code: 
  
  
  
  
  from collections.abc import Callable
  from math import log
  from os import name, system
  from random import gauss, seed
  from typing import TypeVar
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       list:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			seed(1   )
  			return [gauss(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) for _ in range(SCREAMING_SNAKE_CASE   )]
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       list:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			return [k for k in range(SCREAMING_SNAKE_CASE   ) for _ in range(instance_count[k]   )]
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       float:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			return sum(SCREAMING_SNAKE_CASE   ) / instance_count
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       float:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			return instance_count / total_count
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       float:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	[]  # An empty list to store all squared differences
  			# iterate over number of elements in items
  			for i in range(len(SCREAMING_SNAKE_CASE   )   ):
  						# for loop iterates over number of elements in inner layer of items
  						for j in range(len(items[i]   )   ):
  									# appending squared differences to 'squared_diff' list
  									squared_diff.append((items[i][j] - means[i]) ** 2   )
  
      # one divided by (the number of all instances - number of classes) multiplied by
      # sum of all squared differences
  			SCREAMING_SNAKE_CASE_			=	len(SCREAMING_SNAKE_CASE   )  # Number of classes in dataset
  			return 1 / (total_count - n_classes) * sum(SCREAMING_SNAKE_CASE   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       list:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	[]
  			# for loop iterates over number of elements in list
  			for i in range(len(SCREAMING_SNAKE_CASE   )   ):
  						# for loop iterates over number of inner items of each element
  						for j in range(len(x_items[i]   )   ):
  									SCREAMING_SNAKE_CASE_			=	[]  # to store all discriminant values of each item as a list
  									# for loop iterates over number of classes we have in our dataset
  									for k in range(len(SCREAMING_SNAKE_CASE   )   ):
  												# appending values of discriminants for each class to 'temp' list
  												temp.append(
  												    x_items[i][j] * (means[k] / variance)
  												    - (means[k] ** 2 / (2 * variance))
  												    + log(probabilities[k]   )   )
  									# appending discriminant values of each item to 'results' list
  									results.append(SCREAMING_SNAKE_CASE   )
  
  			return [result.index(max(SCREAMING_SNAKE_CASE   )   ) for result in results]
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       float:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	sum(1 for i, j in zip(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) if i == j   )
  			# percentage of accuracy equals to number of correct predictions divided by number
  			# of all data and multiplied by 100
  			return (correct / len(SCREAMING_SNAKE_CASE   )) * 1_00
  
  
  SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					TypeVar("num")
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE = lambda SCREAMING_SNAKE_CASE   : True					,						SCREAMING_SNAKE_CASE = None					,						) ->       num:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			while True:
  						try:
  									SCREAMING_SNAKE_CASE_			=	input_type(input(SCREAMING_SNAKE_CASE   ).strip() or default   )
  									if condition(SCREAMING_SNAKE_CASE   ):
  												return user_input
  									else:
  												print(F'{user_input}: {err_msg}'   )
  												continue
  						except ValueError:
  									print(
  									    F'{user_input}: Incorrect input type, expected {input_type.__name__!r}'   )
  
  def lowercase						(     ) ->       List[str]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			while True:
  						print(' Linear Discriminant Analysis '.center(50					,						'*'   )   )
  						print('*' * 50					,						'\n'   )
  						print('First of all we should specify the number of classes that'   )
  						print('we want to generate as training dataset'   )
  						# Trying to get number of classes
  						SCREAMING_SNAKE_CASE_			=	valid_input(
  						    input_type=SCREAMING_SNAKE_CASE					,						condition=lambda SCREAMING_SNAKE_CASE   : x > 0					,						input_msg='Enter the number of classes (Data Groupings): '					,						err_msg='Number of classes should be positive!'					,						)
  
  						print('-' * 1_00   )
  
  						# Trying to get the value of standard deviation
  						SCREAMING_SNAKE_CASE_			=	valid_input(
  						    input_type=SCREAMING_SNAKE_CASE					,						condition=lambda SCREAMING_SNAKE_CASE   : x >= 0					,						input_msg=(
  						        'Enter the value of standard deviation'
  						        '(Default value is 1.0 for all classes): '
  						    )					,						err_msg='Standard deviation should not be negative!'					,						default='1.0'					,						)
  
  						print('-' * 1_00   )
  
