anamnesis 0.4.3

// SPDX-License-Identifier: MIT OR Apache-2.0

use std::path::PathBuf;
use std::process;

use clap::{Parser, Subcommand};

use anamnesis::{format_bytes, parse, InspectInfo, TargetDtype};

/// Parse any format, recover any precision.
#[derive(Parser)]
#[command(name = "anamnesis", version, about)]
struct Cli {
    #[command(subcommand)]
    command: Commands,
}

#[derive(Subcommand)]
enum Commands {
    /// Parse and summarize a model file.
    Parse {
        /// Path to the model file (`.safetensors`, `.pth`, `.pt`, `.bin`, `.gguf`).
        path: PathBuf,
    },
    /// Inspect format, tensor counts, and size estimates.
    #[command(alias = "info")]
    Inspect {
        /// Path to the model file.
        path: PathBuf,
    },
    /// Dequantize (recover precision) or convert to a target format.
    #[command(alias = "dequantize")]
    Remember {
        /// Path to the input model file.
        path: PathBuf,
        /// Target dtype (currently only `bf16`) or `safetensors` for `.pth`/`.gguf` conversion.
        #[arg(long, default_value = "bf16")]
        to: String,
        /// Output file path (derived from input if omitted).
        #[arg(long, short)]
        output: Option<PathBuf>,
    },
}

// ---------------------------------------------------------------------------
// Format detection
// ---------------------------------------------------------------------------

/// Detected model file format.
enum Format {
    Safetensors,
    #[cfg(feature = "pth")]
    Pth,
    #[cfg(feature = "npz")]
    Npz,
    #[cfg(feature = "gguf")]
    Gguf,
}

/// Builds an `AnamnesisError::Unsupported` explaining that the input
/// matched a format whose Cargo feature is disabled in the current build.
///
/// `format_name` is the user-facing format name (e.g., `"PyTorch"`,
/// `"GGUF"`). `kind` describes how the format was detected (extension or
/// magic bytes). `feature_flag` is the Cargo feature that enables the
/// corresponding parser.
///
/// Compiled only when at least one of `pth`, `npz`, `gguf` is **not**
/// enabled — when the binary is built with all three the helper has no
/// callers and triggers `dead_code`.
#[cfg(not(all(feature = "pth", feature = "npz", feature = "gguf")))]
fn missing_feature_err(
    format_name: &str,
    kind: &str,
    feature_flag: &str,
) -> anamnesis::AnamnesisError {
    anamnesis::AnamnesisError::Unsupported {
        format: format_name.into(),
        detail: format!(
            "input is {kind} but the `{feature_flag}` Cargo feature is not enabled in this \
             build — rebuild with `cargo install anamnesis --features cli,{feature_flag}` \
             (or `cargo build --features cli,{feature_flag}`) to add support"
        ),
    }
}

/// Detects the model format from file extension and magic bytes.
///
/// `.safetensors` → `Safetensors`. `.pth`/`.pt` → `Pth`. `.npz` → `Npz`.
/// `.gguf` → `Gguf`. `.bin` → check ZIP magic (`PK\x03\x04`) for
/// `PyTorch`, then `GGUF` magic. Unknown extensions try `GGUF` magic
/// before defaulting to `Safetensors`.
///
/// # Errors
///
/// Returns `AnamnesisError::Unsupported` when the input matches a
/// format whose Cargo feature (`pth`, `npz`, or `gguf`) is not enabled
/// in this build. This replaces the previous silent fall-through to
/// `Safetensors`, which produced cryptic downstream errors when users
/// pointed the CLI at a `.pth` / `.npz` / `.gguf` file built without
/// the matching feature.
// `clippy::unnecessary_wraps`: when all three of `pth`, `npz`, `gguf`
// are enabled every branch is `Ok(_)` and clippy can't see that other
// feature combinations make the wrap load-bearing. This allow keeps
// the all-features clippy build clean without weakening detection
// elsewhere.
#[allow(clippy::unnecessary_wraps)]
fn detect_format(path: &std::path::Path) -> anamnesis::Result<Format> {
    let ext = path
        .extension()
        .and_then(|e| e.to_str())
        .unwrap_or("")
        .to_ascii_lowercase();

