# kate: default-dictionary en_US

## This file is part of the 'stringi' package for R.
## Copyright (c) 2013-2025, Marek Gagolewski <https://www.gagolewski.com/>
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#' @title
#' Randomly Shuffle Code Points in Each String
#'
#' @description
#' Generates a (pseudo)random permutation of the code points
#' in each string.
#'
#' @details
#' This operation may result in non-Unicode-normalized
#' strings and may give peculiar outputs in case of bidirectional strings.
#'
#' See also \code{\link{stri_reverse}} for reversing the order of code points.
#'
#' @param str character vector
#'
#' @return Returns a character vector.
#'
#' @examples
#' stri_rand_shuffle(c('abcdefghi', '0123456789'))
#' # you can do better than this with stri_rand_strings:
#' stri_rand_shuffle(rep(stri_paste(letters, collapse=''), 10))
#'
#' @family random
#' @export
stri_rand_shuffle <- function(str)
{
    .Call(C_stri_rand_shuffle, str)
}


#' @title
#' Generate Random Strings
#'
#' @description
#' Generates (pseudo)random strings of desired lengths.
#'
#' @details
#' Vectorized over \code{length} and \code{pattern}.
#' If length of \code{length} or \code{pattern} is greater than \code{n},
#' then redundant elements are ignored. Otherwise,
#' these vectors are recycled if necessary.
#'
#' This operation may result in non-Unicode-normalized
#' strings and may give peculiar outputs for bidirectional strings.
#'
#' Sampling of code points from the set specified by \code{pattern}
#' is always done with replacement and each code point appears with equal
#' probability.
#'
#' @param n single integer, number of observations
#' @param length integer vector, desired string lengths
#' @param pattern character vector specifying character classes to draw
#' elements from, see \link{stringi-search-charclass}
#'
#' @return Returns a character vector.
#'
#' @examples
#' stri_rand_strings(5, 10) # 5 strings of length 10
#' stri_rand_strings(5, sample(1:10, 5, replace=TRUE)) # 5 strings of random lengths
#' stri_rand_strings(10, 5, '[\\p{script=latin}&\\p{Ll}]') # small letters from the Latin script
#'
#' # generate n random passwords of length in [8, 14]
#' # consisting of at least one digit, small and big ASCII letter:
#' n <- 10
#' stri_rand_shuffle(stri_paste(
#'    stri_rand_strings(n, 1, '[0-9]'),
#'    stri_rand_strings(n, 1, '[a-z]'),
#'    stri_rand_strings(n, 1, '[A-Z]'),
#'    stri_rand_strings(n, sample(5:11, 5, replace=TRUE), '[a-zA-Z0-9]')
#' ))
#'
#' @family random
#' @export
stri_rand_strings <- function(n, length, pattern = "[A-Za-z0-9]")
{
    .Call(C_stri_rand_strings, n, length, pattern)
}


#' @title
#' A Lorem Ipsum Generator
#'
#' @description
#' Generates (pseudo)random \emph{lorem ipsum} text consisting
#' of a given number of text paragraphs.
#'
#' @details
#' \emph{Lorem ipsum} is a dummy text often used as a source
#' of data for string processing and displaying/lay-outing exercises.
#'
#' The current implementation is very simple:
#' words are selected randomly from a Zipf distribution
#' (based on a set of ca. 190 predefined Latin words).
#' The number of words per sentence and sentences per paragraph
#' follows a discretized, truncated normal distribution.
#' No Markov chain modeling, just i.i.d. word selection.
#'
#' @param n_paragraphs single integer, number of paragraphs to generate
#' @param start_lipsum single logical value; should the resulting
#' text start with \emph{Lorem ipsum dolor sit amet}?
#' @param nparagraphs [DEPRECATED] alias of \code{n_paragraphs}
#'
#' @return Returns a character vector of length \code{n_paragraphs}.
#'
#' @examples
#' cat(sapply(
#'    stri_wrap(stri_rand_lipsum(10), 80, simplify=FALSE),
#'    stri_flatten, collapse='\n'), sep='\n\n')
#' cat(stri_rand_lipsum(10), sep='\n\n')
#'
#' @family random
#' @export
stri_rand_lipsum <- function(n_paragraphs, start_lipsum = TRUE,
    nparagraphs=n_paragraphs)
{
    if (!missing(nparagraphs) && missing(n_paragraphs)) { # DEPRECATED
        warning("The 'nparagraphs' argument in stri_rand_lipsum is a deprecated alias of 'n_paragraphs' and will be removed in a future release of 'stringi'.")
        n_paragraphs <- nparagraphs
    }

