





'''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    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->list:
						seed(1    )
						return [gauss(lowercase__					,	lowercase__    ) for _ in range(lowercase__    )]





def    lowercase_   (		lowercase__					,	lowercase__    ) ->list:
						return [k for k in range(lowercase__    ) for _ in range(instance_count[k]    )]





def    lowercase_   (		lowercase__					,	lowercase__    ) ->float:
						return sum(lowercase__    ) / instance_count





def    lowercase_   (		lowercase__					,	lowercase__    ) ->float:
						return instance_count / total_count





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->float:
						_snake_case: Dict	       =		[]  # An empty list to store all squared differences
						# iterate over number of elements in items
						for i in range(len(lowercase__    )    ):
												# 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
						_snake_case: Any	       =		len(lowercase__    )  # Number of classes in dataset
						return 1 / (total_count - n_classes) * sum(lowercase__    )





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__					,	lowercase__    ) ->list:
						_snake_case: List[Any]	       =		[]
						# for loop iterates over number of elements in list
						for i in range(len(lowercase__    )    ):
												# for loop iterates over number of inner items of each element
												for j in range(len(x_items[i]    )    ):
																		_snake_case: List[str]	       =		[]  # 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(lowercase__    )    ):
																								# 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(lowercase__    )

						return [result.index(max(lowercase__    )    ) for result in results]





def    lowercase_   (		lowercase__					,	lowercase__    ) ->float:
						_snake_case: List[Any]	       =		sum(1 for i, j in zip(lowercase__					,	lowercase__    ) 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(lowercase__    )) * 100


A :			Any							   =      TypeVar('num')





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__					,	lowercase__ = lambda lowercase__    : True					,	lowercase__ = None					,	) ->num:
						while True:
												try:
																		_snake_case: int	       =		input_type(input(lowercase__    ).strip() or default    )
																		if condition(lowercase__    ):
																								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_   (		) ->Optional[Any]:
						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
												_snake_case: List[str]	       =		valid_input(
												    input_type=lowercase__					,	condition=lambda lowercase__    : 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
												_snake_case: Tuple	       =		valid_input(
												    input_type=lowercase__					,	condition=lambda lowercase__    : 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
												_snake_case: List[str]	       =		[]  # An empty list to store instance counts of classes in dataset
												for i in range(lowercase__    ):
																		_snake_case: Dict	       =		valid_input(
																		    input_type=lowercase__					,	condition=lambda lowercase__    : x > 0					,	input_msg=(F'''Enter The number of instances for class_{i+1}: ''')					,	err_msg='Number of instances should be positive!'					,	)
																		counts.append(lowercase__    )
												print('-' * 100    )

												# An empty list to store values of user-entered means of classes
												_snake_case: Tuple	       =		[]
												for a in range(lowercase__    ):
																		_snake_case: str	       =		valid_input(
																		    input_type=lowercase__					,	input_msg=(F'''Enter the value of mean for class_{a+1}: ''')					,	err_msg='This is an invalid value.'					,	)
																		user_means.append(lowercase__    )
												print('-' * 100    )

												print('Standard deviation: '					,	lowercase__    )
												# print out the number of instances in classes in separated line
												for i, count in enumerate(lowercase__					,	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(lowercase__					,	1    ):
																		print(F'''Mean of class_{i} is: {user_mean}'''    )
												print('-' * 100    )

												# Generating training dataset drawn from gaussian distribution
												_snake_case: List[Any]	       =		[
												    gaussian_distribution(user_means[j]					,	lowercase__					,	counts[j]    )
												    for j in range(lowercase__    )
												]
												print('Generated Normal Distribution: \n'					,	lowercase__    )
												print('-' * 100    )

												# Generating Ys to detecting corresponding classes
												_snake_case: List[str]	       =		y_generator(lowercase__					,	lowercase__    )
												print('Generated Corresponding Ys: \n'					,	lowercase__    )
												print('-' * 100    )

												# Calculating the value of actual mean for each class
												_snake_case: List[str]	       =		[calculate_mean(counts[k]					,	x[k]    ) for k in range(lowercase__    )]
												# for loop iterates over number of elements in 'actual_means' list and print
												# out them in separated line
												for i, actual_mean in enumerate(lowercase__					,	1    ):
																		print(F'''Actual(Real) mean of class_{i} is: {actual_mean}'''    )
												print('-' * 100    )

												# Calculating the value of probabilities for each class
												_snake_case: List[str]	       =		[
												    calculate_probabilities(counts[i]					,	sum(lowercase__    )    ) for i in range(lowercase__    )
												]

