(						self    :							Any   ,							SCREAMING_SNAKE_CASE    :							torch.FloatTensor   ,							SCREAMING_SNAKE_CASE    :							str = "pil"   ,							SCREAMING_SNAKE_CASE    :							Optional[List[bool]] = None   ,							):
           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"]:
                  _A  :			Tuple											=      (
                      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)
                  _A  :			Tuple											=      'np'

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

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

           _A  :			Union[str, Any]											=      self.pt_to_numpy(SCREAMING_SNAKE_CASE)

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







class 				__lowerCamelCase				:
    """simple docstring"""


    def __init__(						self    :							Optional[Any]):
           _A  :			Optional[Any]											=      [0]
           _A  :			Optional[Any]											=      0


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int):
           _A  :			Optional[Any]											=      self.__heap[i]
           while i // 2 > 0:
                  if self.__heap[i] > self.__heap[i // 2]:
                         _A  :			Dict											=      self.__heap[i // 2]
                         _A  :			Optional[Any]											=      temporary
                  i //= 2


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int):
           self.__heap.append(SCREAMING_SNAKE_CASE)
           self.__size += 1
           self.__swap_up(self.__size)


    def      A					(						self    :							int   ,							SCREAMING_SNAKE_CASE    :							int):
           while self.__size >= 2 * i:
                  if 2 * i + 1 > self.__size:
                         _A  :			Optional[int]											=      2 * i
                  else:
                         if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                                _A  :			Any											=      2 * i
                         else:
                                _A  :			Optional[Any]											=      2 * i + 1
                  _A  :			List[str]											=      self.__heap[i]
                  if self.__heap[i] < self.__heap[bigger_child]:
                         _A  :			Union[str, Any]											=      self.__heap[bigger_child]
                         _A  :			Optional[int]											=      temporary
                  _A  :			Any											=      bigger_child


    def      A					(						self    :							List[str]):
           _A  :			str											=      self.__heap[1]
           _A  :			Tuple											=      self.__heap[self.__size]
           self.__size -= 1
           self.__heap.pop()
           self.__swap_down(1)
           return max_value


    @property
    def      A					(						self    :							int):
           return self.__heap[1:]


    def __len__(						self    :							List[str]):
           return self.__size


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   # create an instance of BinaryHeap
   A   :  Dict       		=				BinaryHeap()
   binary_heap.insert(6)
   binary_heap.insert(10)
   binary_heap.insert(15)
   binary_heap.insert(12)
   # pop root(max-values because it is max heap)
   print(binary_heap.pop())  # 15
   print(binary_heap.pop())  # 12
   # get the list and size after operations
   print(binary_heap.get_list)
   print(len(binary_heap))






