

'''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 			__magic_name__				(       A    ,      A    ,      A							)	->						list:
	seed(1							)
	return [gauss(lowercase_    ,      lowercase_							) for _ in range(lowercase_							)]

def 			__magic_name__				(       A    ,      A							)	->						list:
	return [k for k in range(lowercase_							) for _ in range(instance_count[k]							)]

def 			__magic_name__				(       A    ,      A							)	->						float:
	return sum(lowercase_							) / instance_count

def 			__magic_name__				(       A    ,      A							)	->						float:
	return instance_count / total_count

def 			__magic_name__				(       A    ,      A    ,      A							)	->						float:
	snake_case							       = []  # 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
	snake_case							       = len(lowercase_							)  # Number of classes in dataset
	return 1 / (total_count - n_classes) * sum(lowercase_							)

def 			__magic_name__				(       A    ,      A    ,      A    ,      A							)	->						list:
	snake_case							       = []
	# 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]							)							):
			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(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 			__magic_name__				(       A    ,      A							)	->						float:
	snake_case							       = 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_							)) * 1_0_0


lowerCAmelCase_        =	TypeVar("num")

def 			__magic_name__				(       A    ,      A    ,      A    ,      A = lambda A							: True    ,      A = None    ,      )	->						num:
	while True:
		try:
			snake_case							       = 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 			__magic_name__				(       )	->						Any:
	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
		snake_case							       = valid_input(
		    input_type=lowercase_    ,      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
		snake_case							       = valid_input(
		    input_type=lowercase_    ,      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
		snake_case							       = []  # An empty list to store instance counts of classes in dataset
		for i in range(lowercase_							):
			snake_case							       = valid_input(
			    input_type=lowercase_    ,      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(lowercase_							)
		print('-' * 1_0_0							)

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

		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('-' * 1_0_0							)

		# 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('-' * 1_0_0							)

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

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

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

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

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

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

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










"""simple docstring"""
import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__SCREAMING_SNAKE_CASE     :  Dict										=					logging.getLogger(__name__)



def 				lowerCAmelCase_(      )		->				Optional[int]:
				_lowerCamelCase					     = argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''							)
				parser.add_argument('''--model_name'''  ,      type=lowercase_  ,      default='''transfo-xl-wt103'''  ,      help='''pretrained model name'''							)
				parser.add_argument(
				    '''--split'''  ,      type=lowercase_  ,      default='''test'''  ,      choices=['''all''', '''valid''', '''test''']  ,      help='''which split to evaluate'''							)
				parser.add_argument('''--batch_size'''  ,      type=lowercase_  ,      default=10  ,      help='''batch size'''							)
				parser.add_argument('''--tgt_len'''  ,      type=lowercase_  ,      default=1_28  ,      help='''number of tokens to predict'''							)
				parser.add_argument('''--ext_len'''  ,      type=lowercase_  ,      default=0  ,      help='''length of the extended context'''							)
				parser.add_argument('''--mem_len'''  ,      type=lowercase_  ,      default=16_00  ,      help='''length of the retained previous heads'''							)
				parser.add_argument('''--clamp_len'''  ,      type=lowercase_  ,      default=10_00  ,      help='''max positional embedding index'''							)
				parser.add_argument('''--no_cuda'''  ,      action='''store_true'''  ,      help='''Do not use CUDA even though CUA is available'''							)
				parser.add_argument('''--work_dir'''  ,      type=lowercase_  ,      required=lowercase_  ,      help='''path to the work_dir'''							)
				parser.add_argument('''--no_log'''  ,      action='''store_true'''  ,      help='''do not log the eval result'''							)
				parser.add_argument('''--same_length'''  ,      action='''store_true'''  ,      help='''set same length attention with masking'''							)
				parser.add_argument('''--server_ip'''  ,      type=lowercase_  ,      default=''''''  ,      help='''Can be used for distant debugging.'''							)
				parser.add_argument('''--server_port'''  ,      type=lowercase_  ,      default=''''''  ,      help='''Can be used for distant debugging.'''							)
				_lowerCamelCase					     = parser.parse_args()
				assert args.ext_len >= 0, "extended context length must be non-negative"

				if args.server_ip and args.server_port:
								# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
								import ptvsd

								print('''Waiting for debugger attach'''							)
								ptvsd.enable_attach(address=(args.server_ip, args.server_port)  ,      redirect_output=lowercase_							)
								ptvsd.wait_for_attach()

