
'''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


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import CLIPImageProcessor


class        SCREAMING_SNAKE_CASE__	(			unittest.TestCase):







				def __init__(    self		,							A_		,							A_=7		,							A_=3		,							A_=18		,							A_=30		,							A_=400		,							A_=True		,							A_=None		,							A_=True		,							A_=None		,							A_=True		,							A_=[0.48_145_466, 0.4_578_275, 0.40_821_073]		,							A_=[0.26_862_954, 0.26_130_258, 0.27_577_711]		,							A_=True		,							)->						List[Any]:







											'''simple docstring'''






											UpperCamelCase					       =			size if size is not None else {'shortest_edge': 20}
											UpperCamelCase					       =			crop_size if crop_size is not None else {'height': 18, 'width': 18}
											UpperCamelCase					       =			parent
											UpperCamelCase					       =			batch_size
											UpperCamelCase					       =			num_channels
											UpperCamelCase					       =			image_size
											UpperCamelCase					       =			min_resolution
											UpperCamelCase					       =			max_resolution
											UpperCamelCase					       =			do_resize
											UpperCamelCase					       =			size
											UpperCamelCase					       =			do_center_crop
											UpperCamelCase					       =			crop_size
											UpperCamelCase					       =			do_normalize
											UpperCamelCase					       =			image_mean
											UpperCamelCase					       =			image_std
											UpperCamelCase					       =			do_convert_rgb







				def 		UpperCAmelCase_  (    self			)->						Dict:







											'''simple docstring'''






											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,
											    "do_convert_rgb": self.do_convert_rgb,
											}







				def 		UpperCAmelCase_  (    self		,							A_=False		,							A_=False		,							A_=False			)->						Optional[int]:







											'''simple docstring'''






											assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

											if equal_resolution:
																		UpperCamelCase					       =			[]
																		for i in range(self.batch_size			):
																									image_inputs.append(
																									    np.random.randint(
																									        255		,							size=(self.num_channels, self.max_resolution, self.max_resolution)		,							dtype=np.uinta			)			)
											else:
																		UpperCamelCase					       =			[]
																		for i in range(self.batch_size			):
																									UpperCamelCase       ,     UpperCamelCase					       =			np.random.choice(np.arange(self.min_resolution		,							self.max_resolution			)		,							2			)
																									image_inputs.append(np.random.randint(255		,							size=(self.num_channels, width, height)		,							dtype=np.uinta			)			)

											if not numpify and not torchify:
																		# PIL expects the channel dimension as last dimension
																		UpperCamelCase					       =			[Image.fromarray(np.moveaxis(A_		,							0		,							-1			)			) for x in image_inputs]

											if torchify:
																		UpperCamelCase					       =			[torch.from_numpy(A_			) for x in image_inputs]

											return image_inputs


@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE__	(			snake_case_				,    unittest.TestCase):
				lowerCAmelCase_			       =				CLIPImageProcessor if is_vision_available() else None







				def 		UpperCAmelCase_  (    self			)->						Dict:







											'''simple docstring'''






											UpperCamelCase					       =			CLIPImageProcessingTester(self			)







				@property
				def 		UpperCAmelCase_  (    self			)->						List[str]:







											'''simple docstring'''






											return self.image_processor_tester.prepare_image_processor_dict()







				def 		UpperCAmelCase_  (    self			)->						List[str]:







											'''simple docstring'''






											UpperCamelCase					       =			self.image_processing_class(**self.image_processor_dict			)
											self.assertTrue(hasattr(A_		,							'do_resize'			)			)
											self.assertTrue(hasattr(A_		,							'size'			)			)
											self.assertTrue(hasattr(A_		,							'do_center_crop'			)			)
											self.assertTrue(hasattr(A_		,							'center_crop'			)			)
											self.assertTrue(hasattr(A_		,							'do_normalize'			)			)
											self.assertTrue(hasattr(A_		,							'image_mean'			)			)
											self.assertTrue(hasattr(A_		,							'image_std'			)			)
											self.assertTrue(hasattr(A_		,							'do_convert_rgb'			)			)







				def 		UpperCAmelCase_  (    self			)->						List[str]:







											'''simple docstring'''






											UpperCamelCase					       =			self.image_processing_class.from_dict(self.image_processor_dict			)
											self.assertEqual(image_processor.size		,							{'shortest_edge': 20}			)
											self.assertEqual(image_processor.crop_size		,							{'height': 18, 'width': 18}			)

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







				def 		UpperCAmelCase_  (    self			)->						Dict:







