


'''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 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							seed(1   )
							return [gauss(UpperCamelCase_	,     UpperCamelCase_   ) for _ in range(UpperCamelCase_   )]







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







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







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







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







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		[]
							# for loop iterates over number of elements in list
							for i in range(len(UpperCamelCase_   )   ):
														# 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(UpperCamelCase_   )   ):
																												# 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(UpperCamelCase_   )

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







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		sum(1 for i, j in zip(UpperCamelCase_	,     UpperCamelCase_   ) 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(UpperCamelCase_   )) * 1_0_0


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







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_ = lambda UpperCamelCase_   : True	,     UpperCamelCase_ = None	,     ):
							while True:
														try:
																					A_ 						=		input_type(input(UpperCamelCase_   ).strip() or default   )
																					if condition(UpperCamelCase_   ):
																												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 			a_							(  ):
							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
														A_ 						=		valid_input(
														    input_type=UpperCamelCase_	,     condition=lambda UpperCamelCase_   : 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
														A_ 						=		valid_input(
														    input_type=UpperCamelCase_	,     condition=lambda UpperCamelCase_   : 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
														A_ 						=		[]  # An empty list to store instance counts of classes in dataset
														for i in range(UpperCamelCase_   ):
																					A_ 						=		valid_input(
																					    input_type=UpperCamelCase_	,     condition=lambda UpperCamelCase_   : x > 0	,     input_msg=(f"Enter The number of instances for class_{i+1}: ")	,     err_msg="Number of instances should be positive!"	,     )
																					counts.append(UpperCamelCase_   )
														print("-" * 1_0_0   )

														# An empty list to store values of user-entered means of classes
														A_ 						=		[]
														for a in range(UpperCamelCase_   ):
																					A_ 						=		valid_input(
																					    input_type=UpperCamelCase_	,     input_msg=(f"Enter the value of mean for class_{a+1}: ")	,     err_msg="This is an invalid value."	,     )
																					user_means.append(UpperCamelCase_   )
														print("-" * 1_0_0   )

														print("Standard deviation: "	,     UpperCamelCase_   )
														# print out the number of instances in classes in separated line
														for i, count in enumerate(UpperCamelCase_	,     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(UpperCamelCase_	,     1   ):
																					print(f"Mean of class_{i} is: {user_mean}"   )
														print("-" * 1_0_0   )

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

														# Generating Ys to detecting corresponding classes
														A_ 						=		y_generator(UpperCamelCase_	,     UpperCamelCase_   )
														print("Generated Corresponding Ys: \n"	,     UpperCamelCase_   )
														print("-" * 1_0_0   )

														# Calculating the value of actual mean for each class
														A_ 						=		[calculate_mean(counts[k]	,     x[k]   ) for k in range(UpperCamelCase_   )]
														# for loop iterates over number of elements in 'actual_means' list and print
														# out them in separated line
														for i, actual_mean in enumerate(UpperCamelCase_	,     1   ):
																					print(f"Actual(Real) mean of class_{i} is: {actual_mean}"   )
														print("-" * 1_0_0   )

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

														# for loop iterates over number of elements in 'probabilities' list and print
														# out them in separated line
														for i, probability in enumerate(UpperCamelCase_	,     1   ):
																					print(f"Probability of class_{i} is: {probability}"   )
														print("-" * 1_0_0   )

														# Calculating the values of variance for each class
														A_ 						=		calculate_variance(UpperCamelCase_	,     UpperCamelCase_	,     sum(UpperCamelCase_   )   )
														print("Variance: "	,     UpperCamelCase_   )
														print("-" * 1_0_0   )

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

														# Calculating Accuracy of the model
														print(f"Accuracy: {accuracy(UpperCamelCase_	,     UpperCamelCase_   )}"   )
														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'''
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		'''\
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415},
}
'''

__SCREAMING_SNAKE_CASE				:							List[Any]		       =		'''\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
'''


__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		'''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]
    >>> sari = datasets.load_metric("sari")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 26.953601953601954}
'''







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		[rgram for rgrams in rgramslist for rgram in rgrams]
							A_ 						=		Counter(UpperCamelCase_   )

							A_ 						=		Counter(UpperCamelCase_   )
							A_ 						=		Counter()
							for sgram, scount in sgramcounter.items():
														A_ 						=		scount * numref

