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更新libclamav库1.0.0版本

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2023-01-14 18:28:39 +08:00
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{"files":{"Cargo.toml":"97b993eb73c85914f776001fef3c405dbdd29d3eae80ac39c0e962f149796715","LICENSE-APACHE":"8797ef61538ec5ee9222ebef7ca4e0f3ec5761b145ca9943d358c450efb644dd","LICENSE-MIT":"5080149357fd0be590bdc10cf92165412bb4d61ce496284d56f2d12874ae3121","README.md":"e500f94a56b62d7c6e8dc8e07540d875ce20c92f582468de95662a28fe96e212","RELEASES.md":"697192465d008397fed6d462d43b9789b42f89dc25b273c61f9ada9882f6fc9a","benches/transpose_benchmarks.rs":"87745b176ceadf9b02a173eb6b6b81103d5e2145c3647e5c3e3bf3a7f8dbbbf9","src/in_place.rs":"8cd6b6bafefa4e7137d86ed8a19958193b435cc573c36dc089b2ddcf75ea47f6","src/lib.rs":"bfd475ad17b2f050b1ee17ba57f82d6ea3fc3bd1485568a12560d214d5672914","src/out_of_place.rs":"306476933ec99fd09f322db767fc6c1b26a15059136112d89d218632336276fb","tests/test_transpose.rs":"caad0cfa784d428dcb4b36d98dc5eac63b46b09fac0a9582f424a50e8b2d0f3c"},"package":"e6522d49d03727ffb138ae4cbc1283d3774f0d10aa7f9bf52e6784c45daf9b23"}

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# THIS FILE IS AUTOMATICALLY GENERATED BY CARGO
#
# When uploading crates to the registry Cargo will automatically
# "normalize" Cargo.toml files for maximal compatibility
# with all versions of Cargo and also rewrite `path` dependencies
# to registry (e.g., crates.io) dependencies.
#
# If you are reading this file be aware that the original Cargo.toml
# will likely look very different (and much more reasonable).
# See Cargo.toml.orig for the original contents.
[package]
name = "transpose"
version = "0.2.2"
authors = ["Elliott Mahler <join.together@gmail.com>"]
description = "Utility for transposing multi-dimensional data"
documentation = "http://docs.rs/transpose"
readme = "README.md"
keywords = [
"array",
"transpose",
"2d",
]
categories = [
"algorithms",
"data structures",
"no-std",
]
license = "MIT OR Apache-2.0"
repository = "http://github.com/ejmahler/transpose"
[[bench]]
name = "transpose_benchmarks"
harness = false
[dependencies.num-integer]
version = "0.1"
default-features = false
[dependencies.strength_reduce]
version = "^0.2.1"
[dev-dependencies.criterion]
version = "0.3"

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APPENDIX: How to apply the Apache License to your work.
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Copyright 2022 The transpose developers
Licensed under the Apache License, Version 2.0 (the "License");
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Copyright (c) 2022 The transpose Developers
Permission is hereby granted, free of charge, to any
person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the
Software without restriction, including without
limitation the rights to use, copy, modify, merge,
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the Software, and to permit persons to whom the Software
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conditions:
The above copyright notice and this permission notice
shall be included in all copies or substantial portions
of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF
ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
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SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE SOFTWARE.

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# transpose
[![crate](https://img.shields.io/crates/v/transpose.svg)](https://crates.io/crates/transpose)
[![license](https://img.shields.io/crates/l/transpose.svg)](https://crates.io/crates/transpose)
[![documentation](https://docs.rs/transpose/badge.svg)](https://docs.rs/transpose/)
![minimum rustc 1.26](https://img.shields.io/badge/rustc-1.26+-red.svg)
Utility for transposing multi-dimensional data See the [API Documentation](https://docs.rs/transpose/) for more details.
`transpose` is `#![no_std]`
## Example
```rust
// Create a 2D array in row-major order: the rows of our 2D array are contiguous,
// and the columns are strided
let input_array = vec![ 1, 2, 3,
4, 5, 6];
// Treat our 6-element array as a 2D 3x2 array, and transpose it to a 2x3 array
let mut output_array = vec![0; 6];
transpose::transpose(&input_array, &mut output_array, 3, 2);
// The rows have become the columns, and the columns have become the rows
let expected_array = vec![ 1, 4,
2, 5,
3, 6];
assert_eq!(output_array, expected_array);
```
## Compatibility
The `transpose` crate requires rustc 1.26 or greater.
