code: 
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
  	import PIL
  
  
  A_    :					List[Any]             =logging.get_logger(__name__)
  
  
  
  
  
  class        lowercase_				( lowerCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		['''pixel_values''']
  
  
  							def __init__(   self			,    _UpperCAmelCase = True			,    _UpperCAmelCase = None			,    _UpperCAmelCase = PIL.Image.NEAREST			,    _UpperCAmelCase = False			,    _UpperCAmelCase = None			,    _UpperCAmelCase = 1 / 255			,    _UpperCAmelCase = False			,    _UpperCAmelCase = True			,    _UpperCAmelCase = True			,    _UpperCAmelCase = None			,    _UpperCAmelCase = None			,    _UpperCAmelCase = True			,    **_UpperCAmelCase			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().__init__(**SCREAMING_SNAKE_CASE_		)
  													a_					=		size if size is not None else {"""height""": 346, """width""": 346}
  													a_					=		get_size_dict(SCREAMING_SNAKE_CASE_		)
  													a_					=		crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
  													a_					=		get_size_dict(SCREAMING_SNAKE_CASE_			,    param_name="""crop_size"""		)
  
  													a_					=		do_resize
  													a_					=		size
  													a_					=		resample
  													a_					=		do_center_crop
  													a_					=		crop_size
  													a_					=		do_rescale
  													a_					=		rescale_factor
  													a_					=		rescale_offset
  													a_					=		do_normalize
  													a_					=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  													a_					=		image_std if image_std is not None else IMAGENET_STANDARD_STD
  													a_					=		include_top
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase = PIL.Image.NEAREST			,    _UpperCAmelCase = None			,    **_UpperCAmelCase			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		get_size_dict(SCREAMING_SNAKE_CASE_		)
  													if "height" not in size or "width" not in size:
  																			raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"		)
  													return resize(
  													    SCREAMING_SNAKE_CASE_			,    size=(size["""height"""], size["""width"""])			,    resample=SCREAMING_SNAKE_CASE_			,    data_format=SCREAMING_SNAKE_CASE_			,    **SCREAMING_SNAKE_CASE_		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase = None			,    **_UpperCAmelCase			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		get_size_dict(SCREAMING_SNAKE_CASE_		)
  													if "height" not in size or "width" not in size:
  																			raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"		)
  													return center_crop(SCREAMING_SNAKE_CASE_			,    size=(size["""height"""], size["""width"""])			,    data_format=SCREAMING_SNAKE_CASE_			,    **SCREAMING_SNAKE_CASE_		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase = True			,    _UpperCAmelCase = None			,    **_UpperCAmelCase			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if offset:
  																			a_					=		(image - 1_2_7.5) * scale
  																			if data_format is not None:
  																									a_					=		to_channel_dimension_format(SCREAMING_SNAKE_CASE_			,    SCREAMING_SNAKE_CASE_		)
  																			a_					=		rescaled_image.astype(np.floataa		)
  													else:
  																			a_					=		rescale(SCREAMING_SNAKE_CASE_			,    scale=SCREAMING_SNAKE_CASE_			,    data_format=SCREAMING_SNAKE_CASE_			,    **SCREAMING_SNAKE_CASE_		)
  													return rescaled_image
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase = None			,    **_UpperCAmelCase			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return normalize(SCREAMING_SNAKE_CASE_			,    mean=SCREAMING_SNAKE_CASE_			,    std=SCREAMING_SNAKE_CASE_			,    data_format=SCREAMING_SNAKE_CASE_			,    **SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase = None			,    _UpperCAmelCase = None			,    _UpperCAmelCase=None			,    _UpperCAmelCase = None			,    _UpperCAmelCase = None			,    _UpperCAmelCase = None			,    _UpperCAmelCase = None			,    _UpperCAmelCase = None			,    _UpperCAmelCase = None			,    _UpperCAmelCase = None			,    _UpperCAmelCase = None			,    _UpperCAmelCase = None			,    _UpperCAmelCase = None			,    _UpperCAmelCase = ChannelDimension.FIRST			,    **_UpperCAmelCase			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		do_resize if do_resize is not None else self.do_resize
  													a_					=		resample if resample is not None else self.resample
  													a_					=		do_center_crop if do_center_crop is not None else self.do_center_crop
  													a_					=		do_rescale if do_rescale is not None else self.do_rescale
  													a_					=		rescale_factor if rescale_factor is not None else self.rescale_factor
  													a_					=		rescale_offset if rescale_offset is not None else self.rescale_offset
  													a_					=		do_normalize if do_normalize is not None else self.do_normalize
  													a_					=		image_mean if image_mean is not None else self.image_mean
  													a_					=		image_std if image_std is not None else self.image_std
  													a_					=		include_top if include_top is not None else self.include_top
  
