code: 
  
  
  
  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      UpperCAmelCase_						(     UpperCamelCase_ ):
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				List[str]       						=						CONFIG_NAME
  
  
  
  
  
  
  						@register_to_config
  						def __init__(					self   ,							_A = True   ,							_A = 8   ,							_A = "lanczos"   ,							_A = True   ,							_A = False   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													super().__init__()
  
  
  
  
  
  
  						@staticmethod
  						def  _A			(					_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if images.ndim == 3:
  																				__SCREAMING_SNAKE_CASE								=      images[None, ...]
  													__SCREAMING_SNAKE_CASE								=      (images * 255).round().astype('uint8'	)
  													if images.shape[-1] == 1:
  																				# special case for grayscale (single channel) images
  																				__SCREAMING_SNAKE_CASE								=      [Image.fromarray(image.squeeze()   ,							mode='L'	) for image in images]
  													else:
  																				__SCREAMING_SNAKE_CASE								=      [Image.fromarray(_A	) for image in images]
  
  													return pil_images
  
  
  
  
  
  
  						@staticmethod
  						def  _A			(					_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if not isinstance(_A   ,							_A	):
  																				__SCREAMING_SNAKE_CASE								=      [images]
  													__SCREAMING_SNAKE_CASE								=      [np.array(_A	).astype(np.floataa	) / 2_5_5.0 for image in images]
  													__SCREAMING_SNAKE_CASE								=      np.stack(_A   ,							axis=0	)
  
  													return images
  
  
  
  
  
  
  						@staticmethod
  						def  _A			(					_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if images.ndim == 3:
  																				__SCREAMING_SNAKE_CASE								=      images[..., None]
  
  													__SCREAMING_SNAKE_CASE								=      torch.from_numpy(images.transpose(0   ,							3   ,							1   ,							2	)	)
  													return images
  
  
  
  
  
  
  						@staticmethod
  						def  _A			(					_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      images.cpu().permute(0   ,							2   ,							3   ,							1	).float().numpy()
  													return images
  
  
  
  
  
  
  						@staticmethod
  						def  _A			(					_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													return 2.0 * images - 1.0
  
  
  
  
  
  
  						@staticmethod
  						def  _A			(					_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													return (images / 2 + 0.5).clamp(0   ,							1	)
  
  
  
  
  
  
  						@staticmethod
  						def  _A			(					_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      image.convert('RGB'	)
  													return image
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A = None   ,							_A = None   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if height is None:
  																				__SCREAMING_SNAKE_CASE								=      image.height
  													if width is None:
  																				__SCREAMING_SNAKE_CASE								=      image.width
  
  													__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      (
  													    x - x % self.config.vae_scale_factor for x in (width, height)
  													)  # resize to integer multiple of vae_scale_factor
  													__SCREAMING_SNAKE_CASE								=      image.resize((width, height)   ,							resample=PIL_INTERPOLATION[self.config.resample]	)
  													return image
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A = None   ,							_A = None   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      (PIL.Image.Image, np.ndarray, torch.Tensor)
  													if isinstance(_A   ,							_A	):
  																				__SCREAMING_SNAKE_CASE								=      [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:
  																											__SCREAMING_SNAKE_CASE								=      [self.convert_to_rgb(_A	) for i in image]
  																				if self.config.do_resize:
  																											__SCREAMING_SNAKE_CASE								=      [self.resize(_A   ,							_A   ,							_A	) for i in image]
  																				__SCREAMING_SNAKE_CASE								=      self.pil_to_numpy(_A	)  # to np
  																				__SCREAMING_SNAKE_CASE								=      self.numpy_to_pt(_A	)  # to pt
  
  													elif isinstance(image[0]   ,							np.ndarray	):
  																				__SCREAMING_SNAKE_CASE								=      np.concatenate(_A   ,							axis=0	) if image[0].ndim == 4 else np.stack(_A   ,							axis=0	)
  																				__SCREAMING_SNAKE_CASE								=      self.numpy_to_pt(_A	)
  																				__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      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	):
  																				__SCREAMING_SNAKE_CASE								=      torch.cat(_A   ,							axis=0	) if image[0].ndim == 4 else torch.stack(_A   ,							axis=0	)
  																				__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      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]
  													__SCREAMING_SNAKE_CASE								=      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   ,							)
  																				__SCREAMING_SNAKE_CASE								=      False
  
  													if do_normalize:
  																				__SCREAMING_SNAKE_CASE								=      self.normalize(_A	)
  
  													return image
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A = "pil"   ,							_A = None   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													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"]:
  																				__SCREAMING_SNAKE_CASE								=      (
  																				    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	)
  																				__SCREAMING_SNAKE_CASE								=      'np'
  
  													if output_type == "latent":
  																				return image
  
  													if do_denormalize is None:
  																				__SCREAMING_SNAKE_CASE								=      [self.config.do_normalize] * image.shape[0]
  