## License
Licensed under either of
* Apache License, Version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or http://www.apache.org/licenses/LICENSE-2.0)
* MIT license ([LICENSE-MIT](LICENSE-MIT) or http://opensource.org/licenses/MIT)
at your option.
### Contribution
Unless you explicitly state otherwise, any contribution intentionally
submitted for inclusion in the work by you, as defined in the Apache-2.0
license, shall be dual licensed as above, without any additional terms or
conditions.

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# Release 0.2.2 (2022-11-07)
## Fixes
- Added missing license files
- Upgraded `criterion` dependency from 0.2 to 0.3
# Release 0.2.1 (2020-03-30)
## Improvements
- Significantly improved the performance of the out-of-place transpose
- Removed depenendence on `std` in the `num_integer` dependency.
# Release 0.2.0 (2019-01-04)
## Features
- Implemented an in-place transpose.
### Breaking Changes
- Documented minimum rust version to be 1.26
# Release 0.1.0 (2019-01-01)
- Initial release. Support for an out-of-place transpose.

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#[macro_use]
extern crate criterion;
extern crate transpose;
use criterion::{Criterion, ParameterizedBenchmark, Throughput};
use std::mem;
use std::time::Duration;
fn bench_oop_transpose<T: Copy + Default>(c: &mut Criterion, tyname: &str) {
let ref sizes = [(4, 4), (8, 8), (16, 16), (64, 64), (256, 256), (1024, 1024), (2048, 2048), (4096, 4096)];
let bench = ParameterizedBenchmark::new(tyname,
|b, &&(width, height)| {
let mut buffer = vec![T::default(); width * height];
let mut scratch = vec![T::default(); width * height];
b.iter(|| { transpose::transpose(&mut buffer, &mut scratch, width, height); });
},
sizes)
.throughput(|&&(width, height)| Throughput::Bytes((width * height * mem::size_of::<T>()) as u32))
.warm_up_time(Duration::from_secs(1));
c.bench("square transposes out-of-place", bench);
}
fn bench_oop_u32(c: &mut Criterion) { bench_oop_transpose::<u32>(c, "u32") }
fn bench_oop_u64(c: &mut Criterion) { bench_oop_transpose::<u64>(c, "u64") }
criterion_group!(out_of_place_benches, bench_oop_u32, bench_oop_u64);
fn bench_inplace_transpose<T: Copy + Default>(c: &mut Criterion, tyname: &str) {
let ref sizes = [(4, 4), (8, 8), (16, 16), (64, 64), (256, 256), (1024, 1024)];
let bench = ParameterizedBenchmark::new(tyname,
|b, &&(width, height)| {
let mut buffer = vec![T::default(); width * height];
let mut scratch = vec![T::default(); std::cmp::max(width, height)];
b.iter(|| { transpose::transpose_inplace(&mut buffer, &mut scratch, width, height); });
},
sizes)
.throughput(|&&(width, height)| Throughput::Bytes((width * height * mem::size_of::<T>()) as u32))
.warm_up_time(Duration::from_secs(1));
c.bench("square transposes inplace", bench);
}
fn bench_inplace_u32(c: &mut Criterion) { bench_inplace_transpose::<u32>(c, "u32") }
fn bench_inplace_u64(c: &mut Criterion) { bench_inplace_transpose::<u64>(c, "u64") }
criterion_group!(inplace_benches, bench_inplace_u32, bench_inplace_u64);
criterion_main!(out_of_place_benches, inplace_benches);

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use strength_reduce::StrengthReducedUsize;
use num_integer;
fn multiplicative_inverse(a: usize, n: usize) -> usize {
// we're going to use a modified version extended euclidean algorithm
// we only need half the output
let mut t = 0;
let mut t_new = 1;
let mut r = n;
let mut r_new = a;
while r_new > 0 {
let quotient = r / r_new;
r = r - quotient * r_new;
core::mem::swap(&mut r, &mut r_new);
// t might go negative here, so we have to do a checked subtract
// if it underflows, wrap it around to the other end of the modulo
// IE, 3 - 4 mod 5 = -1 mod 5 = 4
let t_subtract = quotient * t_new;
t = if t_subtract < t {
t - t_subtract
} else {
n - (t_subtract - t) % n
};
core::mem::swap(&mut t, &mut t_new);
}
t
}
/// Transpose the input array in-place.