				_lowerCamelCase					     = torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
				logger.info('''device: {}'''.format(lowercase_							)							)

				# Load a pre-processed dataset
				# You can also build the corpus yourself using TransfoXLCorpus methods
				# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
				# and tokenizing the dataset
				# The pre-processed corpus is a convertion (using the conversion script )
				_lowerCamelCase					     = TransfoXLCorpus.from_pretrained(args.model_name							)

				_lowerCamelCase					     = corpus.get_iterator('''valid'''  ,      args.batch_size  ,      args.tgt_len  ,      device=lowercase_  ,      ext_len=args.ext_len							)
				_lowerCamelCase					     = corpus.get_iterator('''test'''  ,      args.batch_size  ,      args.tgt_len  ,      device=lowercase_  ,      ext_len=args.ext_len							)

				# Load a pre-trained model
				_lowerCamelCase					     = TransfoXLLMHeadModel.from_pretrained(args.model_name							)
				model.to(lowercase_							)

				logger.info(
				    '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
				        args.batch_size  ,      args.tgt_len  ,      args.ext_len  ,      args.mem_len  ,      args.clamp_len							)							)

				model.reset_memory_length(args.mem_len							)
				if args.clamp_len > 0:
								_lowerCamelCase					     = args.clamp_len
				if args.same_length:
								_lowerCamelCase					     = True

				###############################################################################
				# Evaluation code
				###############################################################################
				def evaluate(lowercase_   :							Any							):
								# Turn on evaluation mode which disables dropout.
								model.eval()
								_lowerCamelCase     ,   _lowerCamelCase					     = 0, 0.0
								_lowerCamelCase					     = time.time()
								with torch.no_grad():
												_lowerCamelCase					     = None
												for idx, (data, target, seq_len) in enumerate(lowercase_							):
																_lowerCamelCase					     = model(lowercase_  ,      lm_labels=lowercase_  ,      mems=lowercase_							)
																_lowerCamelCase     ,   _lowerCamelCase     ,   _lowerCamelCase					     = ret
																_lowerCamelCase					     = loss.mean()
																total_loss += seq_len * loss.item()
																total_len += seq_len
												_lowerCamelCase					     = time.time() - start_time
								logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(lowercase_  ,      10_00 * total_time / (idx + 1)							)							)
								return total_loss / total_len

				# Run on test data.
				if args.split == "all":
								_lowerCamelCase					     = evaluate(lowercase_							)
								_lowerCamelCase					     = evaluate(lowercase_							)
				elif args.split == "valid":
								_lowerCamelCase					     = evaluate(lowercase_							)
								_lowerCamelCase					     = None
				elif args.split == "test":
								_lowerCamelCase					     = evaluate(lowercase_							)
								_lowerCamelCase					     = None

				def format_log(lowercase_   :							Optional[Any]  ,      lowercase_   :							Optional[Any]							):
								_lowerCamelCase					     = '''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(lowercase_  ,      lowercase_  ,      math.exp(lowercase_							)							)
								return log_str

				_lowerCamelCase					     = ''''''
				if valid_loss is not None:
								log_str += format_log(lowercase_  ,      '''valid'''							)
				if test_loss is not None:
								log_str += format_log(lowercase_  ,      '''test'''							)

				logger.info('''=''' * 1_00							)
				logger.info(lowercase_							)
				logger.info('''=''' * 1_00							)


if __name__ == "__main__":
			main()
