runmat_runtime/builtins/io/tabular/

readmatrix.rs

//! MATLAB-compatible `readmatrix` builtin for RunMat.

use std::collections::HashSet;
use std::convert::TryFrom;
use std::io::{BufRead, BufReader};
use std::path::{Path, PathBuf};

use runmat_accelerate_api::HostTensorView;
use runmat_builtins::{LogicalArray, Tensor, Value};
use runmat_filesystem::File;
use runmat_macros::runtime_builtin;

use crate::builtins::common::spec::{
    BroadcastSemantics, BuiltinFusionSpec, BuiltinGpuSpec, ConstantStrategy, GpuOpKind,
    ReductionNaN, ResidencyPolicy, ShapeRequirements,
};
use crate::{build_runtime_error, gather_if_needed_async, BuiltinResult, RuntimeError};

const BUILTIN_NAME: &str = "readmatrix";

#[runmat_macros::register_gpu_spec(builtin_path = "crate::builtins::io::tabular::readmatrix")]
pub const GPU_SPEC: BuiltinGpuSpec = BuiltinGpuSpec {
    name: "readmatrix",
    op_kind: GpuOpKind::Custom("io-readmatrix"),
    supported_precisions: &[],
    broadcast: BroadcastSemantics::None,
    provider_hooks: &[],
    constant_strategy: ConstantStrategy::InlineLiteral,
    residency: ResidencyPolicy::GatherImmediately,
    nan_mode: ReductionNaN::Include,
    two_pass_threshold: None,
    workgroup_size: None,
    accepts_nan_mode: false,
    notes: "Runs entirely on the host; acceleration providers are not involved.",
};

fn readmatrix_error(message: impl Into<String>) -> RuntimeError {
    build_runtime_error(message)
        .with_builtin(BUILTIN_NAME)
        .build()
}

fn readmatrix_error_with_source<E>(message: impl Into<String>, source: E) -> RuntimeError
where
    E: std::error::Error + Send + Sync + 'static,
{
    build_runtime_error(message)
        .with_builtin(BUILTIN_NAME)
        .with_source(source)
        .build()
}

fn map_control_flow(err: RuntimeError) -> RuntimeError {
    let identifier = err.identifier().map(|value| value.to_string());
    let message = err.message().to_string();
    let mut builder = build_runtime_error(message)
        .with_builtin(BUILTIN_NAME)
        .with_source(err);
    if let Some(identifier) = identifier {
        builder = builder.with_identifier(identifier);
    }
    builder.build()
}

#[runmat_macros::register_fusion_spec(builtin_path = "crate::builtins::io::tabular::readmatrix")]
pub const FUSION_SPEC: BuiltinFusionSpec = BuiltinFusionSpec {
    name: "readmatrix",
    shape: ShapeRequirements::Any,
    constant_strategy: ConstantStrategy::InlineLiteral,
    elementwise: None,
    reduction: None,
    emits_nan: false,
    notes: "Not eligible for fusion; executes as a standalone host operation.",
};

#[runtime_builtin(
    name = "readmatrix",
    category = "io/tabular",
    summary = "Import numeric data from delimited text files into a RunMat matrix.",
    keywords = "readmatrix,csv,delimited text,numeric import,table",
    accel = "cpu",
    type_resolver(crate::builtins::io::type_resolvers::readmatrix_type),
    builtin_path = "crate::builtins::io::tabular::readmatrix"
)]
async fn readmatrix_builtin(path: Value, rest: Vec<Value>) -> crate::BuiltinResult<Value> {
    let path_value = gather_if_needed_async(&path)
        .await
        .map_err(map_control_flow)?;
    let options = parse_options(&rest).await?;
    options.validate()?;
    let resolved = resolve_path(&path_value)?;
    let tensor = read_numeric_matrix(&resolved, &options)?;
    finalize_output(tensor, &options)
}

async fn parse_options(args: &[Value]) -> BuiltinResult<ReadMatrixOptions> {
    let mut options = ReadMatrixOptions::default();
    let mut index = 0usize;
    if let Some(Value::Struct(struct_value)) = args.get(index) {
        parse_struct_options(struct_value, &mut options).await?;
        index += 1;
    }
    while index < args.len() {
        if index + 1 >= args.len() {
            return Err(readmatrix_error(
                "readmatrix: name/value inputs must appear in pairs",
            ));
        }
        let name_value = gather_if_needed_async(&args[index])
            .await
            .map_err(map_control_flow)?;
        let name = option_name_from_value(&name_value)?;
        let value = &args[index + 1];
        apply_option(&mut options, &name, value).await?;
        index += 2;
    }
    Ok(options)
}

async fn parse_struct_options(
    struct_value: &runmat_builtins::StructValue,
    options: &mut ReadMatrixOptions,
) -> BuiltinResult<()> {
    for (name, value) in &struct_value.fields {
        apply_option(options, name, value).await?;
    }
    Ok(())
}

