
"""simple docstring"""







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						):
	seed(1						)
	return [gauss(_UpperCamelCase			,    _UpperCamelCase						) for _ in range(_UpperCamelCase						)]
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	return [k for k in range(_UpperCamelCase						) for _ in range(instance_count[k]						)]
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	return sum(_UpperCamelCase						) / instance_count
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	return instance_count / total_count
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		Dict		     = []  # 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
	__lowerCAmelCase     :		Any		     = len(_UpperCamelCase						)  # Number of classes in dataset
	return 1 / (total_count - n_classes) * sum(_UpperCamelCase						)
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		Optional[int]		     = []
	# 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]						)						):
			__lowerCAmelCase     :		Optional[int]		     = []  # 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						):
	__lowerCAmelCase     :		str		     = 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


lowerCamelCase__     =						TypeVar("""num""")
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase = lambda _UpperCamelCase						: True			,    _UpperCamelCase = None			,    ):
	while True:
		try:
			__lowerCAmelCase     :		int		     = 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							():
	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
		__lowerCAmelCase     :		str		     = 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
		__lowerCAmelCase     :		str		     = 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
		__lowerCAmelCase     :		Tuple		     = []  # An empty list to store instance counts of classes in dataset
		for i in range(_UpperCamelCase						):
			__lowerCAmelCase     :		Optional[int]		     = 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
		__lowerCAmelCase     :		List[str]		     = []
		for a in range(_UpperCamelCase						):
			__lowerCAmelCase     :		List[str]		     = 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
		__lowerCAmelCase     :		Any		     = [
		    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
		__lowerCAmelCase     :		Optional[Any]		     = y_generator(_UpperCamelCase			,    _UpperCamelCase						)
		print('Generated Corresponding Ys: \n'			,    _UpperCamelCase						)
		print('-' * 100						)

		# Calculating the value of actual mean for each class
		__lowerCAmelCase     :		List[Any]		     = [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
		__lowerCAmelCase     :		int		     = [
		    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
		__lowerCAmelCase     :		str		     = calculate_variance(_UpperCamelCase			,    _UpperCamelCase			,    sum(_UpperCamelCase						)						)
		print('Variance: '			,    _UpperCamelCase						)
		print('-' * 100						)

		# Predicting Y values
		# storing predicted Y values in 'pre_indexes' variable
		__lowerCAmelCase     :		int		     = 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()

"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DeiTImageProcessor




class 					A__			(      unittest.TestCase):

		def __init__( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=7			,						_SCREAMING_SNAKE_CASE=3			,						_SCREAMING_SNAKE_CASE=18			,						_SCREAMING_SNAKE_CASE=30			,						_SCREAMING_SNAKE_CASE=4_00			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,						_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,						):
			__lowerCAmelCase     :		List[str]		     = size if size is not None else {'height': 20, 'width': 20}
			__lowerCAmelCase     :		List[str]		     = crop_size if crop_size is not None else {'height': 18, 'width': 18}

			__lowerCAmelCase     :		Any		     = parent
			__lowerCAmelCase     :		Optional[Any]		     = batch_size
			__lowerCAmelCase     :		Any		     = num_channels
			__lowerCAmelCase     :		Dict		     = image_size
			__lowerCAmelCase     :		Tuple		     = min_resolution
			__lowerCAmelCase     :		Union[str, Any]		     = max_resolution
			__lowerCAmelCase     :		Optional[int]		     = do_resize
			__lowerCAmelCase     :		Any		     = size
			__lowerCAmelCase     :		Union[str, Any]		     = do_center_crop
			__lowerCAmelCase     :		List[Any]		     = crop_size
			__lowerCAmelCase     :		Optional[int]		     = do_normalize
			__lowerCAmelCase     :		int		     = image_mean
			__lowerCAmelCase     :		Tuple		     = image_std







		def 				__lowerCamelCase				( self					):
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			}




@require_torch
@require_vision
class 					A__			(      _lowerCamelCase			,	unittest.TestCase):
		A_		:			List[str]							  =      DeiTImageProcessor if is_vision_available() else None
		A_		:			int							  =      True

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Optional[int]		     = DeiTImageProcessingTester(self					)

		@property
		def 				__lowerCamelCase				( self					):
			return self.image_processor_tester.prepare_image_processor_dict()

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Any		     = self.image_processing_class(**self.image_processor_dict					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_resize'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'size'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_center_crop'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'center_crop'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_normalize'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'image_mean'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'image_std'					)					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Tuple		     = self.image_processing_class.from_dict(self.image_processor_dict					)
			self.assertEqual(image_processor.size			,						{'height': 20, 'width': 20}					)
			self.assertEqual(image_processor.crop_size			,						{'height': 18, 'width': 18}					)

			__lowerCAmelCase     :		str		     = self.image_processing_class.from_dict(self.image_processor_dict			,						size=42			,						crop_size=84					)
			self.assertEqual(image_processor.size			,						{'height': 42, 'width': 42}					)
			self.assertEqual(image_processor.crop_size			,						{'height': 84, 'width': 84}					)

		def 				__lowerCamelCase				( self					):
			pass

		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Dict		     = self.image_processing_class(**self.image_processor_dict					)
			# create random PIL images
			__lowerCAmelCase     :		List[str]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						Image.Image					)

			# Test not batched input
			__lowerCAmelCase     :		Dict		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		int		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Optional[int]		     = self.image_processing_class(**self.image_processor_dict					)
			# create random numpy tensors
			__lowerCAmelCase     :		List[str]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE			,						numpify=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						np.ndarray					)

			# Test not batched input
			__lowerCAmelCase     :		str		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		Dict		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)







		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Union[str, Any]		     = self.image_processing_class(**self.image_processor_dict					)
			# create random PyTorch tensors
			__lowerCAmelCase     :		List[str]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE			,						torchify=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						torch.Tensor					)

			# Test not batched input
			__lowerCAmelCase     :		Union[str, Any]		     = image_processing(image_inputs[0]			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)

			# Test batched
			__lowerCAmelCase     :		Optional[int]		     = image_processing(_SCREAMING_SNAKE_CASE			,						return_tensors='pt'					).pixel_values
			self.assertEqual(
			    encoded_images.shape			,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.crop_size['height'],
			        self.image_processor_tester.crop_size['width'],
			    )			,						)