
"""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_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			list:


     '''simple docstring'''
     seed(1 )
     return [gauss(lowerCamelCase_	,				lowerCamelCase_ ) for _ in range(lowerCamelCase_ )]


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			list:


     '''simple docstring'''
     return [k for k in range(lowerCamelCase_ ) for _ in range(instance_count[k] )]


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			float:


     '''simple docstring'''
     return sum(lowerCamelCase_ ) / instance_count


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			float:


     '''simple docstring'''
     return instance_count / total_count


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			float:


     '''simple docstring'''
     __a:						Dict		    =					[]  # An empty list to store all squared differences
     # iterate over number of elements in items
     for i in range(len(lowerCamelCase_ ) ):
          # 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:						Tuple		    =					len(lowerCamelCase_ )  # Number of classes in dataset
     return 1 / (total_count - n_classes) * sum(lowerCamelCase_ )


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			list:


     '''simple docstring'''
     __a:						List[str]		    =					[]
     # for loop iterates over number of elements in list
     for i in range(len(lowerCamelCase_ ) ):
          # for loop iterates over number of inner items of each element
          for j in range(len(x_items[i] ) ):
               __a:						Optional[Any]		    =					[]  # 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(lowerCamelCase_ ) ):
                    # 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(lowerCamelCase_ )

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


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			float:


     '''simple docstring'''
     __a:						Optional[Any]		    =					sum(1 for i, j in zip(lowerCamelCase_	,				lowerCamelCase_ ) 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(lowerCamelCase_ )) * 1_00


lowercase		:						str =					TypeVar('num')


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ = lambda lowerCamelCase_ : True	,				lowerCamelCase_ = None	,				)     ->			num:


     '''simple docstring'''
     while True:
          try:
               __a:						List[Any]		    =					input_type(input(lowerCamelCase_ ).strip() or default )
               if condition(lowerCamelCase_ ):
                    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[Any]:


     '''simple docstring'''
     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:						Optional[int]		    =					valid_input(
              input_type=lowerCamelCase_	,				condition=lambda lowerCamelCase_ : x > 0	,				input_msg="""Enter the number of classes (Data Groupings): """	,				err_msg="""Number of classes should be positive!"""	,				)

          print("""-""" * 1_00 )

          # Trying to get the value of standard deviation
          __a:						int		    =					valid_input(
              input_type=lowerCamelCase_	,				condition=lambda lowerCamelCase_ : 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("""-""" * 1_00 )

          # Trying to get number of instances in classes and theirs means to generate
          # dataset
          __a:						int		    =					[]  # An empty list to store instance counts of classes in dataset
          for i in range(lowerCamelCase_ ):
               __a:						List[str]		    =					valid_input(
                   input_type=lowerCamelCase_	,				condition=lambda lowerCamelCase_ : x > 0	,				input_msg=(f"""Enter The number of instances for class_{i+1}: """)	,				err_msg="""Number of instances should be positive!"""	,				)
               counts.append(lowerCamelCase_ )
          print("""-""" * 1_00 )

          # An empty list to store values of user-entered means of classes
          __a:						List[Any]		    =					[]
          for a in range(lowerCamelCase_ ):
               __a:						List[Any]		    =					valid_input(
                   input_type=lowerCamelCase_	,				input_msg=(f"""Enter the value of mean for class_{a+1}: """)	,				err_msg="""This is an invalid value."""	,				)
               user_means.append(lowerCamelCase_ )
          print("""-""" * 1_00 )

          print("""Standard deviation: """	,				lowerCamelCase_ )
          # print out the number of instances in classes in separated line
          for i, count in enumerate(lowerCamelCase_	,				1 ):
               print(f"""Number of instances in class_{i} is: {count}""" )
          print("""-""" * 1_00 )

          # print out mean values of classes separated line
          for i, user_mean in enumerate(lowerCamelCase_	,				1 ):
               print(f"""Mean of class_{i} is: {user_mean}""" )
          print("""-""" * 1_00 )