async fn apply_option(
    options: &mut ReadMatrixOptions,
    name: &str,
    value: &Value,
) -> BuiltinResult<()> {
    let lowered = name.trim().to_ascii_lowercase();
    let is_like = lowered == "like";
    let effective_value = if is_like {
        value.clone()
    } else {
        gather_if_needed_async(value)
            .await
            .map_err(map_control_flow)?
    };
    if name.eq_ignore_ascii_case("Delimiter") {
        let delimiter = parse_delimiter(&effective_value)?;
        options.delimiter = Some(delimiter);
        return Ok(());
    }
    if name.eq_ignore_ascii_case("NumHeaderLines") {
        let header_lines = value_to_usize(&effective_value, "NumHeaderLines")?;
        options.num_header_lines = header_lines;
        return Ok(());
    }
    if name.eq_ignore_ascii_case("TreatAsMissing") {
        let tokens = parse_treat_as_missing(&effective_value).await?;
        for token in tokens {
            options.add_missing_token(&token);
        }
        return Ok(());
    }
    if name.eq_ignore_ascii_case("DecimalSeparator") {
        let sep = parse_separator_char(&effective_value, "DecimalSeparator")?;
        options.decimal_separator = sep;
        return Ok(());
    }
    if name.eq_ignore_ascii_case("ThousandsSeparator") {
        let sep = parse_separator_char(&effective_value, "ThousandsSeparator")?;
        options.thousands_separator = Some(sep);
        return Ok(());
    }
    if name.eq_ignore_ascii_case("EmptyValue") {
        let numeric = value_to_f64(&effective_value, "EmptyValue")?;
        options.empty_value = Some(numeric);
        return Ok(());
    }
    if name.eq_ignore_ascii_case("OutputType") {
        let text = value_to_string_scalar(&effective_value, "OutputType")?;
        options.set_output_type(&text)?;
        return Ok(());
    }
    if name.eq_ignore_ascii_case("Range") {
        let range = parse_range(&effective_value)?;
        options.range = Some(range);
        return Ok(());
    }
    if is_like {
        options.set_like(effective_value)?;
        return Ok(());
    }
    // Unknown options are ignored for forward compatibility.
    Ok(())
}

fn option_name_from_value(value: &Value) -> BuiltinResult<String> {
    value_to_string_scalar(value, "option name")
}

fn parse_delimiter(value: &Value) -> BuiltinResult<Delimiter> {
    let text = value_to_string_scalar(value, "Delimiter")?;
    if text.is_empty() {
        return Err(readmatrix_error("readmatrix: Delimiter cannot be empty"));
    }
    let trimmed_lower = text.trim().to_ascii_lowercase();
    match trimmed_lower.as_str() {
        "tab" => Ok(Delimiter::Char('\t')),
        "space" | "whitespace" => Ok(Delimiter::Whitespace),
        "comma" => Ok(Delimiter::Char(',')),
        "semicolon" => Ok(Delimiter::Char(';')),
        "pipe" => Ok(Delimiter::Char('|')),
        _ => {
            if text.chars().count() == 1 {
                Ok(Delimiter::Char(text.chars().next().unwrap()))
            } else {
                Ok(Delimiter::String(text))
            }
        }
    }
}

fn parse_separator_char(value: &Value, option_name: &str) -> BuiltinResult<char> {
    let text = value_to_string_scalar(value, option_name)?;
    if text.is_empty() {
        return Err(readmatrix_error(format!(
            "readmatrix: {option_name} must be a single character"
        )));
    }
    let mut chars = text.chars();
    let ch = chars.next().unwrap();
    if chars.next().is_some() {
        return Err(readmatrix_error(format!(
            "readmatrix: {option_name} must be a single character"
        )));
    }
    if ch == '\n' || ch == '\r' {
        return Err(readmatrix_error(format!(
            "readmatrix: {option_name} cannot be a newline character"
        )));
    }
    Ok(ch)
}

fn value_to_string_scalar(value: &Value, context: &str) -> BuiltinResult<String> {
    match value {
        Value::String(s) => Ok(s.clone()),
        Value::CharArray(ca) if ca.rows == 1 => Ok(ca.data.iter().collect()),
        Value::StringArray(sa) => {
            if sa.data.len() == 1 {
                Ok(sa.data[0].clone())
            } else {
                Err(readmatrix_error(format!(
                    "readmatrix: {context} must be a scalar string array"
                )))
            }
        }
        _ => Err(readmatrix_error(format!(
            "readmatrix: expected {context} as a string scalar or character vector"
        ))),
    }
}

fn value_to_usize(value: &Value, context: &str) -> BuiltinResult<usize> {
    match value {
        Value::Int(i) => {
            let num = i.to_i64();
            if num < 0 {
                Err(readmatrix_error(format!(
                    "readmatrix: {context} must be a non-negative integer"
                )))
            } else {
                Ok(num as usize)
            }
        }
        Value::Num(n) => {
            if !n.is_finite() {
                return Err(readmatrix_error(format!(
                    "readmatrix: {context} must be a finite non-negative integer"
                )));
            }
            if *n < 0.0 {
                return Err(readmatrix_error(format!(
                    "readmatrix: {context} must be a non-negative integer"
                )));
            }
            if (n.round() - n).abs() > f64::EPSILON {
                return Err(readmatrix_error(format!(
                    "readmatrix: {context} must be an integer value"
                )));
            }
            Ok(n.round() as usize)
        }
        _ => Err(readmatrix_error(format!(
            "readmatrix: {context} must be provided as a numeric scalar"
        ))),
    }
}

fn value_to_f64(value: &Value, context: &str) -> BuiltinResult<f64> {
    match value {
        Value::Num(n) => {
            if n.is_finite() {
                Ok(*n)
            } else {
                Err(readmatrix_error(format!(
                    "readmatrix: {context} must be a finite numeric scalar"
                )))
            }
        }
        Value::Int(i) => Ok(i.to_f64()),
        Value::Tensor(t) => {
            if t.data.len() == 1 {
                let v = t.data[0];
                if v.is_finite() {
                    Ok(v)
                } else {
                    Err(readmatrix_error(format!(
                        "readmatrix: {context} must be a finite numeric scalar"
                    )))
                }
            } else {
                Err(readmatrix_error(format!(
                    "readmatrix: {context} must be a numeric scalar"
                )))
            }
        }
        _ => Err(readmatrix_error(format!(
            "readmatrix: {context} must be a numeric scalar"
        ))),
    }
}