											'''simple docstring'''






											pass







				def 		UpperCAmelCase_  (    self			)->						Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase					       =			self.image_processing_class(**self.image_processor_dict			)
											# create random PIL images
											UpperCamelCase					       =			self.image_processor_tester.prepare_inputs(equal_resolution=A_			)
											for image in image_inputs:
																		self.assertIsInstance(A_		,							Image.Image			)

											# Test not batched input
											UpperCamelCase					       =			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
											UpperCamelCase					       =			image_processing(A_		,							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 		UpperCAmelCase_  (    self			)->						Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase					       =			self.image_processing_class(**self.image_processor_dict			)
											# create random numpy tensors
											UpperCamelCase					       =			self.image_processor_tester.prepare_inputs(equal_resolution=A_		,							numpify=A_			)
											for image in image_inputs:
																		self.assertIsInstance(A_		,							np.ndarray			)

											# Test not batched input
											UpperCamelCase					       =			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
											UpperCamelCase					       =			image_processing(A_		,							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 		UpperCAmelCase_  (    self			)->						List[str]:







											'''simple docstring'''






											UpperCamelCase					       =			self.image_processing_class(**self.image_processor_dict			)
											# create random PyTorch tensors
											UpperCamelCase					       =			self.image_processor_tester.prepare_inputs(equal_resolution=A_		,							torchify=A_			)
											for image in image_inputs:
																		self.assertIsInstance(A_		,							torch.Tensor			)

											# Test not batched input
											UpperCamelCase					       =			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
											UpperCamelCase					       =			image_processing(A_		,							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'],
											    )		,							)


@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE__	(			snake_case_				,    unittest.TestCase):
				lowerCAmelCase_			       =				CLIPImageProcessor if is_vision_available() else None







				def 		UpperCAmelCase_  (    self			)->						int:







											'''simple docstring'''






											UpperCamelCase					       =			CLIPImageProcessingTester(self		,							num_channels=4			)
											UpperCamelCase					       =			3







				@property
				def 		UpperCAmelCase_  (    self			)->						Tuple:







											'''simple docstring'''






											return self.image_processor_tester.prepare_image_processor_dict()







				def 		UpperCAmelCase_  (    self			)->						List[str]:







											'''simple docstring'''






											UpperCamelCase					       =			self.image_processing_class(**self.image_processor_dict			)
											self.assertTrue(hasattr(A_		,							'do_resize'			)			)
											self.assertTrue(hasattr(A_		,							'size'			)			)
											self.assertTrue(hasattr(A_		,							'do_center_crop'			)			)
											self.assertTrue(hasattr(A_		,							'center_crop'			)			)
											self.assertTrue(hasattr(A_		,							'do_normalize'			)			)
											self.assertTrue(hasattr(A_		,							'image_mean'			)			)
											self.assertTrue(hasattr(A_		,							'image_std'			)			)
											self.assertTrue(hasattr(A_		,							'do_convert_rgb'			)			)







				def 		UpperCAmelCase_  (    self			)->						Union[str, Any]:







											'''simple docstring'''






											pass







				def 		UpperCAmelCase_  (    self			)->						Dict:







											'''simple docstring'''






											UpperCamelCase					       =			self.image_processing_class(**self.image_processor_dict			)
											# create random PIL images
											UpperCamelCase					       =			self.image_processor_tester.prepare_inputs(equal_resolution=A_			)
											for image in image_inputs:
																		self.assertIsInstance(A_		,							Image.Image			)

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

											# Test batched
											UpperCamelCase					       =			image_processing(A_		,							return_tensors='pt'			).pixel_values
											self.assertEqual(
											    encoded_images.shape		,							(
											        self.image_processor_tester.batch_size,
											        self.expected_encoded_image_num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )		,							)











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





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





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			return sum(lowercase	) / instance_count





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			return instance_count / total_count





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				[]  # 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
			__lowerCAmelCase											=				len(lowercase	)  # Number of classes in dataset
			return 1 / (total_count - n_classes) * sum(lowercase	)





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase					,	lowercase	)					->					list:
			__lowerCAmelCase											=				[]
			# 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]	)	):
									__lowerCAmelCase											=				[]  # 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    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				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					: Optional[int]      =    TypeVar("""num""")





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

						# Calculating the value of actual mean for each class
						__lowerCAmelCase											=				[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
						__lowerCAmelCase											=				[
						    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
						__lowerCAmelCase											=				calculate_variance(lowercase					,	lowercase					,	sum(lowercase	)	)
						print("""Variance: """					,	lowercase	)
						print("""-""" * 100	)

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

