

"""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		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					seed(1 )
					return [gauss(lowercase__     ,				lowercase__ ) for _ in range(lowercase__ )]
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					return [k for k in range(lowercase__ ) for _ in range(instance_count[k] )]
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					return sum(lowercase__ ) / instance_count
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					return instance_count / total_count
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   []  # An empty list to store all squared differences
					# iterate over number of elements in items
					for i in range(len(lowercase__ ) ):
										# 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     =   len(lowercase__ )  # Number of classes in dataset
					return 1 / (total_count - n_classes) * sum(lowercase__ )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   []
					# for loop iterates over number of elements in list
					for i in range(len(lowercase__ ) ):
										# for loop iterates over number of inner items of each element
										for j in range(len(x_items[i] ) ):
															A     =   []  # 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(lowercase__ ) ):
																				# 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(lowercase__ )

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


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

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

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

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

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

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

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

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

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

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

										# Calculating Accuracy of the model
										print(F"""Accuracy: {accuracy(lowercase__     ,				lowercase__ )}""" )
										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"""







import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin
class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE					,       unittest.TestCase			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   SpeechaTextaTokenizer
							SCREAMING_SNAKE_CASE              =   False






							def        lowerCAmelCase  (    self			:  Dict					):
													super().setUp()

													UpperCAmelCase__												=     '''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''					)
													UpperCAmelCase__												=     ['''he re</w> 123''', '''here a 1456''']
													UpperCAmelCase__												=     dict(zip(lowerCamelCase_		,			range(len(lowerCamelCase_					)					)					)					)

													UpperCAmelCase__												=     {'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

													UpperCAmelCase__												=     tempfile.mkdtemp()
													UpperCAmelCase__												=     os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['''vocab_file''']					)
													UpperCAmelCase__												=     os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['''merges_file''']					)

													with open(self.vocab_file		,			'''w'''		,			encoding='''utf-8'''					) as fp:
																			fp.write(json.dumps(lowerCamelCase_					) + '''\n'''					)

													with open(self.merges_file		,			'''w'''					) as fp:
																			fp.write('''\n'''.join(lowerCamelCase_					)					)






							def        lowerCAmelCase  (    self			:  Union[str, Any]					):
													UpperCAmelCase__												=     list(self.get_tokenizer().get_vocab().keys()					)

													self.assertEqual(vocab_keys[0]		,			'''<s>'''					)
													self.assertEqual(vocab_keys[1]		,			'''<pad>'''					)
													self.assertEqual(vocab_keys[-1]		,			'''vocab'''					)
													self.assertEqual(len(lowerCamelCase_					)		,			14					)






							def        lowerCAmelCase  (    self			:  Optional[Any]					):
													self.assertEqual(self.get_tokenizer().vocab_size		,			14					)






							def        lowerCAmelCase  (    self			:  Any					):
													UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

													# make sure @@ is correctly concatenated
													UpperCAmelCase__												=     [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
													UpperCAmelCase__												=     tokenizer.decode(lowerCamelCase_					)

													self.assertTrue(output_string == '''herecouple words ofthe'''					)






							def        lowerCAmelCase  (    self			:  int					):
													UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

													with tempfile.TemporaryDirectory() as tmp_dirname:
																			tokenizer.save_pretrained(lowerCamelCase_					)
																			os.remove(os.path.join(lowerCamelCase_		,			'''merges.txt'''					)					)

																			# load tokenizer without merges file should not throw an error
																			UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(lowerCamelCase_					)

													with tempfile.TemporaryDirectory() as tmp_dirname:
																			# save tokenizer and load again
																			tokenizer.save_pretrained(lowerCamelCase_					)
																			UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(lowerCamelCase_					)

													self.assertIsNotNone(lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  int					):
													if not self.test_slow_tokenizer:
																			return

													UpperCAmelCase__												=     inspect.signature(self.tokenizer_class.__init__					)
													UpperCAmelCase__												=     self.get_tokenizer()

													for parameter_name, parameter in signature.parameters.items():
																			if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																									self.assertIn(lowerCamelCase_		,			tokenizer.init_kwargs					)