    match ext.as_str() {
        "safetensors" => Ok(Format::Safetensors),
        "pth" | "pt" => {
            #[cfg(feature = "pth")]
            {
                Ok(Format::Pth)
            }
            #[cfg(not(feature = "pth"))]
            {
                Err(missing_feature_err("PyTorch", "a .pth/.pt file", "pth"))
            }
        }
        "npz" => {
            #[cfg(feature = "npz")]
            {
                Ok(Format::Npz)
            }
            #[cfg(not(feature = "npz"))]
            {
                Err(missing_feature_err("NumPy NPZ", "a .npz file", "npz"))
            }
        }
        "gguf" => {
            #[cfg(feature = "gguf")]
            {
                Ok(Format::Gguf)
            }
            #[cfg(not(feature = "gguf"))]
            {
                Err(missing_feature_err("GGUF", "a .gguf file", "gguf"))
            }
        }
        "bin" => {
            if has_zip_magic(path) {
                #[cfg(feature = "pth")]
                {
                    return Ok(Format::Pth);
                }
                #[cfg(not(feature = "pth"))]
                {
                    return Err(missing_feature_err(
                        "PyTorch",
                        "a .bin file with ZIP magic (PyTorch pickle archive)",
                        "pth",
                    ));
                }
            }
            if has_gguf_magic(path) {
                #[cfg(feature = "gguf")]
                {
                    return Ok(Format::Gguf);
                }
                #[cfg(not(feature = "gguf"))]
                {
                    return Err(missing_feature_err(
                        "GGUF",
                        "a .bin file with GGUF magic",
                        "gguf",
                    ));
                }
            }
            Ok(Format::Safetensors)
        }
        _ => {
            if has_gguf_magic(path) {
                #[cfg(feature = "gguf")]
                {
                    return Ok(Format::Gguf);
                }
                #[cfg(not(feature = "gguf"))]
                {
                    return Err(missing_feature_err(
                        "GGUF",
                        "a file whose first four bytes are the GGUF magic",
                        "gguf",
                    ));
                }
            }
            Ok(Format::Safetensors)
        }
    }
}

/// Returns `true` if the file starts with the ZIP local header magic
/// `PK\x03\x04`. Reads only 4 bytes — does not load the file into memory.
///
/// Always available (no feature gate) because [`detect_format`] needs to
/// detect `.bin`-extension `PyTorch` archives even in builds where the
/// `pth` feature is disabled, so it can return a helpful "feature not
/// enabled" error instead of silently misrouting to the safetensors
/// parser.
fn has_zip_magic(path: &std::path::Path) -> bool {
    let mut buf = [0u8; 4];
    std::fs::File::open(path)
        .and_then(|mut f| {
            use std::io::Read;
            f.read_exact(&mut buf)
        })
        .is_ok_and(|()| buf == *b"PK\x03\x04")
}

/// Returns `true` if the file starts with the `GGUF` magic bytes
/// (`"GGUF"`). Reads only 4 bytes — does not load the file into memory.
///
/// Always available (no feature gate) for the same reason as
/// [`has_zip_magic`]: feature-disabled builds still need to recognise
/// GGUF files and produce a helpful error rather than silently falling
/// through to the safetensors parser.
fn has_gguf_magic(path: &std::path::Path) -> bool {
    let mut buf = [0u8; 4];
    std::fs::File::open(path)
        .and_then(|mut f| {
            use std::io::Read;
            f.read_exact(&mut buf)
        })
        .is_ok_and(|()| buf == *b"GGUF")
}

// ---------------------------------------------------------------------------
// Subcommand runners
// ---------------------------------------------------------------------------

fn run() -> anamnesis::Result<()> {
    let cli = Cli::parse();

    match cli.command {
        Commands::Parse { path } => run_parse(&path),
        Commands::Inspect { path } => run_inspect(&path),
        Commands::Remember { path, to, output } => run_remember(&path, &to, output.as_deref()),
    }
}

fn run_parse(path: &std::path::Path) -> anamnesis::Result<()> {
    match detect_format(path)? {
        Format::Safetensors => run_parse_safetensors(path),
        #[cfg(feature = "pth")]
        Format::Pth => run_parse_pth(path),
        #[cfg(feature = "npz")]
        Format::Npz => run_parse_npz(path),
        #[cfg(feature = "gguf")]
        Format::Gguf => run_parse_gguf(path),
    }
}

fn run_parse_safetensors(path: &std::path::Path) -> anamnesis::Result<()> {
    let model = parse(path)?;
    let info = InspectInfo::from(&model.header);
    let total = model.header.tensors.len();

    println!("{total} tensors parsed");

    let quantized = model.header.quantized_count();
    if quantized > 0 {
        println!("  {quantized:>3} quantized   {}", model.header.scheme);
    }

    let scales = model.header.scale_count();
    if scales > 0 {
        let mut dtypes: Vec<String> = Vec::new();
        for entry in model.header.scale_tensors() {
            let s = entry.dtype.to_string();
            if !dtypes.contains(&s) {
                dtypes.push(s);
            }
        }
        let dtype_list = dtypes.join(", ");
        println!("  {scales:>3} scale       {dtype_list}");
    }

    let zeropoints = model.header.zeropoint_count();
    if zeropoints > 0 {
        println!("  {zeropoints:>3} zero-point  I32 (packed)");
    }

    let group_indices = model.header.group_index_count();
    if group_indices > 0 {
        println!("  {group_indices:>3} g_idx       I32 (activation-order)");
    }

    let passthrough = model.header.passthrough_count();
    if passthrough > 0 {
        // Collect passthrough dtype summary.
        let mut dtypes: Vec<String> = Vec::new();
        for entry in model.header.passthrough_tensors() {
            let s = entry.dtype.to_string();
            if !dtypes.contains(&s) {
                dtypes.push(s);
            }
        }
        let dtype_list = dtypes.join(", ");
        println!("  {passthrough:>3} passthrough {dtype_list} (norms, embeddings, lm_head)");
    }