async fn parse_treat_as_missing(value: &Value) -> BuiltinResult<Vec<String>> {
    let mut out = Vec::new();
    let mut stack = vec![value.clone()];
    while let Some(value) = stack.pop() {
        match value {
            Value::String(s) => out.push(s),
            Value::CharArray(ca) if ca.rows == 1 => out.push(ca.data.iter().collect()),
            Value::StringArray(sa) => out.extend(sa.data),
            Value::Num(n) => out.push(format_numeric_token(n)),
            Value::Int(i) => out.push(format!("{}", i.to_i64())),
            Value::Tensor(t) => {
                if t.data.len() == 1 {
                    out.push(format_numeric_token(t.data[0]));
                } else {
                    return Err(readmatrix_error(
                        "readmatrix: TreatAsMissing entries must be scalar values",
                    ));
                }
            }
            Value::Cell(cell) => {
                for handle in &cell.data {
                    let inner = unsafe { &*handle.as_raw() };
                    let gathered = gather_if_needed_async(inner)
                        .await
                        .map_err(map_control_flow)?;
                    stack.push(gathered);
                }
            }
            _ => {
                return Err(readmatrix_error(
                    "readmatrix: TreatAsMissing values must be strings or numeric scalars",
                ))
            }
        }
    }
    Ok(out)
}

fn format_numeric_token(value: f64) -> String {
    if value == 0.0 {
        "0".to_string()
    } else {
        format!("{}", value)
    }
}

#[derive(Clone)]
struct ReadMatrixOptions {
    delimiter: Option<Delimiter>,
    num_header_lines: usize,
    decimal_separator: char,
    thousands_separator: Option<char>,
    treat_as_missing: HashSet<String>,
    empty_value: Option<f64>,
    range: Option<RangeSpec>,
    output_template: OutputTemplate,
}

impl Default for ReadMatrixOptions {
    fn default() -> Self {
        Self {
            delimiter: None,
            num_header_lines: 0,
            decimal_separator: '.',
            thousands_separator: None,
            treat_as_missing: HashSet::new(),
            empty_value: None,
            range: None,
            output_template: OutputTemplate::Double,
        }
    }
}

impl ReadMatrixOptions {
    fn add_missing_token(&mut self, token: &str) {
        let normalized = normalize_missing_token(token);
        self.treat_as_missing.insert(normalized);
    }

    fn is_missing_token(&self, token: &str) -> bool {
        if self.treat_as_missing.is_empty() {
            return false;
        }
        let norm = normalize_missing_token(token);
        self.treat_as_missing.contains(&norm)
    }

    fn empty_value(&self) -> f64 {
        self.empty_value.unwrap_or(f64::NAN)
    }

    fn validate(&self) -> BuiltinResult<()> {
        if let Some(range) = &self.range {
            range.validate()?;
        }
        if let Some(sep) = self.thousands_separator {
            if sep == self.decimal_separator {
                return Err(readmatrix_error(
                    "readmatrix: DecimalSeparator and ThousandsSeparator must differ",
                ));
            }
        }
        Ok(())
    }

    fn set_output_type(&mut self, spec: &str) -> BuiltinResult<()> {
        if matches!(self.output_template, OutputTemplate::Like(_)) {
            return Err(readmatrix_error(
                "readmatrix: cannot combine 'Like' with OutputType",
            ));
        }
        if spec.eq_ignore_ascii_case("double") {
            self.output_template = OutputTemplate::Double;
            return Ok(());
        }
        if spec.eq_ignore_ascii_case("logical") {
            self.output_template = OutputTemplate::Logical;
            return Ok(());
        }
        Err(readmatrix_error(format!(
            "readmatrix: unsupported OutputType '{}'",
            spec
        )))
    }

    fn set_like(&mut self, proto: Value) -> BuiltinResult<()> {
        if matches!(self.output_template, OutputTemplate::Like(_)) {
            return Err(readmatrix_error(
                "readmatrix: multiple 'Like' specifications are not supported",
            ));
        }
        if !matches!(self.output_template, OutputTemplate::Double) {
            return Err(readmatrix_error(
                "readmatrix: cannot combine 'Like' with OutputType overrides",
            ));
        }
        self.output_template = OutputTemplate::Like(proto);
        Ok(())
    }
}

#[derive(Clone)]
enum Delimiter {
    Char(char),
    String(String),
    Whitespace,
}

#[derive(Clone)]
enum OutputTemplate {
    Double,
    Logical,
    Like(Value),
}

#[derive(Clone)]
struct RangeSpec {
    start_row: usize,
    start_col: usize,
    end_row: Option<usize>,
    end_col: Option<usize>,
}

impl RangeSpec {
    fn validate(&self) -> BuiltinResult<()> {
        if let Some(end_row) = self.end_row {
            if end_row < self.start_row {
                return Err(readmatrix_error(
                    "readmatrix: Range end row must be >= start row",
                ));
            }
        }
        if let Some(end_col) = self.end_col {
            if end_col < self.start_col {
                return Err(readmatrix_error(
                    "readmatrix: Range end column must be >= start column",
                ));
            }
        }
        Ok(())
    }
}

fn normalize_missing_token(token: &str) -> String {
    token.trim().to_ascii_lowercase()
}

fn parse_range(value: &Value) -> BuiltinResult<RangeSpec> {
    match value {
        Value::String(s) => parse_range_string(s),
        Value::CharArray(ca) if ca.rows == 1 => {
            let text: String = ca.data.iter().collect();
            parse_range_string(&text)
        }
        Value::StringArray(sa) => {
            if sa.data.len() == 1 {
                parse_range_string(&sa.data[0])
            } else {
                Err(readmatrix_error(
                    "readmatrix: Range string array inputs must be scalar",
                ))
            }
        }
        Value::Tensor(_) => parse_range_numeric(value),
        _ => Err(readmatrix_error(
            "readmatrix: Range must be provided as a string or numeric vector",
        )),
    }
}