          # Generating training dataset drawn from gaussian distribution
          __a:						Tuple		    =					[
              gaussian_distribution(user_means[j]	,				lowerCamelCase_	,				counts[j] )
              for j in range(lowerCamelCase_ )
          ]
          print("""Generated Normal Distribution: \n"""	,				lowerCamelCase_ )
          print("""-""" * 1_00 )

          # Generating Ys to detecting corresponding classes
          __a:						List[Any]		    =					y_generator(lowerCamelCase_	,				lowerCamelCase_ )
          print("""Generated Corresponding Ys: \n"""	,				lowerCamelCase_ )
          print("""-""" * 1_00 )

          # Calculating the value of actual mean for each class
          __a:						Union[str, Any]		    =					[calculate_mean(counts[k]	,				x[k] ) for k in range(lowerCamelCase_ )]
          # for loop iterates over number of elements in 'actual_means' list and print
          # out them in separated line
          for i, actual_mean in enumerate(lowerCamelCase_	,				1 ):
               print(f"""Actual(Real) mean of class_{i} is: {actual_mean}""" )
          print("""-""" * 1_00 )

          # Calculating the value of probabilities for each class
          __a:						Optional[Any]		    =					[
              calculate_probabilities(counts[i]	,				sum(lowerCamelCase_ ) ) for i in range(lowerCamelCase_ )
          ]

          # for loop iterates over number of elements in 'probabilities' list and print
          # out them in separated line
          for i, probability in enumerate(lowerCamelCase_	,				1 ):
               print(f"""Probability of class_{i} is: {probability}""" )
          print("""-""" * 1_00 )

          # Calculating the values of variance for each class
          __a:						Tuple		    =					calculate_variance(lowerCamelCase_	,				lowerCamelCase_	,				sum(lowerCamelCase_ ) )
          print("""Variance: """	,				lowerCamelCase_ )
          print("""-""" * 1_00 )

          # Predicting Y values
          # storing predicted Y values in 'pre_indexes' variable
          __a:						Optional[Any]		    =					predict_y_values(lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )
          print("""-""" * 1_00 )

          # Calculating Accuracy of the model
          print(f"""Accuracy: {accuracy(lowerCamelCase_	,				lowerCamelCase_ )}""" )
          print("""-""" * 1_00 )
          print(""" DONE """.center(1_00	,				"""+""" ) )

          if input("""Press any key to restart or 'q' for quit: """ ).strip().lower() == "q":
               print("""\n""" + """GoodBye!""".center(1_00	,				"""-""" ) + """\n""" )
               break
          system("""cls""" if name == """nt""" else """clear""" )  # noqa: S605


if __name__ == "__main__":
   main()


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase		:						int =					logging.get_logger(__name__)

lowercase		:						Any =					{
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''regnet'''
							SCREAMING_SNAKE_CASE		     =						['''x''', '''y''']






							def __init__(					self						:			Union[str, Any]						, snake_case__						:			Any=3						, snake_case__						:			str=3_2						, snake_case__						:			Tuple=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]						, snake_case__						:			Union[str, Any]=[2, 6, 1_2, 2]						, snake_case__						:			str=6_4						, snake_case__						:			Optional[int]="y"						, snake_case__						:			Tuple="relu"						, **snake_case__						:			str						, ):






												"""simple docstring"""



												super().__init__(**snake_case__    )
												if layer_type not in self.layer_types:
																	raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types    )}"""    )
												__a:						Any		    =					num_channels
												__a:						List[Any]		    =					embedding_size
												__a:						Optional[int]		    =					hidden_sizes
												__a:						Optional[int]		    =					depths
												__a:						Optional[Any]		    =					groups_width
												__a:						Tuple		    =					layer_type
												__a:						str		    =					hidden_act
												# always downsample in the first stage
												__a:						List[str]		    =					True
