
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       (   _a			,   _a			,   _a  ):
  """simple docstring"""




  seed(1  )
  return [gauss(_a			,   _a  ) for _ in range(_a  )]


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




  return [k for k in range(_a  ) for _ in range(instance_count[k]  )]


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




  return sum(_a  ) / instance_count


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




  return instance_count / total_count


def 		_lowerCamelCase       (   _a			,   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						[]  # 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
  _lowerCamelCase      =						len(_a  )  # Number of classes in dataset
  return 1 / (total_count - n_classes) * sum(_a  )


def 		_lowerCamelCase       (   _a			,   _a			,   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						[]
  # 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]  )  ):
      _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(_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 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						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  )) * 1_0_0


_UpperCAmelCase    			=					TypeVar("num")


def 		_lowerCamelCase       (   _a			,   _a			,   _a			,   _a = lambda _a  : True			,   _a = None			,   ):
  """simple docstring"""




  while True:
    try:
      _lowerCamelCase      =						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 		_lowerCamelCase       (   ):
  """simple docstring"""




  while True:
    print(''' Linear Discriminant Analysis '''.center(5_0			,   '''*'''  )  )
    print('''*''' * 5_0			,   '''\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=_a			,   condition=lambda _a  : x > 0			,   input_msg='''Enter the number of classes (Data Groupings): '''			,   err_msg='''Number of classes should be positive!'''			,   )

    print('''-''' * 1_0_0  )

    # Trying to get the value of standard deviation
    _lowerCamelCase      =						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('''-''' * 1_0_0  )

    # 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(_a  ):
      _lowerCamelCase      =						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('''-''' * 1_0_0  )

    # An empty list to store values of user-entered means of classes
    _lowerCamelCase      =						[]
    for a in range(_a  ):
      _lowerCamelCase      =						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('''-''' * 1_0_0  )

    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('''-''' * 1_0_0  )

    # 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('''-''' * 1_0_0  )

    # Generating training dataset drawn from gaussian distribution
    _lowerCamelCase      =						[
        gaussian_distribution(user_means[j]			,   _a			,   counts[j]  )
        for j in range(_a  )
    ]
    print('''Generated Normal Distribution: \n'''			,   _a  )
    print('''-''' * 1_0_0  )

    # Generating Ys to detecting corresponding classes
    _lowerCamelCase      =						y_generator(_a			,   _a  )
    print('''Generated Corresponding Ys: \n'''			,   _a  )
    print('''-''' * 1_0_0  )

    # Calculating the value of actual mean for each class
    _lowerCamelCase      =						[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('''-''' * 1_0_0  )

    # Calculating the value of probabilities for each class
    _lowerCamelCase      =						[
        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('''-''' * 1_0_0  )

    # Calculating the values of variance for each class
    _lowerCamelCase      =						calculate_variance(_a			,   _a			,   sum(_a  )  )
    print('''Variance: '''			,   _a  )
    print('''-''' * 1_0_0  )

    # Predicting Y values
    # storing predicted Y values in 'pre_indexes' variable
    _lowerCamelCase      =						predict_y_values(_a			,   _a			,   _a			,   _a  )
    print('''-''' * 1_0_0  )

    # Calculating Accuracy of the model
    print(F'''Accuracy: {accuracy(_a			,   _a  )}'''  )
    print('''-''' * 1_0_0  )
    print(''' DONE '''.center(1_0_0			,   '''+'''  )  )

    if input('''Press any key to restart or \'q\' for quit: '''  ).strip().lower() == "q":
      print('''\n''' + '''GoodBye!'''.center(1_0_0			,   '''-'''  ) + '''\n'''  )
      break
    system('''cls''' if name == '''nt''' else '''clear'''  )  # noqa: S605


if __name__ == "__main__":
  main()






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  _UpperCamelCase						 = ["image_processor", "tokenizer"]
  _UpperCamelCase						 = "AutoImageProcessor"
  _UpperCamelCase						 = "AutoTokenizer"


  def __init__(					self	,		a__=None	,		a__=None	,		**a__		):
    _lowerCamelCase      =						None
    if "feature_extractor" in kwargs:
      warnings.warn(
          '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
          ''' instead.'''	,		a__	,		)
      _lowerCamelCase      =						kwargs.pop('''feature_extractor'''		)

    _lowerCamelCase      =						image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError('''You need to specify an `image_processor`.'''		)
    if tokenizer is None:
      raise ValueError('''You need to specify a `tokenizer`.'''		)

    super().__init__(a__	,		a__		)
    _lowerCamelCase      =						self.image_processor
    _lowerCamelCase      =						False


  def __call__(					self	,		*a__	,		**a__		):
    # For backward compatibility
    if self._in_target_context_manager:
      return self.current_processor(*a__	,		**a__		)

    _lowerCamelCase      =						kwargs.pop('''images'''	,		a__		)
    _lowerCamelCase      =						kwargs.pop('''text'''	,		a__		)
    if len(a__		) > 0:
      _lowerCamelCase      =						args[0]
      _lowerCamelCase      =						args[1:]

    if images is None and text is None:
      raise ValueError('''You need to specify either an `images` or `text` input to process.'''		)

    if images is not None:
      _lowerCamelCase      =						self.image_processor(a__	,		*a__	,		**a__		)
    if text is not None:
      _lowerCamelCase      =						self.tokenizer(a__	,		**a__		)

    if text is None:
      return inputs
    elif images is None:
      return encodings
    else:
      _lowerCamelCase      =						encodings['''input_ids''']
      return inputs


  def     _UpperCAmelCase  (					self	,		*a__	,		**a__		):
    return self.tokenizer.batch_decode(*a__	,		**a__		)


  def     _UpperCAmelCase  (					self	,		*a__	,		**a__		):
    return self.tokenizer.decode(*a__	,		**a__		)


  @contextmanager
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
        '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
        '''your images inputs, or in a separate call.'''		)
    _lowerCamelCase      =						True
    _lowerCamelCase      =						self.tokenizer
    yield
    _lowerCamelCase      =						self.image_processor
    _lowerCamelCase      =						False


  @property
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''	,		a__	,		)
    return self.image_processor_class


  @property
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''	,		a__	,		)
    return self.image_processor




