

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    (      __UpperCamelCase     :		float   ,       __UpperCamelCase     :		float   ,       __UpperCamelCase     :		int ):
      seed(1 )
      return [gauss(__UpperCamelCase   ,       __UpperCamelCase ) for _ in range(__UpperCamelCase )]





def 			_lowercase    (      __UpperCamelCase     :		int   ,       __UpperCamelCase     :		list ):
      return [k for k in range(__UpperCamelCase ) for _ in range(instance_count[k] )]





def 			_lowercase    (      __UpperCamelCase     :		int   ,       __UpperCamelCase     :		list ):
      return sum(__UpperCamelCase ) / instance_count





def 			_lowercase    (      __UpperCamelCase     :		int   ,       __UpperCamelCase     :		int ):
      return instance_count / total_count





def 			_lowercase    (      __UpperCamelCase     :		list   ,       __UpperCamelCase     :		list   ,       __UpperCamelCase     :		int ):
      snake_case__					=   []  # 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
      snake_case__					=   len(__UpperCamelCase )  # Number of classes in dataset
      return 1 / (total_count - n_classes) * sum(__UpperCamelCase )





def 			_lowercase    (      __UpperCamelCase     :		list   ,       __UpperCamelCase     :		list   ,       __UpperCamelCase     :		float   ,       __UpperCamelCase     :		list ):
      snake_case__					=   []
      # 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] ) ):
                  snake_case__					=   []  # 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 			_lowercase    (      __UpperCamelCase     :		list   ,       __UpperCamelCase     :		list ):
      snake_case__					=   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						:   Any										=    TypeVar('''num''')





def 			_lowercase    (      __UpperCamelCase     :		Callable[[object], num]   ,       __UpperCamelCase     :		str   ,       __UpperCamelCase     :		str   ,       __UpperCamelCase     :		Callable[[num], bool] = lambda __UpperCamelCase : True   ,       __UpperCamelCase     :		str | None = None   ,       ):
      while True:
            try:
                  snake_case__					=   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 			_lowercase    (      ):
      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__					=   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
            snake_case__					=   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
            snake_case__					=   []  # An empty list to store instance counts of classes in dataset
            for i in range(__UpperCamelCase ):
                  snake_case__					=   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
            snake_case__					=   []
            for a in range(__UpperCamelCase ):
                  snake_case__					=   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
            snake_case__					=   [
                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
            snake_case__					=   y_generator(__UpperCamelCase   ,       __UpperCamelCase )
            print("""Generated Corresponding Ys: \n"""   ,       __UpperCamelCase )
            print("""-""" * 100 )

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

            # Predicting Y values
            # storing predicted Y values in 'pre_indexes' variable
            snake_case__					=   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
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()
class     SCREAMING_SNAKE_CASE__     (						unittest.TestCase		):





       '''simple docstring'''




       @property
       def UpperCAmelCase_						(						self       :						Any							)			->    List[str]:
             torch.manual_seed(0							)
             snake_case__					=   UNetaDModel(
                 block_out_channels=(32, 64)    ,		layers_per_block=2    ,		sample_size=32    ,		in_channels=3    ,		out_channels=3    ,		down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,		up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,		)
             return model

       def UpperCAmelCase_						(						self       :						Optional[int]							)			->    int:
             snake_case__					=   """cpu"""
             snake_case__					=   self.dummy_uncond_unet
             snake_case__					=   DDPMScheduler()

             snake_case__					=   DDPMPipeline(unet=lowerCAmelCase__    ,		scheduler=lowerCAmelCase__							)
             ddpm.to(lowerCAmelCase__							)
             ddpm.set_progress_bar_config(disable=lowerCAmelCase__							)

             snake_case__					=   torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
             snake_case__					=   ddpm(generator=lowerCAmelCase__    ,		num_inference_steps=2    ,		output_type="""numpy"""							).images

             snake_case__					=   torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
             snake_case__					=   ddpm(generator=lowerCAmelCase__    ,		num_inference_steps=2    ,		output_type="""numpy"""    ,		return_dict=lowerCAmelCase__							)[0]

             snake_case__					=   image[0, -3:, -3:, -1]
             snake_case__					=   image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             snake_case__					=   np.array(
                 [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2

       def UpperCAmelCase_						(						self       :						Tuple							)			->    str:
             snake_case__					=   self.dummy_uncond_unet
             snake_case__					=   DDPMScheduler(prediction_type="""sample"""							)

             snake_case__					=   DDPMPipeline(unet=lowerCAmelCase__    ,		scheduler=lowerCAmelCase__							)
             ddpm.to(lowerCAmelCase__							)
             ddpm.set_progress_bar_config(disable=lowerCAmelCase__							)

             snake_case__					=   torch.manual_seed(0							)
             snake_case__					=   ddpm(generator=lowerCAmelCase__    ,		num_inference_steps=2    ,		output_type="""numpy"""							).images

             snake_case__					=   torch.manual_seed(0							)
             snake_case__					=   ddpm(generator=lowerCAmelCase__    ,		num_inference_steps=2    ,		output_type="""numpy"""							)[0]

             snake_case__					=   image[0, -3:, -3:, -1]
             snake_case__					=   image_eps[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             snake_case__					=   1E-2 if torch_device != """mps""" else 3E-2
             assert np.abs(image_slice.flatten() - image_eps_slice.flatten()							).max() < tolerance



@slow
@require_torch_gpu
class     SCREAMING_SNAKE_CASE__     (						unittest.TestCase		):





       '''simple docstring'''




       def UpperCAmelCase_						(						self       :						Tuple							)			->    Optional[int]:
             snake_case__					=   """google/ddpm-cifar10-32"""

             snake_case__					=   UNetaDModel.from_pretrained(lowerCAmelCase__							)
             snake_case__					=   DDPMScheduler.from_pretrained(lowerCAmelCase__							)

             snake_case__					=   DDPMPipeline(unet=lowerCAmelCase__    ,		scheduler=lowerCAmelCase__							)
             ddpm.to(lowerCAmelCase__							)
             ddpm.set_progress_bar_config(disable=lowerCAmelCase__							)

             snake_case__					=   torch.manual_seed(0							)
             snake_case__					=   ddpm(generator=lowerCAmelCase__    ,		output_type="""numpy"""							).images

             snake_case__					=   image[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             snake_case__					=   np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]							)
             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2






