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_   :      Dict       =	CONFIG_NAME

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

	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase   ):
			if images.ndim == 3:
					__A							:   Optional[int]	  =				images[None, ...]
			__A							:   Optional[Any]	  =				(images * 255).round().astype("uint8"   )
			if images.shape[-1] == 1:
					# special case for grayscale (single channel) images
					__A							:   Dict	  =				[Image.fromarray(image.squeeze()							,			mode="L"   ) for image in images]
			else:
					__A							:   Any	  =				[Image.fromarray(SCREAMING_SNAKE_CASE__   ) for image in images]

			return pil_images

	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase   ):
			if not isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__   ):
					__A							:   Any	  =				[images]
			__A							:   str	  =				[np.array(SCREAMING_SNAKE_CASE__   ).astype(np.floataa   ) / 2_55.0 for image in images]
			__A							:   List[Any]	  =				np.stack(SCREAMING_SNAKE_CASE__							,			axis=0   )

			return images

	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase   ):
			if images.ndim == 3:
					__A							:   Optional[int]	  =				images[..., None]

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

	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase   ):
			__A							:   int	  =				images.cpu().permute(0							,			2							,			3							,			1   ).float().numpy()
			return images

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

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

	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase   ):
			__A							:   Optional[Any]	  =				image.convert("RGB"   )
			return image

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase = None							,			__UpperCAmelCase = None							,			):
			if height is None:
					__A							:   Any	  =				image.height
			if width is None:
					__A							:   Optional[Any]	  =				image.width

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

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase = None							,			__UpperCAmelCase = None							,			):
			__A							:   Optional[Any]	  =				(PIL.Image.Image, np.ndarray, torch.Tensor)
			if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__   ):
					__A							:   Any	  =				[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							:   str	  =				[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							:   Any	  =				np.concatenate(SCREAMING_SNAKE_CASE__							,			axis=0   ) if image[0].ndim == 4 else np.stack(SCREAMING_SNAKE_CASE__							,			axis=0   )
					__A							:   Optional[Any]	  =				self.numpy_to_pt(SCREAMING_SNAKE_CASE__   )
					__A							:   Optional[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							:   Optional[Any]	  =				torch.cat(SCREAMING_SNAKE_CASE__							,			axis=0   ) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE__							,			axis=0   )
					__A							:   str	  =				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							:   int	  =				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							:   Union[str, Any]	  =				False

			if do_normalize:
					__A							:   List[str]	  =				self.normalize(SCREAMING_SNAKE_CASE__   )

			return image







	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase = "pil"							,			__UpperCAmelCase = 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							:   Union[str, Any]	  =				[self.config.do_normalize] * image.shape[0]

			__A							:   Union[str, 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

			__A							:   Tuple	  =				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 _a       (	_a ):

	'''simple docstring'''



	lowerCamelCase_   :      Any       =	CONFIG_NAME

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

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

			return pil_images

	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase   ):
			return image[:, :, 1] * 2**8 + image[:, :, 2]

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			if images.ndim == 3:
					__A							:   Union[str, Any]	  =				images[None, ...]
			__A							:   Optional[Any]	  =				images[:, :, :, 3:]
			if images.shape[-1] == 6:
					__A							:   Optional[int]	  =				(images_depth * 255).round().astype("uint8"   )
					__A							:   Tuple	  =				[
					    Image.fromarray(self.rgblike_to_depthmap(SCREAMING_SNAKE_CASE__   )							,			mode="I;16"   ) for image_depth in images_depth
					]
			elif images.shape[-1] == 4:
					__A							:   List[str]	  =				(images_depth * 65_535.0).astype(np.uintaa   )
					__A							:   Dict	  =				[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							,			__UpperCAmelCase							,			__UpperCAmelCase = "pil"							,			__UpperCAmelCase = 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							:   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"							,			SCREAMING_SNAKE_CASE__							,			standard_warn=SCREAMING_SNAKE_CASE__   )
					__A							:   List[str]	  =				"np"

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

			__A							:   Optional[Any]	  =				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							:   Tuple	  =				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"   )






def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase    )							->	Tuple:
		__A							:   Union[str, Any]	  =				index
		__A							:   int	  =				2 * index + 1
		__A							:   Dict	  =				2 * index + 2
		if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
				__A							:   Dict	  =				left_index

		if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
				__A							:   int	  =				right_index

		if largest != index:
				__A  ,       __A							:   Dict	  =				unsorted[index], unsorted[largest]
				heapify(_lowercase ,     _lowercase ,     _lowercase    )



def    lowerCamelCase_   (  _lowercase    )							->	Tuple:
		__A							:   Dict	  =				len(_lowercase    )
		for i in range(n // 2 - 1 ,     -1 ,     -1    ):
				heapify(_lowercase ,     _lowercase ,     _lowercase    )
		for i in range(n - 1 ,     0 ,     -1    ):
				__A  ,       __A							:   Dict	  =				unsorted[i], unsorted[0]
				heapify(_lowercase ,     0 ,     _lowercase    )
		return unsorted


if __name__ == "__main__":
					UpperCamelCase      			=     input('Enter numbers separated by a comma:\n').strip()
					UpperCamelCase      			=     [int(item) for item in user_input.split(',')]
					print(heap_sort(unsorted))





