



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		( _UpperCAmelCase				:   Tuple					,  _UpperCAmelCase				:   Tuple					,  _UpperCAmelCase				:   Optional[int]		):







		'''simple docstring'''





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

def 			a		( _UpperCAmelCase				:   Any					,  _UpperCAmelCase				:   List[Any]		):







		'''simple docstring'''





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

def 			a		( _UpperCAmelCase				:   Tuple					,  _UpperCAmelCase				:   int		):







		'''simple docstring'''





		return sum(_UpperCAmelCase		) / instance_count

def 			a		( _UpperCAmelCase				:   Tuple					,  _UpperCAmelCase				:   int		):







		'''simple docstring'''





		return instance_count / total_count

def 			a		( _UpperCAmelCase				:   List[str]					,  _UpperCAmelCase				:   List[Any]					,  _UpperCAmelCase				:   int		):







		'''simple docstring'''





		__UpperCAmelCase       :	Optional[int]						=      []  # 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
		__UpperCAmelCase       :	Optional[int]						=      len(_UpperCAmelCase		)  # Number of classes in dataset
		return 1 / (total_count - n_classes) * sum(_UpperCAmelCase		)

def 			a		( _UpperCAmelCase				:   str					,  _UpperCAmelCase				:   Optional[Any]					,  _UpperCAmelCase				:   Optional[int]					,  _UpperCAmelCase				:   Optional[int]		):







		'''simple docstring'''





		__UpperCAmelCase       :	Optional[Any]						=      []
		# 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]		)		):
						__UpperCAmelCase       :	str						=      []  # 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 			a		( _UpperCAmelCase				:   int					,  _UpperCAmelCase				:   str		):







		'''simple docstring'''





		__UpperCAmelCase       :	Dict						=      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		)) * 1_00


__A       =TypeVar("num")

def 			a		( _UpperCAmelCase				:   List[Any]					,  _UpperCAmelCase				:   List[str]					,  _UpperCAmelCase				:   Any					,  _UpperCAmelCase				:   Any = lambda _UpperCAmelCase		: True					,  _UpperCAmelCase				:   List[Any] = None					,  ):







		'''simple docstring'''





		while True:
				try:
						__UpperCAmelCase       :	Dict						=      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 			a		( ):







		'''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
				__UpperCAmelCase       :	List[str]						=      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('''-''' * 1_00		)

				# Trying to get the value of standard deviation
				__UpperCAmelCase       :	Dict						=      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('''-''' * 1_00		)

				# Trying to get number of instances in classes and theirs means to generate
				# dataset
				__UpperCAmelCase       :	int						=      []  # An empty list to store instance counts of classes in dataset
				for i in range(_UpperCAmelCase		):
						__UpperCAmelCase       :	List[Any]						=      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('''-''' * 1_00		)

				# An empty list to store values of user-entered means of classes
				__UpperCAmelCase       :	int						=      []
				for a in range(_UpperCAmelCase		):
						__UpperCAmelCase       :	Union[str, Any]						=      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('''-''' * 1_00		)

				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('''-''' * 1_00		)

				# 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('''-''' * 1_00		)

				# Generating training dataset drawn from gaussian distribution
				__UpperCAmelCase       :	List[Any]						=      [
				    gaussian_distribution(user_means[j]					,  _UpperCAmelCase					,  counts[j]		)
				    for j in range(_UpperCAmelCase		)
				]
				print('''Generated Normal Distribution: \n'''					,  _UpperCAmelCase		)
				print('''-''' * 1_00		)

				# Generating Ys to detecting corresponding classes
				__UpperCAmelCase       :	Optional[Any]						=      y_generator(_UpperCAmelCase					,  _UpperCAmelCase		)
				print('''Generated Corresponding Ys: \n'''					,  _UpperCAmelCase		)
				print('''-''' * 1_00		)

				# Calculating the value of actual mean for each class
				__UpperCAmelCase       :	List[str]						=      [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('''-''' * 1_00		)

				# Calculating the value of probabilities for each class
				__UpperCAmelCase       :	Optional[int]						=      [
				    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('''-''' * 1_00		)

				# Calculating the values of variance for each class
				__UpperCAmelCase       :	Optional[Any]						=      calculate_variance(_UpperCAmelCase					,  _UpperCAmelCase					,  sum(_UpperCAmelCase		)		)
				print('''Variance: '''					,  _UpperCAmelCase		)
				print('''-''' * 1_00		)

