




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_		):
    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_		):
    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_		):
    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


SCREAMING_SNAKE_CASE_						:					int			   =       TypeVar('''num''')
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_ = lambda UpperCAmelCase_		: True   ,    UpperCAmelCase_ = None   ,    ):
    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 ():
    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()












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 SwinaSRImageProcessor
 from transformers.image_transforms import get_image_size







class 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



     def __init__(      self :       Optional[int]						,				A :       Dict						,				A :       Union[str, Any]=7						,				A :       List[str]=3						,				A :       Optional[int]=18						,				A :       Optional[int]=30						,				A :       Optional[Any]=4_00						,				A :       Optional[Any]=True						,				A :       Tuple=1 / 2_55						,				A :       Tuple=True						,				A :       Optional[int]=8						,				)			->			Any:






         """simple docstring"""
         a__      =       parent
         a__      =       batch_size
         a__      =       num_channels
         a__      =       image_size
         a__      =       min_resolution
         a__      =       max_resolution
         a__      =       do_rescale
         a__      =       rescale_factor
         a__      =       do_pad
         a__      =       pad_size

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






         """simple docstring"""
         return {
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
             "pad_size": self.pad_size,
         }






     def 				__SCREAMING_SNAKE_CASE		(      self :       int						,				A :       int=False						,				A :       Any=False						,				A :       List[Any]=False    )			->			Optional[Any]:






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

         if equal_resolution:
             a__      =       []
             for i in range(self.batch_size    ):
                 image_inputs.append(
                     np.random.randint(
                         2_55						,				size=(self.num_channels, self.max_resolution, self.max_resolution)						,				dtype=np.uinta    )    )
         else:
             a__      =       []
             for i in range(self.batch_size    ):
                 a__ ,		a__      =       np.random.choice(np.arange(self.min_resolution						,				self.max_resolution    )						,				2    )
                 image_inputs.append(np.random.randint(2_55						,				size=(self.num_channels, width, height)						,				dtype=np.uinta    )    )

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

         if torchify:
             a__      =       [torch.from_numpy(A    ) for x in image_inputs]

         return image_inputs







@require_torch
@require_vision
class 		__UpperCAmelCase(		lowercase__       ,	unittest.TestCase ):





     """simple docstring"""



     __UpperCAmelCase	: Dict						=    SwinaSRImageProcessor if is_vision_available() else None

     def 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			Dict:






         """simple docstring"""
         a__      =       SwinaSRImageProcessingTester(self    )

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]    )			->			List[Any]:






         """simple docstring"""
         return self.image_processor_tester.prepare_image_processor_dict()

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			int:






         """simple docstring"""
         a__      =       self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(A						,				"""do_rescale"""    )    )
         self.assertTrue(hasattr(A						,				"""rescale_factor"""    )    )
         self.assertTrue(hasattr(A						,				"""do_pad"""    )    )
         self.assertTrue(hasattr(A						,				"""pad_size"""    )    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			List[Any]:






         """simple docstring"""
         pass

     def 				__SCREAMING_SNAKE_CASE		(      self :       int						,				A :       Optional[Any]    )			->			List[str]:






         """simple docstring"""
         a__ ,		a__      =       get_image_size(A    )
         a__      =       self.image_processor_tester.pad_size

         a__      =       (old_height // size + 1) * size - old_height
         a__      =       (old_width // size + 1) * size - old_width
         return old_height + pad_height, old_width + pad_width

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






         """simple docstring"""
         # Initialize image_processor
         a__      =       self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         a__      =       self.image_processor_tester.prepare_inputs(equal_resolution=A    )
         for image in image_inputs:
             self.assertIsInstance(A						,				Image.Image    )

         # Test not batched input
         a__      =       image_processor(image_inputs[0]						,				return_tensors="""pt"""    ).pixel_values
         a__ ,		a__      =       self.calculate_expected_size(np.array(image_inputs[0]    )    )
         self.assertEqual(
             encoded_images.shape						,				(
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )						,				)

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






         """simple docstring"""
         # Initialize image_processor
         a__      =       self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         a__      =       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
         a__      =       image_processor(image_inputs[0]						,				return_tensors="""pt"""    ).pixel_values
         a__ ,		a__      =       self.calculate_expected_size(image_inputs[0]    )
         self.assertEqual(
             encoded_images.shape						,				(
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )						,				)






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






         """simple docstring"""
         # Initialize image_processor
         a__      =       self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         a__      =       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
         a__      =       image_processor(image_inputs[0]						,				return_tensors="""pt"""    ).pixel_values
         a__ ,		a__      =       self.calculate_expected_size(image_inputs[0]    )
         self.assertEqual(
             encoded_images.shape						,				(
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )						,				)