												# for loop iterates over number of elements in 'probabilities' list and print
												# out them in separated line
												for i, probability in enumerate(lowercase__					,	1    ):
																		print(F'''Probability of class_{i} is: {probability}'''    )
												print('-' * 100    )

												# Calculating the values of variance for each class
												_snake_case: Union[str, Any]	       =		calculate_variance(lowercase__					,	lowercase__					,	sum(lowercase__    )    )
												print('Variance: '					,	lowercase__    )
												print('-' * 100    )

												# Predicting Y values
												# storing predicted Y values in 'pre_indexes' variable
												_snake_case: Optional[int]	       =		predict_y_values(lowercase__					,	lowercase__					,	lowercase__					,	lowercase__    )
												print('-' * 100    )

												# Calculating Accuracy of the model
												print(F'''Accuracy: {accuracy(lowercase__					,	lowercase__    )}'''    )
												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

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:







		class       lowerCamelCase       :


							@staticmethod
							def  SCREAMING_SNAKE_CASE_		(       *__snake_case						:	Optional[Any]							,  **__snake_case						:	str							):





													'''simple docstring'''





													pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class       lowerCamelCase       (    unittest.TestCase	):
					_SCREAMING_SNAKE_CASE				 =     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
					_SCREAMING_SNAKE_CASE				 =     TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	Any							,  __snake_case						:	Tuple							,  __snake_case						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		ImageClassificationPipeline(model=__snake_case							,  image_processor=__snake_case							,  top_k=2							)
											_snake_case: Tuple	       =		[
											    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
											    'http://images.cocodataset.org/val2017/000000039769.jpg',
											]
											return image_classifier, examples


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	Any							,  __snake_case						:	str							):





											'''simple docstring'''





											_snake_case: Dict	       =		image_classifier('./tests/fixtures/tests_samples/COCO/000000039769.png'							)

											self.assertEqual(
											    __snake_case							,  [
											        {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											    ]							,  )

											import datasets

											_snake_case: str	       =		datasets.load_dataset('hf-internal-testing/fixtures_image_utils'							,  'image'							,  split='test'							)

											# Accepts URL + PIL.Image + lists
											_snake_case: List[str]	       =		image_classifier(
											    [
											        Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        # RGBA
											        dataset[0]['file'],
											        # LA
											        dataset[1]['file'],
											        # L
											        dataset[2]['file'],
											    ]							)
											self.assertEqual(
											    __snake_case							,  [
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											    ]							,  )


					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: Any	       =		'hf-internal-testing/tiny-random-vit'
											_snake_case: List[str]	       =		pipeline('image-classification'							,  model=__snake_case							)

											_snake_case: List[str]	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]							,  )

											_snake_case: Dict	       =		image_classifier(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]							,  top_k=2							,  )
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											    ]							,  )


					@require_tf
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: List[str]	       =		'hf-internal-testing/tiny-random-vit'
											_snake_case: List[str]	       =		pipeline('image-classification'							,  model=__snake_case							,  framework='tf'							)

											_snake_case: Any	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]							,  )

											_snake_case: str	       =		image_classifier(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]							,  top_k=2							,  )
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											    ]							,  )


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		PreTrainedTokenizer()

											# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
											_snake_case: Any	       =		pipeline(
											    'image-classification'							,  model='hf-internal-testing/tiny-random-vit'							,  tokenizer=__snake_case							)

											self.assertIs(image_classifier.tokenizer							,  __snake_case							)




					@slow
					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: Any	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-conv'							)
											_snake_case: int	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.4_385, 'label': 'tabby, tabby cat'},
											        {'score': 0.321, 'label': 'tiger cat'},
											        {'score': 0.0_502, 'label': 'Egyptian cat'},
											        {'score': 0.0_137, 'label': 'crib, cot'},
											        {'score': 0.007, 'label': 'radiator'},
											    ]							,  )

											_snake_case: Union[str, Any]	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-fourier'							)
											_snake_case: int	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.5_658, 'label': 'tabby, tabby cat'},
											        {'score': 0.1_309, 'label': 'tiger cat'},
											        {'score': 0.0_722, 'label': 'Egyptian cat'},
											        {'score': 0.0_707, 'label': 'remote control, remote'},
											        {'score': 0.0_082, 'label': 'computer keyboard, keypad'},
											    ]							,  )

											_snake_case: Optional[int]	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-learned'							)
											_snake_case: str	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.3_022, 'label': 'tabby, tabby cat'},
											        {'score': 0.2_362, 'label': 'Egyptian cat'},
											        {'score': 0.1_856, 'label': 'tiger cat'},
											        {'score': 0.0_324, 'label': 'remote control, remote'},
											        {'score': 0.0_096, 'label': 'quilt, comforter, comfort, puff'},
											    ]							,  )