fn parse_range_string(text: &str) -> BuiltinResult<RangeSpec> {
    let trimmed = text.trim();
    if trimmed.is_empty() {
        return Err(readmatrix_error("readmatrix: Range string cannot be empty"));
    }
    let parts: Vec<&str> = trimmed.split(':').collect();
    if parts.len() > 2 {
        return Err(readmatrix_error(format!(
            "readmatrix: invalid Range specification '{}'",
            text
        )));
    }
    let start = parse_cell_reference(parts[0])?;
    if start.col.is_none() {
        return Err(readmatrix_error(
            "readmatrix: Range must specify a starting column",
        ));
    }
    let end_ref = if parts.len() == 2 {
        Some(parse_cell_reference(parts[1])?)
    } else {
        None
    };
    if let Some(ref end) = end_ref {
        if end.col.is_none() {
            return Err(readmatrix_error(
                "readmatrix: Range end must include a column reference",
            ));
        }
    }
    let start_row = start.row.unwrap_or(0);
    let start_col = start.col.unwrap();
    let end_row = end_ref.as_ref().and_then(|r| r.row);
    let end_col = end_ref.as_ref().and_then(|r| r.col);
    Ok(RangeSpec {
        start_row,
        start_col,
        end_row,
        end_col,
    })
}

fn parse_range_numeric(value: &Value) -> BuiltinResult<RangeSpec> {
    let elements = match value {
        Value::Tensor(t) => t.data.clone(),
        _ => {
            return Err(readmatrix_error(
                "readmatrix: numeric Range must be provided as a vector with 2 or 4 elements",
            ))
        }
    };
    if elements.len() != 2 && elements.len() != 4 {
        return Err(readmatrix_error(
            "readmatrix: numeric Range must contain exactly 2 or 4 elements",
        ));
    }
    let mut indices = Vec::with_capacity(elements.len());
    for (idx, value) in elements.iter().enumerate() {
        let converted = positive_index(*value, idx)?;
        indices.push(converted);
    }
    let start_row = indices[0];
    let start_col = indices[1];
    let (end_row, end_col) = if indices.len() == 4 {
        (Some(indices[2]), Some(indices[3]))
    } else {
        (None, None)
    };
    Ok(RangeSpec {
        start_row,
        start_col,
        end_row,
        end_col,
    })
}

fn positive_index(value: f64, position: usize) -> BuiltinResult<usize> {
    if !value.is_finite() {
        return Err(readmatrix_error("readmatrix: Range indices must be finite"));
    }
    if value < 1.0 {
        return Err(readmatrix_error("readmatrix: Range indices must be >= 1"));
    }
    let rounded = value.round();
    if (rounded - value).abs() > f64::EPSILON {
        return Err(readmatrix_error(
            "readmatrix: Range indices must be integers",
        ));
    }
    let zero_based = (rounded as i64) - 1;
    if zero_based < 0 {
        return Err(readmatrix_error("readmatrix: Range indices must be >= 1"));
    }
    usize::try_from(zero_based).map_err(|_| {
        readmatrix_error(format!(
            "readmatrix: Range index {} is too large to fit in usize",
            position + 1
        ))
    })
}

#[derive(Clone, Copy)]
struct CellReference {
    row: Option<usize>,
    col: Option<usize>,
}

fn parse_cell_reference(token: &str) -> BuiltinResult<CellReference> {
    let mut letters = String::new();
    let mut digits = String::new();
    for ch in token.trim().chars() {
        if ch == '$' {
            continue;
        }
        if ch.is_ascii_alphabetic() {
            letters.push(ch.to_ascii_uppercase());
        } else if ch.is_ascii_digit() {
            digits.push(ch);
        } else {
            return Err(readmatrix_error(format!(
                "readmatrix: invalid Range component '{}'",
                token
            )));
        }
    }
    if letters.is_empty() && digits.is_empty() {
        return Err(readmatrix_error(
            "readmatrix: Range references cannot be empty",
        ));
    }
    let col = if letters.is_empty() {
        None
    } else {
        Some(column_index_from_letters(&letters)?)
    };
    let row = if digits.is_empty() {
        None
    } else {
        let parsed = digits.parse::<usize>().map_err(|_| {
            readmatrix_error(format!(
                "readmatrix: invalid row index '{}' in Range component '{}'",
                digits, token
            ))
        })?;
        if parsed == 0 {
            return Err(readmatrix_error("readmatrix: Range rows must be >= 1"));
        }
        Some(parsed - 1)
    };
    Ok(CellReference { row, col })
}

fn column_index_from_letters(letters: &str) -> BuiltinResult<usize> {
    let mut value: usize = 0;
    for ch in letters.chars() {
        if !ch.is_ascii_uppercase() {
            return Err(readmatrix_error(format!(
                "readmatrix: invalid column designator '{}' in Range",
                letters
            )));
        }
        let digit = (ch as u8 - b'A' + 1) as usize;
        value = value
            .checked_mul(26)
            .and_then(|v| v.checked_add(digit))
            .ok_or_else(|| readmatrix_error("readmatrix: Range column index overflowed"))?;
    }
    value
        .checked_sub(1)
        .ok_or_else(|| readmatrix_error("readmatrix: Range column index underflowed"))
}

fn apply_range(
    rows: &[Vec<f64>],
    max_cols: usize,
    range: &RangeSpec,
    default_fill: f64,
) -> (Vec<Vec<f64>>, usize) {
    if rows.is_empty() || max_cols == 0 {
        return (Vec::new(), 0);
    }
    if range.start_row >= rows.len() || range.start_col >= max_cols {
        return (Vec::new(), 0);
    }
    let last_row = rows.len().saturating_sub(1);
    let mut end_row = range.end_row.unwrap_or(last_row);
    if end_row > last_row {
        end_row = last_row;
    }
    if end_row < range.start_row {
        return (Vec::new(), 0);
    }

    let last_col = max_cols.saturating_sub(1);
    let mut end_col = range.end_col.unwrap_or(last_col);
    if end_col > last_col {
        end_col = last_col;
    }
    if end_col < range.start_col {
        return (Vec::new(), 0);
    }