							A_ 						=		Counter(UpperCamelCase_   )
							A_ 						=		Counter()
							for cgram, ccount in cgramcounter.items():
														A_ 						=		ccount * numref

							# KEEP
							A_ 						=		sgramcounter_rep & cgramcounter_rep
							A_ 						=		keepgramcounter_rep & rgramcounter
							A_ 						=		sgramcounter_rep & rgramcounter

							A_ 						=		0
							A_ 						=		0
							for keepgram in keepgramcountergood_rep:
														keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
														# Fix an alleged bug [2] in the keep score computation.
														# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
														keeptmpscorea += keepgramcountergood_rep[keepgram]
							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							#      a target exactly.
							A_ 						=		1
							A_ 						=		1
							if len(UpperCamelCase_   ) > 0:
														A_ 						=		keeptmpscorea / len(UpperCamelCase_   )
							if len(UpperCamelCase_   ) > 0:
														# Fix an alleged bug [2] in the keep score computation.
														# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
														A_ 						=		keeptmpscorea / sum(keepgramcounterall_rep.values()   )
							A_ 						=		0
							if keepscore_precision > 0 or keepscore_recall > 0:
														A_ 						=		2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

							# DELETION
							A_ 						=		sgramcounter_rep - cgramcounter_rep
							A_ 						=		delgramcounter_rep - rgramcounter
							A_ 						=		sgramcounter_rep - rgramcounter
							A_ 						=		0
							A_ 						=		0
							for delgram in delgramcountergood_rep:
														deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
														deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							# a target exactly.
							A_ 						=		1
							if len(UpperCamelCase_   ) > 0:
														A_ 						=		deltmpscorea / len(UpperCamelCase_   )

							# ADDITION
							A_ 						=		set(UpperCamelCase_   ) - set(UpperCamelCase_   )
							A_ 						=		set(UpperCamelCase_   ) & set(UpperCamelCase_   )
							A_ 						=		set(UpperCamelCase_   ) - set(UpperCamelCase_   )

							A_ 						=		0
							for addgram in addgramcountergood:
														addtmpscore += 1

							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							# a target exactly.
							A_ 						=		1
							A_ 						=		1
							if len(UpperCamelCase_   ) > 0:
														A_ 						=		addtmpscore / len(UpperCamelCase_   )
							if len(UpperCamelCase_   ) > 0:
														A_ 						=		addtmpscore / len(UpperCamelCase_   )
							A_ 						=		0
							if addscore_precision > 0 or addscore_recall > 0:
														A_ 						=		2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

							return (keepscore, delscore_precision, addscore)







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		len(UpperCamelCase_   )

							A_ 						=		ssent.split(" "   )
							A_ 						=		csent.split(" "   )
							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]

							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]
							A_ 						=		[]
							for rsent in rsents:
														A_ 						=		rsent.split(" "   )
														A_ 						=		[]
														A_ 						=		[]
														A_ 						=		[]
														ragramslist.append(UpperCamelCase_   )
														for i in range(0	,     len(UpperCamelCase_   ) - 1   ):
																					if i < len(UpperCamelCase_   ) - 1:
																												A_ 						=		ragrams[i] + " " + ragrams[i + 1]
																												ragrams.append(UpperCamelCase_   )
																					if i < len(UpperCamelCase_   ) - 2:
																												A_ 						=		ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
																												ragrams.append(UpperCamelCase_   )
																					if i < len(UpperCamelCase_   ) - 3:
																												A_ 						=		ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
																												ragrams.append(UpperCamelCase_   )
														ragramslist.append(UpperCamelCase_   )
														ragramslist.append(UpperCamelCase_   )
														ragramslist.append(UpperCamelCase_   )

							for i in range(0	,     len(UpperCamelCase_   ) - 1   ):
														if i < len(UpperCamelCase_   ) - 1:
																					A_ 						=		sagrams[i] + " " + sagrams[i + 1]
																					sagrams.append(UpperCamelCase_   )
														if i < len(UpperCamelCase_   ) - 2:
																					A_ 						=		sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
																					sagrams.append(UpperCamelCase_   )
														if i < len(UpperCamelCase_   ) - 3:
																					A_ 						=		sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
																					sagrams.append(UpperCamelCase_   )

							for i in range(0	,     len(UpperCamelCase_   ) - 1   ):
														if i < len(UpperCamelCase_   ) - 1:
																					A_ 						=		cagrams[i] + " " + cagrams[i + 1]
																					cagrams.append(UpperCamelCase_   )
														if i < len(UpperCamelCase_   ) - 2:
																					A_ 						=		cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
																					cagrams.append(UpperCamelCase_   )
														if i < len(UpperCamelCase_   ) - 3:
																					A_ 						=		cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
																					cagrams.append(UpperCamelCase_   )

