



"""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 				lowerCAmelCase_(      lowercase_   :							float  ,      lowercase_   :							float  ,      lowercase_   :							int							)		->				list:
				seed(1							)
				return [gauss(lowercase_  ,      lowercase_							) for _ in range(lowercase_							)]



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



def 				lowerCAmelCase_(      lowercase_   :							int  ,      lowercase_   :							list							)		->				float:
				return sum(lowercase_							) / instance_count



def 				lowerCAmelCase_(      lowercase_   :							int  ,      lowercase_   :							int							)		->				float:
				return instance_count / total_count



def 				lowerCAmelCase_(      lowercase_   :							list  ,      lowercase_   :							list  ,      lowercase_   :							int							)		->				float:
				_lowerCamelCase					     = []  # 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
				_lowerCamelCase					     = len(lowercase_							)  # Number of classes in dataset
				return 1 / (total_count - n_classes) * sum(lowercase_							)



def 				lowerCAmelCase_(      lowercase_   :							list  ,      lowercase_   :							list  ,      lowercase_   :							float  ,      lowercase_   :							list							)		->				list:
				_lowerCamelCase					     = []
				# 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]							)							):
												_lowerCamelCase					     = []  # 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 				lowerCAmelCase_(      lowercase_   :							list  ,      lowercase_   :							list							)		->				float:
				_lowerCamelCase					     = 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_00


__SCREAMING_SNAKE_CASE     :  Tuple										=					TypeVar('''num''')



def 				lowerCAmelCase_(      lowercase_   :							Callable[[object], num]  ,      lowercase_   :							str  ,      lowercase_   :							str  ,      lowercase_   :							Callable[[num], bool] = lambda lowercase_							: True  ,      lowercase_   :							str | None = None  ,      )		->				num:
				while True:
								try:
												_lowerCamelCase					     = 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 				lowerCAmelCase_(      )		->				Any:
				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
								_lowerCamelCase					     = 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('''-''' * 1_00							)

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

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

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

								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_00							)

								# 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_00							)

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

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

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

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

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

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

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





"""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()