///
/// Given an input array of size input_width * input_height, representing flattened 2D data stored in row-major order,
/// transpose the rows and columns of that input array, in-place.
///
/// Despite being in-place, this algorithm requires max(width * height) in scratch space.
///
/// ```
/// // row-major order: the rows of our 2D array are contiguous,
/// // and the columns are strided
/// let original_array = vec![ 1, 2, 3,
/// 4, 5, 6];
/// let mut input_array = original_array.clone();
///
/// // Treat our 6-element array as a 2D 3x2 array, and transpose it to a 2x3 array
/// // transpose_inplace requires max(width, height) scratch space, which is in this case 3
/// let mut scratch = vec![0; 3];
/// transpose::transpose_inplace(&mut input_array, &mut scratch, 3, 2);
///
/// // The rows have become the columns, and the columns have become the rows
/// let expected_array = vec![ 1, 4,
/// 2, 5,
/// 3, 6];
/// assert_eq!(input_array, expected_array);
///
/// // If we transpose it again, we should get our original data back.
/// transpose::transpose_inplace(&mut input_array, &mut scratch, 2, 3);
/// assert_eq!(original_array, input_array);
/// ```
///
/// # Panics
///
/// Panics if `input.len() != input_width * input_height` or if `output.len() != input_width * input_height`
pub fn transpose_inplace<T: Copy>(buffer: &mut [T], scratch: &mut [T], width: usize, height: usize) {
assert_eq!(width*height, buffer.len());
assert_eq!(core::cmp::max(width, height), scratch.len());
let gcd = StrengthReducedUsize::new(num_integer::gcd(width, height));
let a = StrengthReducedUsize::new(height / gcd);
let b = StrengthReducedUsize::new(width / gcd);
let a_inverse = multiplicative_inverse(a.get(), b.get());
let strength_reduced_height = StrengthReducedUsize::new(height);
let index_fn = |x, y| x + y * width;
if gcd.get() > 1 {
for x in 0..width {
let column_offset = (x / b) % strength_reduced_height;
let wrapping_point = height - column_offset;
// wrapped rotation -- do the "right half" of the array, then the "left half"
for y in 0..wrapping_point {
scratch[y] = buffer[index_fn(x, y + column_offset)];
}
for y in wrapping_point..height {
scratch[y] = buffer[index_fn(x, y + column_offset - height)];
}
// copy the data back into the column
for y in 0..height {
buffer[index_fn(x, y)] = scratch[y];
}
}
}
// Permute the rows
{
let row_scratch = &mut scratch[0..width];
for (y, row) in buffer.chunks_exact_mut(width).enumerate() {
for x in 0..width {
let helper_val = if y <= height + x%gcd - gcd.get() { x + y*(width-1) } else { x + y*(width-1) + height };
let (helper_div, helper_mod) = StrengthReducedUsize::div_rem(helper_val, gcd);
let gather_x = (a_inverse * helper_div)%b + b.get()*helper_mod;
row_scratch[x] = row[gather_x];
}
row.copy_from_slice(row_scratch);
}
}
// Permute the columns
for x in 0..width {
let column_offset = x % strength_reduced_height;
let wrapping_point = height - column_offset;
// wrapped rotation -- do the "right half" of the array, then the "left half"
for y in 0..wrapping_point {
scratch[y] = buffer[index_fn(x, y + column_offset)];
}
for y in wrapping_point..height {
scratch[y] = buffer[index_fn(x, y + column_offset - height)];
}
// Copy the data back to the buffer, but shuffle it as we do so
for y in 0..height {
let shuffled_y = (y * width - (y / a)) % strength_reduced_height;
buffer[index_fn(x, y)] = scratch[shuffled_y];
}
}
}

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//! Utility for transposing multi-dimensional data stored as a flat slice
//!