							((A_)						,				(A_)						,				(A_)) 						=		SARIngram(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
							((A_)						,				(A_)						,				(A_)) 						=		SARIngram(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
							((A_)						,				(A_)						,				(A_)) 						=		SARIngram(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
							((A_)						,				(A_)						,				(A_)) 						=		SARIngram(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )
							A_ 						=		sum([keepascore, keepascore, keepascore, keepascore]   ) / 4
							A_ 						=		sum([delascore, delascore, delascore, delascore]   ) / 4
							A_ 						=		sum([addascore, addascore, addascore, addascore]   ) / 4
							A_ 						=		(avgkeepscore + avgdelscore + avgaddscore) / 3
							return finalscore







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_ = True	,     UpperCamelCase_ = "13a"	,     UpperCamelCase_ = True   ):
							# Normalization is requried for the ASSET dataset (one of the primary
							# datasets in sentence simplification) to allow using space
							# to split the sentence. Even though Wiki-Auto and TURK datasets,
							# do not require normalization, we do it for consistency.
							# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
							# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

							if lowercase:
														A_ 						=		sentence.lower()

							if tokenizer in ["13a", "intl"]:
														if version.parse(sacrebleu.__version__   ).major >= 2:
																					A_ 						=		sacrebleu.metrics.bleu._get_tokenizer(UpperCamelCase_   )()(UpperCamelCase_   )
														else:
																					A_ 						=		sacrebleu.TOKENIZERS[tokenizer]()(UpperCamelCase_   )
							elif tokenizer == "moses":
														A_ 						=		sacremoses.MosesTokenizer().tokenize(UpperCamelCase_	,     return_str=UpperCamelCase_	,     escape=UpperCamelCase_   )
							elif tokenizer == "penn":
														A_ 						=		sacremoses.MosesTokenizer().penn_tokenize(UpperCamelCase_	,     return_str=UpperCamelCase_   )
							else:
														A_ 						=		sentence

							if not return_str:
														A_ 						=		normalized_sent.split()

							return normalized_sent







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							, _KWARGS_DESCRIPTION	)
class 				__lowerCAmelCase      (      datasets.Metric	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			Optional[int]  ):
													return datasets.MetricInfo(
													    description=_DESCRIPTION   ,     citation=_CITATION   ,     inputs_description=_KWARGS_DESCRIPTION   ,     features=datasets.Features(
													        {
													            "sources": datasets.Value("string"   ,     id="sequence"  ),
													            "predictions": datasets.Value("string"   ,     id="sequence"  ),
													            "references": datasets.Sequence(datasets.Value("string"   ,     id="sequence"  )   ,     id="references"  ),
													        }  )   ,     codebase_urls=[
													        "https://github.com/cocoxu/simplification/blob/master/SARI.py",
													        "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
													    ]   ,     reference_urls=["https://www.aclweb.org/anthology/Q16-1029.pdf"]   ,     )






						def 							_UpperCAmelCase					(     self       :			Tuple   ,     lowerCAmelCase       :			Optional[Any]   ,     lowerCAmelCase       :			Dict   ,     lowerCAmelCase       :			List[Any]  ):
													if not (len(lowerCAmelCase  ) == len(lowerCAmelCase  ) == len(lowerCAmelCase  )):
																				raise ValueError("Sources length must match predictions and references lengths."  )
													A_ 						=		0
													for src, pred, refs in zip(lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase  ):
																				sari_score += SARIsent(normalize(lowerCAmelCase  )   ,     normalize(lowerCAmelCase  )   ,     [normalize(lowerCAmelCase  ) for sent in refs]  )
													A_ 						=		sari_score / len(lowerCAmelCase  )
													return {"sari": 1_00 * sari_score}