  													a_					=		size if size is not None else self.size
  													a_					=		get_size_dict(SCREAMING_SNAKE_CASE_		)
  													a_					=		crop_size if crop_size is not None else self.crop_size
  													a_					=		get_size_dict(SCREAMING_SNAKE_CASE_			,    param_name="""crop_size"""		)
  
  													a_					=		make_list_of_images(SCREAMING_SNAKE_CASE_		)
  
  													if not valid_images(SCREAMING_SNAKE_CASE_		):
  																			raise ValueError(
  																			    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
  																			    """torch.Tensor, tf.Tensor or jax.ndarray."""		)
  
  													if do_resize and size is None or resample is None:
  																			raise ValueError("""Size and resample must be specified if do_resize is True."""		)
  
  													if do_center_crop and crop_size is None:
  																			raise ValueError("""Crop size must be specified if do_center_crop is True."""		)
  
  													if do_rescale and rescale_factor is None:
  																			raise ValueError("""Rescale factor must be specified if do_rescale is True."""		)
  
  													if do_normalize and (image_mean is None or image_std is None):
  																			raise ValueError("""Image mean and std must be specified if do_normalize is True."""		)
  
  													# All transformations expect numpy arrays.
  													a_					=		[to_numpy_array(SCREAMING_SNAKE_CASE_		) for image in images]
  
  													if do_resize:
  																			a_					=		[self.resize(image=SCREAMING_SNAKE_CASE_			,    size=SCREAMING_SNAKE_CASE_			,    resample=SCREAMING_SNAKE_CASE_		) for image in images]
  
  													if do_center_crop:
  																			a_					=		[self.center_crop(image=SCREAMING_SNAKE_CASE_			,    size=SCREAMING_SNAKE_CASE_		) for image in images]
  
  													if do_rescale:
  																			a_					=		[self.rescale(image=SCREAMING_SNAKE_CASE_			,    scale=SCREAMING_SNAKE_CASE_			,    offset=SCREAMING_SNAKE_CASE_		) for image in images]
  
  													if do_normalize:
  																			a_					=		[self.normalize(image=SCREAMING_SNAKE_CASE_			,    mean=SCREAMING_SNAKE_CASE_			,    std=SCREAMING_SNAKE_CASE_		) for image in images]
  
  													if include_top:
  																			a_					=		[self.normalize(image=SCREAMING_SNAKE_CASE_			,    mean=[0, 0, 0]			,    std=SCREAMING_SNAKE_CASE_		) for image in images]
  
  													a_					=		[to_channel_dimension_format(SCREAMING_SNAKE_CASE_			,    SCREAMING_SNAKE_CASE_		) for image in images]
  
  													a_					=		{"""pixel_values""": images}
  													return BatchFeature(data=SCREAMING_SNAKE_CASE_			,    tensor_type=SCREAMING_SNAKE_CASE_		)
style_context: 
  
  
  from collections.abc import Callable
  from math import log
  from os import name, system
  from random import gauss, seed
  from typing import TypeVar
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						seed(1      )
  						return [gauss(UpperCAmelCase__       ,    UpperCAmelCase__      ) for _ in range(UpperCAmelCase__      )]
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						return [k for k in range(UpperCAmelCase__      ) for _ in range(instance_count[k]      )]
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						return sum(UpperCAmelCase__      ) / instance_count
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						return instance_count / total_count
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		[]  # An empty list to store all squared differences
  						# iterate over number of elements in items
  						for i in range(len(UpperCAmelCase__      )      ):
  												# 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
  						a_					=		len(UpperCAmelCase__      )  # Number of classes in dataset
  						return 1 / (total_count - n_classes) * sum(UpperCAmelCase__      )
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		[]
  						# for loop iterates over number of elements in list
  						for i in range(len(UpperCAmelCase__      )      ):
  												# for loop iterates over number of inner items of each element
  												for j in range(len(x_items[i]      )      ):
  																		a_					=		[]  # 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(UpperCAmelCase__      )      ):
  																								# 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(UpperCAmelCase__      )
  
  						return [result.index(max(UpperCAmelCase__      )      ) for result in results]
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		sum(1 for i, j in zip(UpperCAmelCase__       ,    UpperCAmelCase__      ) 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(UpperCAmelCase__      )) * 100
  
  
  A_    :					Dict             =TypeVar("""num""")
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__ = lambda UpperCAmelCase__      : True       ,    UpperCAmelCase__ = None       ,    ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						while True:
  												try:
  																		a_					=		input_type(input(UpperCAmelCase__      ).strip() or default      )
  																		if condition(UpperCAmelCase__      ):
  																								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   lowerCamelCase_       (					):
  
  
  