    # Whoa! A pure R function in stringi :)
    # Version 0.3-1 (Marek Gagolewski, 2014-10-16)

    n_paragraphs <- as.integer(n_paragraphs)
    stopifnot(is.finite(n_paragraphs), n_paragraphs >= 1)
    start_lipsum <- identical(start_lipsum, TRUE)

    rwords <- function(n) {
        # generate n random words
        words <- c("SED", "IN", "UT", "ET", "AC", "EU", "NON", "NEC", "AMET", "SIT",
            "VEL", "AT", "MAURIS", "A", "VITAE", "EGET", "QUIS", "NUNC", "NULLA",
            "ID", "VESTIBULUM", "PELLENTESQUE", "TINCIDUNT", "ALIQUAM", "IPSUM",
            "DONEC", "TURPIS", "LIGULA", "EGESTAS", "NIBH", "SAPIEN", "ANTE", "NISL",
            "VELIT", "ERAT", "EROS", "LEO", "MAGNA", "JUSTO", "ENIM", "MI", "PURUS",
            "EST", "LACUS", "LOREM", "QUAM", "DIAM", "RISUS", "DOLOR", "SEM", "AUGUE",
            "NEQUE", "TEMPOR", "DUI", "ARCU", "METUS", "TORTOR", "URNA", "LIBERO",
            "PHARETRA", "TEMPUS", "FAUCIBUS", "LECTUS", "SUSPENDISSE", "FELIS", "ODIO",
            "ORCI", "VARIUS", "MASSA", "TELLUS", "VOLUTPAT", "BLANDIT", "INTERDUM",
            "LOBORTIS", "MAXIMUS", "NISI", "LUCTUS", "PORTTITOR", "AUCTOR", "ELEMENTUM",
            "EX", "MAECENAS", "MALESUADA", "TRISTIQUE", "ULLAMCORPER", "ULTRICES",
            "NULLAM", "CONSEQUAT", "LACINIA", "PHASELLUS", "ACCUMSAN", "DAPIBUS",
            "ELEIFEND", "COMMODO", "DUIS", "EFFICITUR", "ELIT", "IMPERDIET", "AENEAN",
            "IACULIS", "NAM", "CONSECTETUR", "FERMENTUM", "PORTA", "SCELERISQUE",
            "SODALES", "FEUGIAT", "LAOREET", "VULPUTATE", "DICTUM", "QUISQUE", "FACILISIS",
            "FINIBUS", "ORNARE", "PULVINAR", "RHONCUS", "CONDIMENTUM", "MOLLIS",
            "PRETIUM", "ALIQUET", "CONGUE", "POSUERE", "SUSCIPIT", "ULTRICIES", "CURABITUR",
            "GRAVIDA", "MATTIS", "VIVERRA", "CURSUS", "EUISMOD", "RUTRUM", "VENENATIS",
            "CONVALLIS", "PROIN", "VEHICULA", "PLACERAT", "SAGITTIS", "CRAS", "INTEGER",
            "MORBI", "VIVAMUS", "PRAESENT", "BIBENDUM", "MOLESTIE", "SEMPER", "FRINGILLA",
            "FUSCE", "DIGNISSIM", "ETIAM", "HENDRERIT", "SOLLICITUDIN", "PER", "FAMES",
            "POTENTI", "AD", "APTENT", "CLASS", "CONUBIA", "HIMENAEOS", "INCEPTOS",
            "LITORA", "NOSTRA", "SOCIOSQU", "TACITI", "TORQUENT", "HABITANT", "NETUS",
            "SENECTUS", "PRIMIS", "CUM", "DIS", "MAGNIS", "MONTES", "MUS", "NASCETUR",
            "NATOQUE", "PARTURIENT", "PENATIBUS", "RIDICULUS", "SOCIIS", "ADIPISCING",
            "FACILISI", "CUBILIA", "CURAE", "DICTUMST", "HABITASSE", "HAC", "PLATEA")

        # Zipf distribution
        dzipf <- function(k, N, s) 1/k^s/sum(1/(1:N)^s)
        pzipf.y <- c(0, cumsum(dzipf(1:length(words), length(words), 0.5)))
        robs <- findInterval(runif(n), pzipf.y)
        words[robs]
    }


    rtruncnorm <- function(n, a, b, mu, sd) {
        # truncated discretized normal distribution
        x <- round(rnorm(n, mu, sd))
        while (any(x < a | x > b)) x[x < a | x > b] <- round(rnorm(sum(x < a | x >
            b), mu, sd))
        x
    }

    sent_para <- rtruncnorm(n_paragraphs, 7, 20, 11, 3)
    word_sent <- lapply(sent_para, function(numsent) rtruncnorm(numsent, 2, Inf,
        8, 3))

    totwords <- sum(unlist(word_sent))
    words <- rwords(totwords)
    seps <- sample(c(" ", ", "), replace = TRUE, size = totwords, prob = c(0.9, 0.1))
    seps[cumsum(unlist(word_sent))] <- sample(c(". ", "? ", "! "), size = length(unlist(word_sent)),
        replace = TRUE, prob = c(0.95, 0.025, 0.025))  # end of sentence
    seps[cumsum(sapply(word_sent, sum))] <- ".\n"  # end of para
    seps[totwords] <- "."  # very last sentence in very last para

    if (start_lipsum) {
        words <- c("LOREM", "IPSUM", "DOLOR", "SIT", "AMET", words)
        seps <- c(" ", " ", " ", " ", ", ", seps)
    }

    ret <- stri_split_charclass(stri_paste(words, seps, collapse = ""), "[\\n]")[[1]]
    ret <- stri_trans_totitle(ret, opts_brkiter = stri_opts_brkiter(type = "sentence"))
    ret
}