






from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar
def    a__    (	A		,					A		,					A       ):
   seed(1       )
   return [gauss(A		,					A       ) for _ in range(A       )]
def    a__    (	A		,					A       ):
   return [k for k in range(A       ) for _ in range(instance_count[k]       )]
def    a__    (	A		,					A       ):
   return sum(A       ) / instance_count
def    a__    (	A		,					A       ):
   return instance_count / total_count
def    a__    (	A		,					A		,					A       ):
   lowercase__    :						Optional[Any]       =			[]  # An empty list to store all squared differences
   # iterate over number of elements in items
   for i in range(len(A       )       ):
      # 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
   lowercase__    :						str       =			len(A       )  # Number of classes in dataset
   return 1 / (total_count - n_classes) * sum(A       )
def    a__    (	A		,					A		,					A		,					A       ):
   lowercase__    :						Union[str, Any]       =			[]
   # for loop iterates over number of elements in list
   for i in range(len(A       )       ):
      # for loop iterates over number of inner items of each element
      for j in range(len(x_items[i]       )       ):
         lowercase__    :						List[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(A       )       ):
            # 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(A       )

   return [result.index(max(A       )       ) for result in results]
def    a__    (	A		,					A       ):
   lowercase__    :						str       =			sum(1 for i, j in zip(A		,					A       ) 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(A       )) * 100


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

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

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

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

      # Generating Ys to detecting corresponding classes
      lowercase__    :						Tuple       =			y_generator(A		,					A       )
      print("""Generated Corresponding Ys: \n"""		,					A       )
      print("""-""" * 100       )

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

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

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

      # Calculating the values of variance for each class
      lowercase__    :						Optional[Any]       =			calculate_variance(A		,					A		,					sum(A       )       )
      print("""Variance: """		,					A       )
      print("""-""" * 100       )

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

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










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a 			=							{
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a 			=							[
     'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MobileViTV2ForImageClassification',
     'MobileViTV2ForSemanticSegmentation',
     'MobileViTV2Model',
     'MobileViTV2PreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

 a 			=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