  						# Trying to get number of instances in classes and theirs means to generate
  						# dataset
  						SCREAMING_SNAKE_CASE_			=	[]  # An empty list to store instance counts of classes in dataset
  						for i in range(SCREAMING_SNAKE_CASE   ):
  									SCREAMING_SNAKE_CASE_			=	valid_input(
  									    input_type=SCREAMING_SNAKE_CASE					,						condition=lambda SCREAMING_SNAKE_CASE   : x > 0					,						input_msg=(F'Enter The number of instances for class_{i+1}: ')					,						err_msg='Number of instances should be positive!'					,						)
  									counts.append(SCREAMING_SNAKE_CASE   )
  						print('-' * 1_00   )
  
  						# An empty list to store values of user-entered means of classes
  						SCREAMING_SNAKE_CASE_			=	[]
  						for a in range(SCREAMING_SNAKE_CASE   ):
  									SCREAMING_SNAKE_CASE_			=	valid_input(
  									    input_type=SCREAMING_SNAKE_CASE					,						input_msg=(F'Enter the value of mean for class_{a+1}: ')					,						err_msg='This is an invalid value.'					,						)
  									user_means.append(SCREAMING_SNAKE_CASE   )
  						print('-' * 1_00   )
  
  						print('Standard deviation: '					,						SCREAMING_SNAKE_CASE   )
  						# print out the number of instances in classes in separated line
  						for i, count in enumerate(SCREAMING_SNAKE_CASE					,						1   ):
  									print(F'Number of instances in class_{i} is: {count}'   )
  						print('-' * 1_00   )
  
  						# print out mean values of classes separated line
  						for i, user_mean in enumerate(SCREAMING_SNAKE_CASE					,						1   ):
  									print(F'Mean of class_{i} is: {user_mean}'   )
  						print('-' * 1_00   )
  
  						# Generating training dataset drawn from gaussian distribution
  						SCREAMING_SNAKE_CASE_			=	[
  						    gaussian_distribution(user_means[j]					,						SCREAMING_SNAKE_CASE					,						counts[j]   )
  						    for j in range(SCREAMING_SNAKE_CASE   )
  						]
  						print('Generated Normal Distribution: \n'					,						SCREAMING_SNAKE_CASE   )
  						print('-' * 1_00   )
  
  						# Generating Ys to detecting corresponding classes
  						SCREAMING_SNAKE_CASE_			=	y_generator(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  						print('Generated Corresponding Ys: \n'					,						SCREAMING_SNAKE_CASE   )
  						print('-' * 1_00   )
  
  						# Calculating the value of actual mean for each class
  						SCREAMING_SNAKE_CASE_			=	[calculate_mean(counts[k]					,						x[k]   ) for k in range(SCREAMING_SNAKE_CASE   )]
  						# for loop iterates over number of elements in 'actual_means' list and print
  						# out them in separated line
  						for i, actual_mean in enumerate(SCREAMING_SNAKE_CASE					,						1   ):
  									print(F'Actual(Real) mean of class_{i} is: {actual_mean}'   )
  						print('-' * 1_00   )
  
  						# Calculating the value of probabilities for each class
  						SCREAMING_SNAKE_CASE_			=	[
  						    calculate_probabilities(counts[i]					,						sum(SCREAMING_SNAKE_CASE   )   ) for i in range(SCREAMING_SNAKE_CASE   )
  						]
  
  						# for loop iterates over number of elements in 'probabilities' list and print
  						# out them in separated line
  						for i, probability in enumerate(SCREAMING_SNAKE_CASE					,						1   ):
  									print(F'Probability of class_{i} is: {probability}'   )
  						print('-' * 1_00   )
  
  						# Calculating the values of variance for each class
  						SCREAMING_SNAKE_CASE_			=	calculate_variance(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						sum(SCREAMING_SNAKE_CASE   )   )
  						print('Variance: '					,						SCREAMING_SNAKE_CASE   )
  						print('-' * 1_00   )
  
  						# Predicting Y values
  						# storing predicted Y values in 'pre_indexes' variable
  						SCREAMING_SNAKE_CASE_			=	predict_y_values(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  						print('-' * 1_00   )
  