//! This library treats Rust slices as flattened row-major 2D arrays, and provides functions to transpose these 2D arrays, so that the row data becomes the column data, and vice versa.
//! ```
//! // Create a 2D array in row-major order: the rows of our 2D array are contiguous,
//! // and the columns are strided
//! let input_array = vec![ 1, 2, 3,
//! 4, 5, 6];
//!
//! // Treat our 6-element array as a 2D 3x2 array, and transpose it to a 2x3 array
//! let mut output_array = vec![0; 6];
//! transpose::transpose(&input_array, &mut output_array, 3, 2);
//!
//! // The rows have become the columns, and the columns have become the rows
//! let expected_array = vec![ 1, 4,
//! 2, 5,
//! 3, 6];
//! assert_eq!(output_array, expected_array);
//!
//! // If we transpose our data again, we should get our original data back.
//! let mut final_array = vec![0; 6];
//! transpose::transpose(&output_array, &mut final_array, 2, 3);
//! assert_eq!(final_array, input_array);
//! ```
//!
//! This library supports both in-place and out-of-place transposes. The out-of-place
//! transpose is much, much faster than the in-place transpose -- the in-place transpose should
//! only be used in situations where the system doesn't have enough memory to do an out-of-place transpose.
//!
//! The out-of-place transpose uses one out of three different algorithms depending on the length of the input array.
//!
//! - Small: simple iteration over the array.
//! - Medium: divide the array into tiles of fixed size, and process each tile separately.
//! - Large: recursively split the array into smaller parts until each part is of a good size for the tiling algorithm, and then transpose each part.
#![no_std]
extern crate num_integer;
extern crate strength_reduce;
mod in_place;
mod out_of_place;
pub use in_place::transpose_inplace;
pub use out_of_place::transpose;

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// Block size used by the tiling algoritms
const BLOCK_SIZE: usize = 16;
// Number of segments used by the segmented block transpose function
const NBR_SEGMENTS: usize = 4;
// recursively split data until the number of rows and columns is below this number
const RECURSIVE_LIMIT: usize = 128;
// Largest size for for using the direct approach
const SMALL_LEN: usize = 255;
// Largest size for using the tiled approach
const MEDIUM_LEN: usize = 1024*1024;
/// Given an array of size width * height, representing a flattened 2D array,
/// transpose the rows and columns of that 2D array into the output.
/// Benchmarking shows that loop tiling isn't effective for small arrays.
unsafe fn transpose_small<T: Copy>(input: &[T], output: &mut [T], width: usize, height: usize) {
for x in 0..width {
for y in 0..height {
let input_index = x + y * width;
let output_index = y + x * height;
*output.get_unchecked_mut(output_index) = *input.get_unchecked(input_index);
}
}
}
// Transpose a subset of the array, from the input into the output. The idea is that by transposing one block at a time, we can be more cache-friendly
// SAFETY: Width * height must equal input.len() and output.len(), start_x + block_width must be <= width, start_y + block height must be <= height
unsafe fn transpose_block<T: Copy>(input: &[T], output: &mut [T], width: usize, height: usize, start_x: usize, start_y: usize, block_width: usize, block_height: usize) {
for inner_x in 0..block_width {
for inner_y in 0..block_height {
let x = start_x + inner_x;
let y = start_y + inner_y;
let input_index = x + y * width;
let output_index = y + x * height;
*output.get_unchecked_mut(output_index) = *input.get_unchecked(input_index);
}
}
}
// Transpose a subset of the array, from the input into the output. The idea is that by transposing one block at a time, we can be more cache-friendly
// SAFETY: Width * height must equal input.len() and output.len(), start_x + block_width must be <= width, start_y + block height must be <= height
// This function works as `transpose_block`, but also divides the loop into a number of segments. This makes it more cache fiendly for large sizes.
unsafe fn transpose_block_segmented<T: Copy>(input: &[T], output: &mut [T], width: usize, height: usize, start_x: usize, start_y: usize, block_width: usize, block_height: usize) {
let height_per_div = block_height/NBR_SEGMENTS;
for subblock in 0..NBR_SEGMENTS {
for inner_x in 0..block_width {
for inner_y in 0..height_per_div {
let x = start_x + inner_x;
let y = start_y + inner_y + subblock*height_per_div;
let input_index = x + y * width;
let output_index = y + x * height;
*output.get_unchecked_mut(output_index) = *input.get_unchecked(input_index);
}
}
}
}
/// Given an array of size width * height, representing a flattened 2D array,
/// transpose the rows and columns of that 2D array into the output.
