






import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast





def 						__UpperCAmelCase      (					a_							,					a_=1_00							,					a_=" "):
							snake_case_           =							text.split(a_)
							return [character.join(text[i : i + n]).strip() for i in range(0							,					len(a_)							,					a_)]





def 						__UpperCAmelCase      (					a_):
							snake_case_      ,		snake_case_           =							[], []
							for title, text in zip(documents['title']							,					documents['text']):
														if text is not None:
																					for passage in split_text(a_):
																												titles.append(title if title is not None else '')
																												texts.append(a_)
							return {"title": titles, "text": texts}





def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_							,					a_							,					a_):
							snake_case_           =							load_dataset(
							    'csv'							,					data_files=[csv_path]							,					split='train'							,					delimiter='\t'							,					column_names=['title', 'text'])
							snake_case_           =							kb_dataset.map(
							    a_							,					batched=a_							,					num_proc=1)  # if you want you can load already splitted csv.
							snake_case_           =							[kb_dataset.shard(a_							,					a_							,					contiguous=a_) for i in range(a_)]
							snake_case_           =							kb_list[process_num]

							snake_case_           =							'data_' + str(a_)
							snake_case_           =							os.path.join(a_							,					a_)

							snake_case_           =							DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base')
							snake_case_           =							ctx_encoder.to(device=a_)

							def embed(
							    a_							,					a_							,					a_							,					a_) -> dict:
														snake_case_           =							ctx_tokenizer(
														    documents['title']							,					documents['text']							,					truncation=a_							,					padding='longest'							,					return_tensors='pt')['input_ids']
														snake_case_           =							ctx_encoder(input_ids.to(device=a_)							,					return_dict=a_).pooler_output
														return {"embeddings": embeddings.detach().cpu().numpy()}

							snake_case_           =							Features(
							    {'text': Value('string'), 'title': Value('string'), 'embeddings': Sequence(Value('float32'))})  # optional, save as float32 instead of float64 to save space

							snake_case_           =							data_shrad.map(
							    partial(a_							,					ctx_encoder=a_							,					ctx_tokenizer=a_							,					device=a_)							,					batched=a_							,					batch_size=16							,					features=a_							,					)
							dataset.save_to_disk(a_)





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							[]

							for shard_address in glob(str(a_) + '/*/'):
														data_shard_list.append(load_from_disk(a_))

							snake_case_           =							concatenate_datasets(a_)
							faiss.omp_set_num_threads(96)

							snake_case_           =							faiss.IndexHNSWFlat(7_68							,					1_28							,					faiss.METRIC_INNER_PRODUCT)
							concat.add_faiss_index('embeddings'							,					custom_index=a_)
							concat.get_index('embeddings').save(
							    a_)  # since we load the index in to memory,we can directly update the index in the disk













from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar





def 						__UpperCAmelCase      (					a_							,					a_							,					a_):
							seed(1)
							return [gauss(a_							,					a_) for _ in range(a_)]





def 						__UpperCAmelCase      (					a_							,					a_):
							return [k for k in range(a_) for _ in range(instance_count[k])]





def 						__UpperCAmelCase      (					a_							,					a_):
							return sum(a_) / instance_count





def 						__UpperCAmelCase      (					a_							,					a_):
							return instance_count / total_count





def 						__UpperCAmelCase      (					a_							,					a_							,					a_):
							snake_case_           =							[]  # 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
							snake_case_           =							len(a_)  # Number of classes in dataset
							return 1 / (total_count - n_classes) * sum(a_)





def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_):
							snake_case_           =							[]
							# 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])):
																					snake_case_           =							[]  # 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 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							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_00


lowercase  							= TypeVar("num")





def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_ = lambda a_: True							,					a_ = None							,					):
							while True:
														try:
																					snake_case_           =							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 						__UpperCAmelCase      (					):
							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
														snake_case_           =							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_00)

														# Trying to get the value of standard deviation
														snake_case_           =							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_00)

														# Trying to get number of instances in classes and theirs means to generate
														# dataset
														snake_case_           =							[]  # An empty list to store instance counts of classes in dataset
														for i in range(a_):
																					snake_case_           =							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_00)

														# An empty list to store values of user-entered means of classes
														snake_case_           =							[]
														for a in range(a_):
																					snake_case_           =							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_00)

														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_00)

														# 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_00)

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

														# Generating Ys to detecting corresponding classes
														snake_case_           =							y_generator(a_							,					a_)
														print('Generated Corresponding Ys: \n'							,					a_)
														print('-' * 1_00)

														# Calculating the value of actual mean for each class
														snake_case_           =							[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_00)

														# Calculating the value of probabilities for each class
														snake_case_           =							[
														    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_00)

														# Calculating the values of variance for each class
														snake_case_           =							calculate_variance(a_							,					a_							,					sum(a_))
														print('Variance: '							,					a_)
														print('-' * 1_00)

														# Predicting Y values
														# storing predicted Y values in 'pre_indexes' variable
														snake_case_           =							predict_y_values(a_							,					a_							,					a_							,					a_)
														print('-' * 1_00)

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