    let mut subset = Vec::new();
    let mut subset_max_cols = 0usize;
    for row_idx in range.start_row..=end_row {
        if row_idx >= rows.len() {
            break;
        }
        let row = &rows[row_idx];
        let mut extracted = Vec::with_capacity(end_col - range.start_col + 1);
        for col_idx in range.start_col..=end_col {
            if col_idx >= max_cols {
                break;
            }
            let value = row.get(col_idx).copied().unwrap_or(default_fill);
            extracted.push(value);
        }
        subset_max_cols = subset_max_cols.max(extracted.len());
        subset.push(extracted);
    }

    if subset_max_cols == 0 {
        (Vec::new(), 0)
    } else {
        (subset, subset_max_cols)
    }
}

fn finalize_output(tensor: Tensor, options: &ReadMatrixOptions) -> BuiltinResult<Value> {
    match &options.output_template {
        OutputTemplate::Double => Ok(Value::Tensor(tensor)),
        OutputTemplate::Logical => tensor_to_logical(tensor),
        OutputTemplate::Like(proto) => finalize_like(tensor, proto),
    }
}

fn tensor_to_logical(tensor: Tensor) -> BuiltinResult<Value> {
    let mut data = Vec::with_capacity(tensor.data.len());
    for value in &tensor.data {
        let bit = if *value == 0.0 { 0 } else { 1 };
        data.push(bit);
    }
    let logical = LogicalArray::new(data, tensor.shape.clone())
        .map_err(|e| readmatrix_error(format!("readmatrix: {e}")))?;
    Ok(Value::LogicalArray(logical))
}

fn finalize_like(tensor: Tensor, proto: &Value) -> BuiltinResult<Value> {
    match proto {
        Value::LogicalArray(_) | Value::Bool(_) => tensor_to_logical(tensor),
        Value::GpuTensor(handle) => tensor_to_gpu(tensor, handle),
        Value::Tensor(_) | Value::Num(_) | Value::Int(_) => Ok(Value::Tensor(tensor)),
        Value::ComplexTensor(_) | Value::Complex(_, _) => Ok(Value::Tensor(tensor)),
        Value::CharArray(_) | Value::String(_) | Value::StringArray(_) => Ok(Value::Tensor(tensor)),
        Value::Cell(_) => Ok(Value::Tensor(tensor)),
        _ => Ok(Value::Tensor(tensor)),
    }
}

fn tensor_to_gpu(
    tensor: Tensor,
    _handle: &runmat_accelerate_api::GpuTensorHandle,
) -> BuiltinResult<Value> {
    if let Some(provider) = runmat_accelerate_api::provider() {
        let view = HostTensorView {
            data: &tensor.data,
            shape: &tensor.shape,
        };
        if let Ok(uploaded) = provider.upload(&view) {
            return Ok(Value::GpuTensor(uploaded));
        }
    }
    Ok(Value::Tensor(tensor))
}

fn read_numeric_matrix(path: &Path, options: &ReadMatrixOptions) -> BuiltinResult<Tensor> {
    let file = File::open(path).map_err(|err| {
        readmatrix_error_with_source(
            format!("readmatrix: unable to read '{}': {err}", path.display()),
            err,
        )
    })?;
    let reader = BufReader::new(file);
    let mut data_lines: Vec<(usize, String)> = Vec::new();
    for (idx, line_result) in reader.lines().enumerate() {
        let line_number = idx + 1;
        let line = line_result.map_err(|err| {
            readmatrix_error_with_source(
                format!("readmatrix: error reading '{}': {err}", path.display()),
                err,
            )
        })?;
        let cleaned = line.trim_end_matches('\r');
        if line_number <= options.num_header_lines {
            continue;
        }
        if cleaned.trim().is_empty() {
            continue;
        }
        data_lines.push((line_number, cleaned.to_string()));
    }

    if data_lines.is_empty() {
        return Tensor::new(Vec::new(), vec![0, 0])
            .map_err(|e| readmatrix_error(format!("readmatrix: {e}")));
    }

    if let Some((_, first_line)) = data_lines.first_mut() {
        if first_line.starts_with('\u{FEFF}') {
            let stripped = first_line.trim_start_matches('\u{FEFF}').to_string();
            *first_line = stripped;
        }
    }

    let delimiter = options
        .delimiter
        .clone()
        .or_else(|| detect_delimiter(&data_lines))
        .unwrap_or(Delimiter::Whitespace);

    let mut rows: Vec<Vec<f64>> = Vec::new();
    let mut max_cols = 0usize;

    for (line_number, text) in &data_lines {
        let fields = split_fields(text, &delimiter);
        if fields.is_empty() {
            continue;
        }
        let mut row = Vec::with_capacity(fields.len());
        for (index, field) in fields.iter().enumerate() {
            let value = parse_numeric_token(field, options, *line_number, index + 1)?;
            row.push(value);
        }
        if row.len() > max_cols {
            max_cols = row.len();
        }
        rows.push(row);
    }

    if rows.is_empty() {
        return Tensor::new(Vec::new(), vec![0, 0])
            .map_err(|e| readmatrix_error(format!("readmatrix: {e}")));
    }

    let default_fill = options.empty_value();
    if let Some(range) = &options.range {
        let (subset_rows, subset_cols) = apply_range(&rows, max_cols, range, default_fill);
        rows = subset_rows;
        max_cols = subset_cols;
    }

    if rows.is_empty() || max_cols == 0 {
        return Tensor::new(Vec::new(), vec![0, 0])
            .map_err(|e| readmatrix_error(format!("readmatrix: {e}")));
    }
    let row_count = rows.len();
    let mut data = vec![default_fill; row_count * max_cols];

    for (row_index, row) in rows.iter().enumerate() {
        for col_index in 0..max_cols {
            let value = row.get(col_index).copied().unwrap_or(default_fill);
            data[col_index * row_count + row_index] = value;
        }
    }