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

				# Calculating Accuracy of the model
				print(f'Accuracy: {accuracy(_UpperCAmelCase					,  _UpperCAmelCase		)}'		)
				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()









from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase     =       logging.get_logger(__name__)







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 __snake_case				=    ["input_ids", "attention_mask"]



 def __init__(      self						, _UpperCAmelCase="[PAD]"						, _UpperCAmelCase="[BOS]"						, _UpperCAmelCase="[EOS]"						, _UpperCAmelCase="[MASK]"						, _UpperCAmelCase="[CLS]"						, _UpperCAmelCase="[SEP]"						, _UpperCAmelCase=20_48						, **_UpperCAmelCase						, ):
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else pad_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else bos_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else eos_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else mask_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else cls_token
       snake_case_			= AddedToken(_UpperCAmelCase						, lstrip=_UpperCAmelCase						, rstrip=_UpperCAmelCase   ) if isinstance(_UpperCAmelCase						, _UpperCAmelCase   ) else sep_token

       super().__init__(
           pad_token=_UpperCAmelCase						, bos_token=_UpperCAmelCase						, eos_token=_UpperCAmelCase						, mask_token=_UpperCAmelCase						, cls_token=_UpperCAmelCase						, sep_token=_UpperCAmelCase						, model_max_length=_UpperCAmelCase						, **_UpperCAmelCase						, )

       snake_case_			= 2**8  # utf is 8 bits

       # define special tokens dict
       snake_case_			= {
           self.pad_token: 0,
           self.bos_token: 1,
           self.eos_token: 2,
           self.mask_token: 3,
           self.cls_token: 4,
           self.sep_token: 5,
       }
       snake_case_			= len(self.special_tokens_encoder   )
       snake_case_			= {v: k for k, v in self.special_tokens_encoder.items()}



 def 		UpperCamelCase__	(      self   ):
       snake_case_			= self.special_tokens_encoder.copy()
       vocab.update(self.added_tokens_encoder   )
       for i in range(self._utf_vocab_size   ):
             snake_case_			= chr(_UpperCAmelCase   )
             snake_case_			= i + len(self.special_tokens_encoder   )
       return vocab



 @property
 def 		UpperCamelCase__	(      self   ):
       return self._utf_vocab_size + self._num_special_tokens



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None						, _UpperCAmelCase = False   ):
       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=_UpperCAmelCase						, token_ids_a=_UpperCAmelCase						, already_has_special_tokens=_UpperCAmelCase   )

       # normal case: some special tokens
       if token_ids_a is None:
             return [1] + [0] * len(_UpperCAmelCase   ) + [1]
       return [1] + ([0] * len(_UpperCAmelCase   )) + [1] + ([0] * len(_UpperCAmelCase   )) + [1]



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None   ):
       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       else:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       snake_case_			= [chr(_UpperCAmelCase   ) for i in text.encode('''utf-8'''   )]
       return tokens



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       if token in self.special_tokens_encoder:
             snake_case_			= self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
             snake_case_			= self.added_tokens_encoder[token]
       elif len(_UpperCAmelCase   ) != 1:
             snake_case_			= self.unk_token_id
       else:
             snake_case_			= ord(_UpperCAmelCase   ) + self._num_special_tokens
       return token_id



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       if index in self.special_tokens_decoder:
             snake_case_			= self.special_tokens_decoder[index]
       elif index in self.added_tokens_decoder:
             snake_case_			= self.added_tokens_decoder[index]
       else:
             snake_case_			= chr(index - self._num_special_tokens   )
       return token



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       snake_case_			= b''''''
       for token in tokens:
             if token in self.special_tokens_decoder:
                   snake_case_			= self.special_tokens_decoder[token].encode('''utf-8'''   )
             elif token in self.added_tokens_decoder:
                   snake_case_			= self.special_tokens_decoder[token].encode('''utf-8'''   )
             elif token in self.special_tokens_encoder:
                   snake_case_			= token.encode('''utf-8'''   )
             elif token in self.added_tokens_encoder:
                   snake_case_			= token.encode('''utf-8'''   )
             else:
                   snake_case_			= bytes([ord(_UpperCAmelCase   )]   )
             bstring += tok_string
       snake_case_			= bstring.decode('''utf-8'''						, errors='''replace'''   )
       return string





 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = None   ):
       return ()