  													__SCREAMING_SNAKE_CASE								=      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
  
  													__SCREAMING_SNAKE_CASE								=      self.pt_to_numpy(_A	)
  
  													if output_type == "np":
  																				return image
  
  													if output_type == "pil":
  																				return self.numpy_to_pil(_A	)
  
  
  
  
  
  
  
  class      UpperCAmelCase_						(     UpperCamelCase_ ):
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				Optional[int]       						=						CONFIG_NAME
  
  
  
  
  
  
  						@register_to_config
  						def __init__(					self   ,							_A = True   ,							_A = 8   ,							_A = "lanczos"   ,							_A = True   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													super().__init__()
  
  
  
  
  
  
  						@staticmethod
  						def  _A			(					_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if images.ndim == 3:
  																				__SCREAMING_SNAKE_CASE								=      images[None, ...]
  													__SCREAMING_SNAKE_CASE								=      (images * 255).round().astype('uint8'	)
  													if images.shape[-1] == 1:
  																				# special case for grayscale (single channel) images
  																				__SCREAMING_SNAKE_CASE								=      [Image.fromarray(image.squeeze()   ,							mode='L'	) for image in images]
  													else:
  																				__SCREAMING_SNAKE_CASE								=      [Image.fromarray(image[:, :, :3]	) for image in images]
  
  													return pil_images
  
  
  
  
  
  
  						@staticmethod
  						def  _A			(					_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													return image[:, :, 1] * 2**8 + image[:, :, 2]
  
  
  
  
  
  
  						def  _A			(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if images.ndim == 3:
  																				__SCREAMING_SNAKE_CASE								=      images[None, ...]
  													__SCREAMING_SNAKE_CASE								=      images[:, :, :, 3:]
  													if images.shape[-1] == 6:
  																				__SCREAMING_SNAKE_CASE								=      (images_depth * 255).round().astype('uint8'	)
  																				__SCREAMING_SNAKE_CASE								=      [
  																				    Image.fromarray(self.rgblike_to_depthmap(_A	)   ,							mode='I;16'	) for image_depth in images_depth
  																				]
  													elif images.shape[-1] == 4:
  																				__SCREAMING_SNAKE_CASE								=      (images_depth * 6_5_5_3_5.0).astype(np.uintaa	)
  																				__SCREAMING_SNAKE_CASE								=      [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   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													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"]:
  																				__SCREAMING_SNAKE_CASE								=      (
  																				    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	)
  																				__SCREAMING_SNAKE_CASE								=      'np'
  
  													if do_denormalize is None:
  																				__SCREAMING_SNAKE_CASE								=      [self.config.do_normalize] * image.shape[0]
  
  													__SCREAMING_SNAKE_CASE								=      torch.stack(
  													    [self.denormalize(image[i]	) if do_denormalize[i] else image[i] for i in range(image.shape[0]	)]	)
  
  													__SCREAMING_SNAKE_CASE								=      self.pt_to_numpy(_A	)
  
  													if output_type == "np":
  																				if image.shape[-1] == 6:
  																											__SCREAMING_SNAKE_CASE								=      np.stack([self.rgblike_to_depthmap(im[:, :, 3:]	) for im in image]   ,							axis=0	)
  																				else:
  																											__SCREAMING_SNAKE_CASE								=      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"""	)
  
  
  
  
  
  
  
style_context: 
  
  
  
  def 			__lowercase     (				a__     )       ->				list:
  							__SCREAMING_SNAKE_CASE								=      1.3
  							__SCREAMING_SNAKE_CASE								=      len(a__     )
  							__SCREAMING_SNAKE_CASE								=      False
  
  							while not completed:
  														# Update the gap value for a next comb
  														__SCREAMING_SNAKE_CASE								=      int(gap / shrink_factor     )
  														if gap <= 1:
  																					__SCREAMING_SNAKE_CASE								=      True
  
  														__SCREAMING_SNAKE_CASE								=      0
  														while index + gap < len(a__     ):
  																					if data[index] > data[index + gap]:
  																												# Swap values
  																												__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      data[index + gap], data[index]
  																												__SCREAMING_SNAKE_CASE								=      False
  																					index += 1
  
  							return data
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  			lowerCAmelCase__	:  int 		=input('''Enter numbers separated by a comma:\n''').strip()
  			lowerCAmelCase__	:  Dict 		=[int(item) for item in user_input.split(''',''')]
  			print(comb_sort(unsorted))
  
  
  
  
  
  
  