    Tensor::new(data, vec![row_count, max_cols])
        .map_err(|e| readmatrix_error(format!("readmatrix: {e}")))
}

fn detect_delimiter(lines: &[(usize, String)]) -> Option<Delimiter> {
    if lines.is_empty() {
        return None;
    }
    let sample: Vec<&str> = lines
        .iter()
        .take(32)
        .map(|(_, line)| line.as_str())
        .collect();
    let candidates = [',', '\t', ';', '|'];
    let mut best: Option<(f64, Delimiter)> = None;

    for candidate in candidates {
        let mut counts = Vec::new();
        for line in &sample {
            if !line.contains(candidate) {
                continue;
            }
            let fields = line.split(candidate).count();
            if fields >= 2 {
                counts.push(fields);
            }
        }
        if counts.is_empty() {
            continue;
        }
        let average = counts.iter().copied().sum::<usize>() as f64 / counts.len() as f64;
        if average < 2.0 {
            continue;
        }
        if best
            .as_ref()
            .map(|(best_avg, _)| average > *best_avg)
            .unwrap_or(true)
        {
            best = Some((average, Delimiter::Char(candidate)));
        }
    }

    if let Some((_, delimiter)) = best {
        return Some(delimiter);
    }

    let mut whitespace_hits = 0usize;
    for line in &sample {
        if line.split_whitespace().count() > 1 {
            whitespace_hits += 1;
        }
    }
    if whitespace_hits > 0 {
        Some(Delimiter::Whitespace)
    } else {
        None
    }
}

fn split_fields(line: &str, delimiter: &Delimiter) -> Vec<String> {
    match delimiter {
        Delimiter::Char(ch) => split_with_char_delim(line, *ch),
        Delimiter::String(pattern) => line.split(pattern).map(|s| s.to_string()).collect(),
        Delimiter::Whitespace => line.split_whitespace().map(|s| s.to_string()).collect(),
    }
}

fn split_with_char_delim(line: &str, delimiter: char) -> Vec<String> {
    let mut fields = Vec::new();
    let mut current = String::new();
    let mut in_quotes = false;
    let mut chars = line.chars().peekable();
    while let Some(ch) = chars.next() {
        if ch == '"' {
            if in_quotes && chars.peek() == Some(&'"') {
                current.push('"');
                chars.next();
            } else {
                in_quotes = !in_quotes;
            }
            continue;
        }
        if ch == delimiter && !in_quotes {
            fields.push(current.clone());
            current.clear();
        } else {
            current.push(ch);
        }
    }
    fields.push(current);
    fields
}

fn parse_numeric_token(
    token: &str,
    options: &ReadMatrixOptions,
    line_number: usize,
    column_number: usize,
) -> BuiltinResult<f64> {
    let trimmed = token.trim();
    if trimmed.is_empty() {
        return Ok(options.empty_value());
    }
    let unquoted = unquote(trimmed);
    let inner = unquoted.trim();
    if inner.is_empty() {
        return Ok(options.empty_value());
    }
    if options.is_missing_token(inner) {
        return Ok(f64::NAN);
    }
    let normalized = normalize_numeric_token(inner, options);
    if normalized.is_empty() {
        return Ok(options.empty_value());
    }
    let lower = normalized.to_ascii_lowercase();
    if lower == "nan" {
        return Ok(f64::NAN);
    }
    if matches!(lower.as_str(), "inf" | "+inf" | "infinity" | "+infinity") {
        return Ok(f64::INFINITY);
    }
    if matches!(lower.as_str(), "-inf" | "-infinity") {
        return Ok(f64::NEG_INFINITY);
    }
    normalized.parse::<f64>().map_err(|_| {
        readmatrix_error(format!(
            "readmatrix: unable to parse numeric value '{}' on line {} column {}",
            inner, line_number, column_number
        ))
    })
}

fn normalize_numeric_token(token: &str, options: &ReadMatrixOptions) -> String {
    let mut text = token.to_string();
    if let Some(thousands) = options.thousands_separator {
        if thousands != options.decimal_separator {
            text = text.chars().filter(|ch| *ch != thousands).collect();
        }
    }
    if options.decimal_separator != '.' {
        text = text.replace(options.decimal_separator, ".");
    }
    text
}

fn unquote(token: &str) -> &str {
    if token.len() >= 2 {
        let bytes = token.as_bytes();
        if (bytes[0] == b'"' && bytes[token.len() - 1] == b'"')
            || (bytes[0] == b'\'' && bytes[token.len() - 1] == b'\'')
        {
            return &token[1..token.len() - 1];
        }
    }
    token
}

fn resolve_path(value: &Value) -> BuiltinResult<PathBuf> {
    match value {
        Value::String(s) => normalize_path(s),
        Value::CharArray(ca) if ca.rows == 1 => {
            let text: String = ca.data.iter().collect();
            normalize_path(&text)
        }
        Value::CharArray(_) => Err(readmatrix_error(
            "readmatrix: expected a 1-by-N character vector for the file name",
        )),
        Value::StringArray(sa) => {
            if sa.data.len() == 1 {
                normalize_path(&sa.data[0])
            } else {
                Err(readmatrix_error(
                    "readmatrix: string array inputs must be scalar",
                ))
            }
        }
        other => Err(readmatrix_error(format!(
            "readmatrix: expected filename as string scalar or character vector, got {other:?}"
        ))),
    }
}

fn normalize_path(raw: &str) -> BuiltinResult<PathBuf> {
    if raw.is_empty() {
        return Err(readmatrix_error("readmatrix: filename must not be empty"));
    }
    Ok(Path::new(raw).to_path_buf())
}