  						# Calculating Accuracy of the model
  						print(F'Accuracy: {accuracy(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )}'   )
  						print('-' * 1_00   )
  						print(' DONE '.center(1_00					,						'+'   )   )
  
  						if input('Press any key to restart or \'q\' for quit: '   ).strip().lower() == "q":
  									print('\n' + 'GoodBye!'.center(1_00					,						'-'   ) + '\n'   )
  									break
  						system('cls' if name == 'nt' else 'clear'   )  # noqa: S605
  
  
  if __name__ == "__main__":
  				main()
  
style_context: 
  
  
  
  
  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_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   CONFIG_NAME
  
  
  
  
  
  	@register_to_config
  	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 8			,    SCREAMING_SNAKE_CASE = "lanczos"			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = False			,    )     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__()
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				PIL.Image.Image:
  
  
  
  
  				"""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(SCREAMING_SNAKE_CASE			) for image in images]
  
  				return pil_images
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if not isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	[images]
  				SCREAMING_SNAKE_CASE_			=	[np.array(SCREAMING_SNAKE_CASE			).astype(np.floataa			) / 2_5_5.0 for image in images]
  				SCREAMING_SNAKE_CASE_			=	np.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
  
  				return images
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				torch.FloatTensor:
  
  
  
  
  				"""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_(       SCREAMING_SNAKE_CASE			)     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	images.cpu().permute(0			,    2			,    3			,    1			).float().numpy()
  				return images
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return 2.0 * images - 1.0
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return (images / 2 + 0.5).clamp(0			,    1			)
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				PIL.Image.Image:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	image.convert('RGB'			)
  				return image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				PIL.Image.Image:
  
  
  
  
  				"""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			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				torch.Tensor:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	(PIL.Image.Image, np.ndarray, torch.Tensor)
  				if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	[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:
  										SCREAMING_SNAKE_CASE_			=	[self.convert_to_rgb(SCREAMING_SNAKE_CASE			) for i in image]
  							if self.config.do_resize:
  										SCREAMING_SNAKE_CASE_			=	[self.resize(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for i in image]
  							SCREAMING_SNAKE_CASE_			=	self.pil_to_numpy(SCREAMING_SNAKE_CASE			)  # to np
  							SCREAMING_SNAKE_CASE_			=	self.numpy_to_pt(SCREAMING_SNAKE_CASE			)  # to pt
  
  				elif isinstance(image[0]			,    np.ndarray			):
  							SCREAMING_SNAKE_CASE_			=	np.concatenate(SCREAMING_SNAKE_CASE			,    axis=0			) if image[0].ndim == 4 else np.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
  							SCREAMING_SNAKE_CASE_			=	self.numpy_to_pt(SCREAMING_SNAKE_CASE			)
  							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(SCREAMING_SNAKE_CASE			,    axis=0			) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE			,    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()}]'			,    SCREAMING_SNAKE_CASE			,    )
  							SCREAMING_SNAKE_CASE_			=	False
  
  				if do_normalize:
  							SCREAMING_SNAKE_CASE_			=	self.normalize(SCREAMING_SNAKE_CASE			)
  
  				return image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = "pil"			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				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"]:
  							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'			,    SCREAMING_SNAKE_CASE			,    standard_warn=SCREAMING_SNAKE_CASE			)
  							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(SCREAMING_SNAKE_CASE			)
  
  				if output_type == "np":
  							return image
  
  				if output_type == "pil":
  							return self.numpy_to_pil(SCREAMING_SNAKE_CASE			)
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   CONFIG_NAME
  
  
  
  
  
  	@register_to_config
  	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 8			,    SCREAMING_SNAKE_CASE = "lanczos"			,    SCREAMING_SNAKE_CASE = True			,    )     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__()
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Tuple:
  
  
  
  
  				"""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_(       SCREAMING_SNAKE_CASE			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return image[:, :, 1] * 2**8 + image[:, :, 2]
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Optional[int]:
  
  
  
  
  				"""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(SCREAMING_SNAKE_CASE			)			,    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(SCREAMING_SNAKE_CASE			,    mode='I;16'			) for image_depth in images_depth]
  				else:
  							raise Exception('Not supported'			)
  
  				return pil_images
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = "pil"			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				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"]:
  							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'			,    SCREAMING_SNAKE_CASE			,    standard_warn=SCREAMING_SNAKE_CASE			)
  							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(SCREAMING_SNAKE_CASE			)
  
  				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(SCREAMING_SNAKE_CASE			), self.numpy_to_depth(SCREAMING_SNAKE_CASE			)
  				else:
  							raise Exception(f'This type {output_type} is not supported'			)
  