/// This algorithm divides the input into tiles of size BLOCK_SIZE*BLOCK_SIZE,
/// in order to reduce cache misses. This works well for medium sizes, when the
/// data for each tile fits in the caches.
fn transpose_tiled<T: Copy>(input: &[T], output: &mut [T], input_width: usize, input_height: usize) {
let x_block_count = input_width / BLOCK_SIZE;
let y_block_count = input_height / BLOCK_SIZE;
let remainder_x = input_width - x_block_count * BLOCK_SIZE;
let remainder_y = input_height - y_block_count * BLOCK_SIZE;
for y_block in 0..y_block_count {
for x_block in 0..x_block_count {
unsafe {
transpose_block(
input, output,
input_width, input_height,
x_block * BLOCK_SIZE, y_block * BLOCK_SIZE,
BLOCK_SIZE, BLOCK_SIZE,
);
}
}
//if the input_width is not cleanly divisible by block_size, there are still a few columns that haven't been transposed
if remainder_x > 0 {
unsafe {
transpose_block(
input, output,
input_width, input_height,
input_width - remainder_x, y_block * BLOCK_SIZE,
remainder_x, BLOCK_SIZE);
}
}
}
//if the input_height is not cleanly divisible by BLOCK_SIZE, there are still a few rows that haven't been transposed
if remainder_y > 0 {
for x_block in 0..x_block_count {
unsafe {
transpose_block(
input, output,
input_width, input_height,
x_block * BLOCK_SIZE, input_height - remainder_y,
BLOCK_SIZE, remainder_y,
);
}
}
//if the input_width is not cleanly divisible by block_size, there are still a few rows+columns that haven't been transposed
if remainder_x > 0 {
unsafe {
transpose_block(
input, output,
input_width, input_height,
input_width - remainder_x, input_height - remainder_y,
remainder_x, remainder_y);
}
}
}
}
/// Given an array of size width * height, representing a flattened 2D array,
/// transpose the rows and columns of that 2D array into the output.
/// This is a recursive algorithm that divides the array into smaller pieces, until they are small enough to
/// transpose directly without worrying about cache misses.
/// Once they are small enough, they are transposed using a tiling algorithm.
fn transpose_recursive<T: Copy>(input: &[T], output: &mut [T], row_start: usize, row_end: usize, col_start: usize, col_end: usize, total_columns: usize, total_rows: usize) {
let nbr_rows = row_end - row_start;
let nbr_cols = col_end - col_start;
if (nbr_rows <= RECURSIVE_LIMIT && nbr_cols <= RECURSIVE_LIMIT) || nbr_rows<=2 || nbr_cols<=2 {
let x_block_count = nbr_cols / BLOCK_SIZE;
let y_block_count = nbr_rows / BLOCK_SIZE;
let remainder_x = nbr_cols - x_block_count * BLOCK_SIZE;
let remainder_y = nbr_rows - y_block_count * BLOCK_SIZE;
for y_block in 0..y_block_count {
for x_block in 0..x_block_count {
unsafe {
transpose_block_segmented(
input, output,
total_columns, total_rows,
col_start + x_block * BLOCK_SIZE, row_start + y_block * BLOCK_SIZE,
BLOCK_SIZE, BLOCK_SIZE,
);
}
}
//if the input_width is not cleanly divisible by block_size, there are still a few columns that haven't been transposed
if remainder_x > 0 {
unsafe {
transpose_block(
input, output,
total_columns, total_rows,
col_start + x_block_count * BLOCK_SIZE, row_start + y_block * BLOCK_SIZE,
remainder_x, BLOCK_SIZE);
}
}
}
//if the input_height is not cleanly divisible by BLOCK_SIZE, there are still a few rows that haven't been transposed
if remainder_y > 0 {
for x_block in 0..x_block_count {
unsafe {
transpose_block(
input, output,
total_columns, total_rows,
col_start + x_block * BLOCK_SIZE, row_start + y_block_count * BLOCK_SIZE,
BLOCK_SIZE, remainder_y,
);
}
}
//if the input_width is not cleanly divisible by block_size, there are still a few rows+columns that haven't been transposed
if remainder_x > 0 {
unsafe {
transpose_block(
input, output,
total_columns, total_rows,
col_start + x_block_count * BLOCK_SIZE, row_start + y_block_count * BLOCK_SIZE,
remainder_x, remainder_y);
}
}
}
} else if nbr_rows >= nbr_cols {
transpose_recursive(input, output, row_start, row_start + (nbr_rows / 2), col_start, col_end, total_columns, total_rows);
transpose_recursive(input, output, row_start + (nbr_rows / 2), row_end, col_start, col_end, total_columns, total_rows);
} else {
transpose_recursive(input, output, row_start, row_end, col_start, col_start + (nbr_cols / 2), total_columns, total_rows);
transpose_recursive(input, output, row_start, row_end, col_start + (nbr_cols / 2), col_end, total_columns, total_rows);
}
}
/// Transpose the input array into the output array.