#[cfg(test)]
pub(crate) mod tests {
    use super::*;
    use futures::executor::block_on;
    use runmat_time::unix_timestamp_ms;
    use std::fs;

    use crate::builtins::common::test_support;
    use runmat_accelerate_api::HostTensorView;
    use runmat_builtins::{CharArray, IntValue, LogicalArray, StringArray, Tensor};

    fn unique_path(prefix: &str) -> PathBuf {
        let millis = unix_timestamp_ms();
        let mut path = std::env::temp_dir();
        path.push(format!("runmat_{prefix}_{}_{}", std::process::id(), millis));
        path
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_reads_csv_data() {
        let path = unique_path("readmatrix_csv");
        fs::write(&path, "1,2,3\n4,5,6\n").expect("write sample file");
        let result = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            Vec::new(),
        ))
        .expect("readmatrix");
        match result {
            Value::Tensor(t) => {
                assert_eq!(t.shape, vec![2, 3]);
                assert_eq!(t.data, vec![1.0, 4.0, 2.0, 5.0, 3.0, 6.0]);
            }
            other => panic!("expected tensor result, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_skips_header_lines() {
        let path = unique_path("readmatrix_header");
        fs::write(&path, "time,value\n0,10\n1,12\n").expect("write sample file");
        let args = vec![Value::from("NumHeaderLines"), Value::Int(IntValue::I32(1))];
        let result = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            args,
        ))
        .expect("readmatrix");
        match result {
            Value::Tensor(t) => {
                assert_eq!(t.shape, vec![2, 2]);
                assert_eq!(t.data, vec![0.0, 1.0, 10.0, 12.0]);
            }
            other => panic!("expected tensor result, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_respects_delimiter_option() {
        let path = unique_path("readmatrix_tab");
        fs::write(&path, "1\t2\t3\n4\t5\t6\n").expect("write sample file");
        let args = vec![Value::from("Delimiter"), Value::from("tab")];
        let result = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            args,
        ))
        .expect("readmatrix");
        match result {
            Value::Tensor(t) => {
                assert_eq!(t.shape, vec![2, 3]);
                assert_eq!(t.data, vec![1.0, 4.0, 2.0, 5.0, 3.0, 6.0]);
            }
            other => panic!("expected tensor result, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_respects_range_string() {
        let path = unique_path("readmatrix_range_string");
        fs::write(&path, "11,12,13\n21,22,23\n31,32,33\n").expect("write sample file");
        let args = vec![Value::from("Range"), Value::from("B2:C3")];
        let result = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            args,
        ))
        .expect("readmatrix");
        match result {
            Value::Tensor(t) => {
                assert_eq!(t.shape, vec![2, 2]);
                assert_eq!(t.data, vec![22.0, 32.0, 23.0, 33.0]);
            }
            other => panic!("expected tensor result, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_respects_range_numeric_vector() {
        let path = unique_path("readmatrix_range_numeric");
        fs::write(&path, "11,12,13\n21,22,23\n31,32,33\n").expect("write sample file");
        let range = Tensor::new(vec![2.0, 2.0, 3.0, 3.0], vec![1, 4]).expect("range tensor");
        let args = vec![Value::from("Range"), Value::Tensor(range)];
        let result = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            args,
        ))
        .expect("readmatrix");
        match result {
            Value::Tensor(t) => {
                assert_eq!(t.shape, vec![2, 2]);
                assert_eq!(t.data, vec![22.0, 32.0, 23.0, 33.0]);
            }
            other => panic!("expected tensor result, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_treats_custom_missing_tokens() {
        let path = unique_path("readmatrix_missing");
        fs::write(&path, "1,NA,3\nNA,5,missing\n").expect("write file");
        let strings = StringArray::new(vec!["NA".to_string(), "missing".to_string()], vec![1, 2])
            .expect("string array");
        let args = vec![Value::from("TreatAsMissing"), Value::StringArray(strings)];
        let result = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            args,
        ))
        .expect("readmatrix");
        match result {
            Value::Tensor(t) => {
                assert_eq!(t.shape, vec![2, 3]);
                assert!(t.data[1].is_nan()); // column 1, row 2
                assert!(t.data[2].is_nan()); // column 2, row 1
                assert!(t.data[5].is_nan()); // column 3, row 2
            }
            other => panic!("expected tensor result, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_uses_decimal_and_thousands_separators() {
        let path = unique_path("readmatrix_decimal");
        fs::write(&path, "1.234,56;7.890,12\n").expect("write sample file");
        let args = vec![
            Value::from("Delimiter"),
            Value::from(";"),
            Value::from("DecimalSeparator"),
            Value::from(","),
            Value::from("ThousandsSeparator"),
            Value::from("."),
        ];
        let result = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            args,
        ))
        .expect("readmatrix");
        match result {
            Value::Tensor(t) => {
                assert_eq!(t.shape, vec![1, 2]);
                assert!((t.data[0] - 1234.56).abs() < 1e-9);
                assert!((t.data[1] - 7890.12).abs() < 1e-9);
            }
            other => panic!("expected tensor result, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_applies_empty_value() {
        let path = unique_path("readmatrix_empty_value");
        fs::write(&path, "1,,3\n4,,6\n").expect("write sample file");
        let args = vec![Value::from("EmptyValue"), Value::Num(0.0)];
        let result = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            args,
        ))
        .expect("readmatrix");
        match result {
            Value::Tensor(t) => {
                assert_eq!(t.shape, vec![2, 3]);
                assert_eq!(t.data, vec![1.0, 4.0, 0.0, 0.0, 3.0, 6.0]);
            }
            other => panic!("expected tensor result, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_accepts_struct_options() {
        let path = unique_path("readmatrix_struct_opts");
        fs::write(&path, "header1,header2\n9,10\n11,12\n").expect("write sample file");
        let mut options_struct = runmat_builtins::StructValue::new();
        options_struct
            .fields
            .insert("Delimiter".to_string(), Value::from(","));
        options_struct
            .fields
            .insert("NumHeaderLines".to_string(), Value::Int(IntValue::I32(1)));
        let args = vec![Value::Struct(options_struct)];