  						"""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
  												a_					=		valid_input(
  												    input_type=UpperCAmelCase__       ,    condition=lambda UpperCAmelCase__      : x > 0       ,    input_msg="""Enter the number of classes (Data Groupings): """       ,    err_msg="""Number of classes should be positive!"""       ,    )
  
  												print("""-""" * 100      )
  
  												# Trying to get the value of standard deviation
  												a_					=		valid_input(
  												    input_type=UpperCAmelCase__       ,    condition=lambda UpperCAmelCase__      : 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("""-""" * 100      )
  
  												# Trying to get number of instances in classes and theirs means to generate
  												# dataset
  												a_					=		[]  # An empty list to store instance counts of classes in dataset
  												for i in range(UpperCAmelCase__      ):
  																		a_					=		valid_input(
  																		    input_type=UpperCAmelCase__       ,    condition=lambda UpperCAmelCase__      : x > 0       ,    input_msg=(F"Enter The number of instances for class_{i+1}: ")       ,    err_msg="""Number of instances should be positive!"""       ,    )
  																		counts.append(UpperCAmelCase__      )
  												print("""-""" * 100      )
  
  												# An empty list to store values of user-entered means of classes
  												a_					=		[]
  												for a in range(UpperCAmelCase__      ):
  																		a_					=		valid_input(
  																		    input_type=UpperCAmelCase__       ,    input_msg=(F"Enter the value of mean for class_{a+1}: ")       ,    err_msg="""This is an invalid value."""       ,    )
  																		user_means.append(UpperCAmelCase__      )
  												print("""-""" * 100      )
  
  												print("""Standard deviation: """       ,    UpperCAmelCase__      )
  												# print out the number of instances in classes in separated line
  												for i, count in enumerate(UpperCAmelCase__       ,    1      ):
  																		print(F"Number of instances in class_{i} is: {count}"      )
  												print("""-""" * 100      )
  
  												# print out mean values of classes separated line
  												for i, user_mean in enumerate(UpperCAmelCase__       ,    1      ):
  																		print(F"Mean of class_{i} is: {user_mean}"      )
  												print("""-""" * 100      )
  
  												# Generating training dataset drawn from gaussian distribution
  												a_					=		[
  												    gaussian_distribution(user_means[j]       ,    UpperCAmelCase__       ,    counts[j]      )
  												    for j in range(UpperCAmelCase__      )
  												]
  												print("""Generated Normal Distribution: \n"""       ,    UpperCAmelCase__      )
  												print("""-""" * 100      )
  
  												# Generating Ys to detecting corresponding classes
  												a_					=		y_generator(UpperCAmelCase__       ,    UpperCAmelCase__      )
  												print("""Generated Corresponding Ys: \n"""       ,    UpperCAmelCase__      )
  												print("""-""" * 100      )
  
  												# Calculating the value of actual mean for each class
  												a_					=		[calculate_mean(counts[k]       ,    x[k]      ) for k in range(UpperCAmelCase__      )]
  												# for loop iterates over number of elements in 'actual_means' list and print
  												# out them in separated line
  												for i, actual_mean in enumerate(UpperCAmelCase__       ,    1      ):
  																		print(F"Actual(Real) mean of class_{i} is: {actual_mean}"      )
  												print("""-""" * 100      )
  
  												# Calculating the value of probabilities for each class
  												a_					=		[
  												    calculate_probabilities(counts[i]       ,    sum(UpperCAmelCase__      )      ) for i in range(UpperCAmelCase__      )
  												]
  
  												# for loop iterates over number of elements in 'probabilities' list and print
  												# out them in separated line
  												for i, probability in enumerate(UpperCAmelCase__       ,    1      ):
  																		print(F"Probability of class_{i} is: {probability}"      )
  												print("""-""" * 100      )
  
  												# Calculating the values of variance for each class
  												a_					=		calculate_variance(UpperCAmelCase__       ,    UpperCAmelCase__       ,    sum(UpperCAmelCase__      )      )
  												print("""Variance: """       ,    UpperCAmelCase__      )
  												print("""-""" * 100      )
  
  												# Predicting Y values
  												# storing predicted Y values in 'pre_indexes' variable
  												a_					=		predict_y_values(UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      )
  												print("""-""" * 100      )
  
  												# Calculating Accuracy of the model
  												print(F"Accuracy: {accuracy(UpperCAmelCase__       ,    UpperCAmelCase__      )}"      )
  												print("""-""" * 100      )
  												print(""" DONE """.center(100       ,    """+"""      )      )
  
  												if input("""Press any key to restart or 'q' for quit: """      ).strip().lower() == "q":
  																		print("""\n""" + """GoodBye!""".center(100       ,    """-"""      ) + """\n"""      )
  																		break
  												system("""cls""" if name == """nt""" else """clear"""      )  # noqa: S605
  
  
  if __name__ == "__main__":
  	main()