///
/// Given an input array of size input_width * input_height, representing flattened 2D data stored in row-major order,
/// transpose the rows and columns of that input array into the output array
/// ```
/// // row-major order: the rows of our 2D array are contiguous,
/// // and the columns are strided
/// let input_array = vec![ 1, 2, 3,
/// 4, 5, 6];
///
/// // Treat our 6-element array as a 2D 3x2 array, and transpose it to a 2x3 array
/// let mut output_array = vec![0; 6];
/// transpose::transpose(&input_array, &mut output_array, 3, 2);
///
/// // The rows have become the columns, and the columns have become the rows
/// let expected_array = vec![ 1, 4,
/// 2, 5,
/// 3, 6];
/// assert_eq!(output_array, expected_array);
///
/// // If we transpose it again, we should get our original data back.
/// let mut final_array = vec![0; 6];
/// transpose::transpose(&output_array, &mut final_array, 2, 3);
/// assert_eq!(final_array, input_array);
/// ```
///
/// # Panics
///
/// Panics if `input.len() != input_width * input_height` or if `output.len() != input_width * input_height`
pub fn transpose<T: Copy>(input: &[T], output: &mut [T], input_width: usize, input_height: usize) {
assert_eq!(input_width*input_height, input.len());
assert_eq!(input_width*input_height, output.len());
if input.len() <= SMALL_LEN {
unsafe { transpose_small(input, output, input_width, input_height) };
}
else if input.len() <= MEDIUM_LEN {
transpose_tiled(input, output, input_width, input_height);
}
else {
transpose_recursive(input, output, 0, input_height, 0, input_width, input_width, input_height);
}
}

51
clamav/libclamav_rust/.cargo/vendor/transpose/tests/test_transpose.rs vendored Normal file
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extern crate transpose;
fn gen_data(width: usize, height: usize) -> Vec<usize> {
(0..width*height).collect()
}
const BLOCK_SIZE: usize = 16;
#[test]
fn test_out_of_place_transpose() {
let sizes = [
0, 1, 2,
BLOCK_SIZE - 1, BLOCK_SIZE, BLOCK_SIZE + 1,
BLOCK_SIZE * 4 - 1, BLOCK_SIZE * 5, BLOCK_SIZE * 4 + 1
];
for &width in &sizes {
for &height in &sizes {
let input = gen_data(width, height);
let mut output = vec![0; width * height];
transpose::transpose(&input, &mut output, width, height);
for x in 0..width {
for y in 0..height {
assert_eq!(input[x + y * width], output[y + x * height], "x = {}, y = {}", x, y);
}
}
}
}
}
#[test]
fn test_transpose_inplace() {
for width in 1..10 {
for height in 1..10 {
let input = gen_data(width, height);
let mut output = input.clone();
let mut scratch = vec![usize::default(); std::cmp::max(width, height)];
transpose::transpose_inplace(&mut output, &mut scratch, width, height);
for x in 0..width {
for y in 0..height {
assert_eq!(input[x + y * width], output[y + x * height], "x = {}, y = {}", x, y);
}
}
}
}
}