        let result = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            args,
        ))
        .expect("readmatrix");
        match result {
            Value::Tensor(t) => {
                assert_eq!(t.shape, vec![2, 2]);
                assert_eq!(t.data, vec![9.0, 11.0, 10.0, 12.0]);
            }
            other => panic!("expected tensor result, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_errors_on_non_numeric_field() {
        let path = unique_path("readmatrix_error");
        fs::write(&path, "1,abc,3\n").expect("write sample file");
        let err = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            Vec::new(),
        ))
        .expect_err("readmatrix should fail");
        let message = err.message().to_string();
        assert!(
            message.contains("unable to parse numeric value"),
            "unexpected error message: {message}"
        );
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_returns_empty_on_no_data() {
        let path = unique_path("readmatrix_empty");
        File::create(&path).expect("create file");
        let result = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            Vec::new(),
        ))
        .expect("readmatrix");
        match result {
            Value::Tensor(t) => {
                assert_eq!(t.shape, vec![0, 0]);
                assert!(t.data.is_empty());
            }
            other => panic!("expected tensor result, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_output_type_logical() {
        let path = unique_path("readmatrix_output_logical");
        fs::write(&path, "0,1,-3\nNaN,0,5\n").expect("write sample file");
        let args = vec![Value::from("OutputType"), Value::from("logical")];
        let result = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            args,
        ))
        .expect("readmatrix");
        match result {
            Value::LogicalArray(arr) => {
                assert_eq!(arr.shape, vec![2, 3]);
                assert_eq!(arr.data, vec![0, 1, 1, 0, 1, 1]);
            }
            other => panic!("expected logical array, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_like_logical_proto() {
        let path = unique_path("readmatrix_like_logical");
        fs::write(&path, "1,0\n0,5\n").expect("write sample file");
        let proto = LogicalArray::new(vec![1], vec![1]).expect("logical prototype");
        let args = vec![Value::from("Like"), Value::LogicalArray(proto)];
        let result = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            args,
        ))
        .expect("readmatrix");
        match result {
            Value::LogicalArray(arr) => {
                assert_eq!(arr.shape, vec![2, 2]);
                assert_eq!(arr.data, vec![1, 0, 0, 1]);
            }
            other => panic!("expected logical array, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_like_gpu_proto() {
        test_support::with_test_provider(|provider| {
            let path = unique_path("readmatrix_like_gpu");
            fs::write(&path, "1,2\n3,4\n").expect("write sample file");
            let proto_tensor = Tensor::new(vec![0.0, 0.0], vec![1, 2]).expect("tensor");
            let view = HostTensorView {
                data: &proto_tensor.data,
                shape: &proto_tensor.shape,
            };
            let handle = provider.upload(&view).expect("upload prototype");
            let args = vec![Value::from("Like"), Value::GpuTensor(handle.clone())];
            let result = block_on(readmatrix_builtin(
                Value::from(path.to_string_lossy().to_string()),
                args,
            ))
            .expect("readmatrix");
            assert!(
                matches!(result, Value::GpuTensor(_)),
                "expected GPU tensor result, got {result:?}"
            );
            let gathered = test_support::gather(result).expect("gather result");
            assert_eq!(gathered.shape, vec![2, 2]);
            assert_eq!(gathered.data, vec![1.0, 3.0, 2.0, 4.0]);
            let _ = fs::remove_file(&path);
        });
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_accepts_character_vector_path() {
        let path = unique_path("readmatrix_char_path");
        fs::write(&path, "1 2 3\n").expect("write sample file");
        let text = path.to_string_lossy().to_string();
        let chars: Vec<char> = text.chars().collect();
        let len = chars.len();
        let char_array = CharArray::new(chars, 1, len).expect("char array");
        let result = block_on(readmatrix_builtin(Value::CharArray(char_array), Vec::new()))
            .expect("readmatrix");
        match result {
            Value::Tensor(t) => {
                assert_eq!(t.shape, vec![1, 3]);
                assert_eq!(t.data, vec![1.0, 2.0, 3.0]);
            }
            other => panic!("expected tensor result, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_handles_quoted_fields() {
        let path = unique_path("readmatrix_quotes");
        fs::write(&path, "\"1\",\"2\",\"3\"\n").expect("write sample file");
        let result = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            Vec::new(),
        ))
        .expect("readmatrix");
        match result {
            Value::Tensor(t) => {
                assert_eq!(t.shape, vec![1, 3]);
                assert_eq!(t.data, vec![1.0, 2.0, 3.0]);
            }
            other => panic!("expected tensor result, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_preserves_negative_infinity() {
        let path = unique_path("readmatrix_infinity");
        fs::write(&path, "-Inf,Inf,NaN\n").expect("write sample file");
        let result = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            Vec::new(),
        ))
        .expect("readmatrix");
        match result {
            Value::Tensor(t) => {
                assert_eq!(t.shape, vec![1, 3]);
                assert!(t.data[0].is_infinite() && t.data[0].is_sign_negative());
                assert!(t.data[1].is_infinite() && t.data[1].is_sign_positive());
                assert!(t.data[2].is_nan());
            }
            other => panic!("expected tensor result, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }

    #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
    #[test]
    fn readmatrix_supports_whitespace_delimiter() {
        let path = unique_path("readmatrix_whitespace");
        fs::write(&path, "1 2 3\n4 5 6\n").expect("write sample file");
        let result = block_on(readmatrix_builtin(
            Value::from(path.to_string_lossy().to_string()),
            Vec::new(),
        ))
        .expect("readmatrix");
        match result {
            Value::Tensor(t) => {
                assert_eq!(t.shape, vec![2, 3]);
                assert_eq!(t.data, vec![1.0, 4.0, 2.0, 5.0, 3.0, 6.0]);
            }
            other => panic!("expected tensor result, got {other:?}"),
        }
        let _ = fs::remove_file(&path);
    }
}