



'''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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		seed(1     )
		return [gauss(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ) for _ in range(SCREAMING_SNAKE_CASE     )]







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		return [k for k in range(SCREAMING_SNAKE_CASE     ) for _ in range(instance_count[k]     )]







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		return sum(SCREAMING_SNAKE_CASE     ) / instance_count







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		return instance_count / total_count







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Tuple										=      []  # An empty list to store all squared differences
		# iterate over number of elements in items
		for i in range(len(SCREAMING_SNAKE_CASE     )     ):
				# 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
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      len(SCREAMING_SNAKE_CASE     )  # Number of classes in dataset
		return 1 / (total_count - n_classes) * sum(SCREAMING_SNAKE_CASE     )







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      []
		# for loop iterates over number of elements in list
		for i in range(len(SCREAMING_SNAKE_CASE     )     ):
				# for loop iterates over number of inner items of each element
				for j in range(len(x_items[i]     )     ):
						SCREAMING_SNAKE_CASE_						:int										=      []  # 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(SCREAMING_SNAKE_CASE     )     ):
								# 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(SCREAMING_SNAKE_CASE     )

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







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Any										=      sum(1 for i, j in zip(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ) 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(SCREAMING_SNAKE_CASE     )) * 100


SCREAMING_SNAKE_CASE__     : List[Any]				=   TypeVar("""num""")







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

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

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

				# Generating training dataset drawn from gaussian distribution
				SCREAMING_SNAKE_CASE_						:Any										=      [
				    gaussian_distribution(user_means[j] ,		SCREAMING_SNAKE_CASE ,		counts[j]     )
				    for j in range(SCREAMING_SNAKE_CASE     )
				]
				print('Generated Normal Distribution: \n' ,		SCREAMING_SNAKE_CASE     )
				print('-' * 100     )

				# Generating Ys to detecting corresponding classes
				SCREAMING_SNAKE_CASE_						:str										=      y_generator(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
				print('Generated Corresponding Ys: \n' ,		SCREAMING_SNAKE_CASE     )
				print('-' * 100     )

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

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

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

				# Calculating the values of variance for each class
				SCREAMING_SNAKE_CASE_						:List[str]										=      calculate_variance(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		sum(SCREAMING_SNAKE_CASE     )     )
				print('Variance: ' ,		SCREAMING_SNAKE_CASE     )
				print('-' * 100     )

				# Predicting Y values
				# storing predicted Y values in 'pre_indexes' variable
				SCREAMING_SNAKE_CASE_						:Any										=      predict_y_values(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
				print('-' * 100     )

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



"""simple docstring"""
from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A									=						logging.get_logger(__name__)

A									=						{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    'prophetnet'
  snake_case_        =    ['past_key_values']
  snake_case_        =    {
      'num_attention_heads': 'num_encoder_attention_heads',
  }



  def __init__( self					: Union[str, Any]       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[Union[str, Callable]] = "gelu"       ,       snake_case					: Optional[int] = 3_0522       ,       snake_case					: Optional[int] = 1024       ,       snake_case					: Optional[int] = 4096       ,       snake_case					: Optional[int] = 12       ,       snake_case					: Optional[int] = 16       ,       snake_case					: Optional[int] = 4096       ,       snake_case					: Optional[int] = 12       ,       snake_case					: Optional[int] = 16       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[int] = 512       ,       snake_case					: Optional[float] = 0.02       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[int] = 0       ,       snake_case					: Optional[int] = 2       ,       snake_case					: Optional[int] = 32       ,       snake_case					: Optional[int] = 128       ,       snake_case					: Optional[bool] = False       ,       snake_case					: Optional[float] = 0.0       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[int] = 0       ,       snake_case					: Optional[int] = 1       ,       snake_case					: Optional[int] = 2       ,       **snake_case					: Optional[int]       ,       )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     vocab_size
         __magic_name__			:		Tuple        =     hidden_size
         __magic_name__			:		str        =     encoder_ffn_dim
         __magic_name__			:		Tuple        =     num_encoder_layers
         __magic_name__			:		int        =     num_encoder_attention_heads
         __magic_name__			:		int        =     decoder_ffn_dim
         __magic_name__			:		Optional[int]        =     num_decoder_layers
         __magic_name__			:		str        =     num_decoder_attention_heads
         __magic_name__			:		Optional[Any]        =     max_position_embeddings
         __magic_name__			:		Union[str, Any]        =     init_std  # Normal(0, this parameter)
         __magic_name__			:		Tuple        =     activation_function

         # parameters for prophetnet
         __magic_name__			:		int        =     ngram
         __magic_name__			:		str        =     num_buckets
         __magic_name__			:		Any        =     relative_max_distance
         __magic_name__			:		Optional[int]        =     disable_ngram_loss
         __magic_name__			:		str        =     eps

         # 3 Types of Dropout
         __magic_name__			:		Dict        =     attention_dropout
         __magic_name__			:		Optional[int]        =     activation_dropout
         __magic_name__			:		List[Any]        =     dropout

         __magic_name__			:		str        =     use_cache

         super().__init__(
             pad_token_id=snake_case       ,       bos_token_id=snake_case       ,       eos_token_id=snake_case       ,       is_encoder_decoder=snake_case       ,       add_cross_attention=snake_case       ,       decoder_start_token_id=snake_case       ,       **snake_case       ,       )



  @property
  def 							_UpperCAmelCase      ( self					: str      )	->			int:


         '''simple docstring'''





         return self.num_encoder_layers + self.num_decoder_layers




  @num_hidden_layers.setter
  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: int      )	->			Optional[int]:


         '''simple docstring'''





         raise NotImplementedError(
             '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
             ''' `num_decoder_layers`.'''      )