    println!("File: {}", format_bytes(info.current_size));
    Ok(())
}

#[cfg(feature = "pth")]
fn run_parse_pth(path: &std::path::Path) -> anamnesis::Result<()> {
    let parsed = anamnesis::parse_pth(path)?;
    let info = parsed.inspect();
    // Use tensor_info() (metadata only) instead of tensors() — avoids
    // materializing tensor data just for the display path.
    let tensor_info = parsed.tensor_info();

    println!(
        "Parsed {} (PyTorch state_dict)",
        path.file_name()
            .and_then(|n| n.to_str())
            .unwrap_or("(unknown)")
    );
    println!("  Tensors:    {}", info.tensor_count);
    println!("  Total size: {}", format_bytes(info.total_bytes));
    let dtype_list: String = info
        .dtypes
        .iter()
        .map(ToString::to_string)
        .collect::<Vec<_>>()
        .join(", ");
    println!("  Dtypes:     {dtype_list}");
    let endian = if info.big_endian {
        "big-endian"
    } else {
        "little-endian"
    };
    println!("  Byte order: {endian}");
    println!();

    for t in &tensor_info {
        let shape_str = format!("{:?}", t.shape);
        // CAST: usize → u64, tensor byte lengths fit in u64
        #[allow(clippy::as_conversions)]
        let byte_len = t.byte_len as u64;
        println!(
            "  {:<30} {:<6} {:<15} {}",
            t.name,
            t.dtype,
            shape_str,
            format_bytes(byte_len)
        );
    }
    Ok(())
}

#[cfg(feature = "npz")]
fn run_parse_npz(path: &std::path::Path) -> anamnesis::Result<()> {
    // Use inspect_npz (header-only) instead of parse_npz — avoids loading
    // all tensor data into memory for a display-only operation.
    let info = anamnesis::inspect_npz(path)?;

    println!(
        "Parsed {} (NPZ archive)",
        path.file_name()
            .and_then(|n| n.to_str())
            .unwrap_or("(unknown)")
    );
    println!("  Tensors:    {}", info.tensors.len());
    println!("  Total size: {}", format_bytes(info.total_bytes));
    println!();

    for t in &info.tensors {
        let shape_str = format!("{:?}", t.shape);
        // CAST: usize → u64, tensor byte lengths fit
        #[allow(clippy::as_conversions)]
        let byte_len = t.byte_len as u64;
        println!(
            "  {:<30} {:<6} {:<15} {}",
            t.name,
            t.dtype,
            shape_str,
            format_bytes(byte_len)
        );
    }
    Ok(())
}

#[cfg(feature = "npz")]
fn run_inspect_npz(path: &std::path::Path) -> anamnesis::Result<()> {
    // Header-only — no tensor data loaded.
    let info = anamnesis::inspect_npz(path)?;
    println!("{info}");
    Ok(())
}

fn run_inspect(path: &std::path::Path) -> anamnesis::Result<()> {
    match detect_format(path)? {
        Format::Safetensors => {
            let model = parse(path)?;
            let info = InspectInfo::from(&model.header);
            println!("{info}");
        }
        #[cfg(feature = "pth")]
        Format::Pth => {
            let parsed = anamnesis::parse_pth(path)?;
            let info = parsed.inspect();
            println!("{info}");
        }
        #[cfg(feature = "npz")]
        Format::Npz => run_inspect_npz(path)?,
        #[cfg(feature = "gguf")]
        Format::Gguf => {
            let parsed = anamnesis::parse_gguf(path)?;
            let info = parsed.inspect();
            println!("{info}");
        }
    }
    Ok(())
}

fn run_remember(
    path: &std::path::Path,
    to: &str,
    output: Option<&std::path::Path>,
) -> anamnesis::Result<()> {
    match detect_format(path)? {
        Format::Safetensors => run_remember_safetensors(path, to, output),
        #[cfg(feature = "pth")]
        Format::Pth => {
            let to_lower = to.to_ascii_lowercase();
            if to_lower != "safetensors" && to_lower != "bf16" {
                return Err(anamnesis::AnamnesisError::Unsupported {
                    format: "pth".into(),
                    detail: format!(
                        "unsupported --to value `{to}` for .pth files \
                         (supported: `safetensors`, `bf16` — .pth conversion \
                         always produces safetensors)"
                    ),
                });
            }
            run_remember_pth(path, output)
        }
        #[cfg(feature = "npz")]
        Format::Npz => Err(anamnesis::AnamnesisError::Unsupported {
            format: "NPZ".into(),
            detail: "NPZ tensors are already full-precision; \
                     no dequantization or conversion needed"
                .into(),
        }),
        #[cfg(feature = "gguf")]
        Format::Gguf => {
            let to_lower = to.to_ascii_lowercase();
            if to_lower != "safetensors" && to_lower != "bf16" {
                return Err(anamnesis::AnamnesisError::Unsupported {
                    format: "GGUF".into(),
                    detail: format!(
                        "unsupported --to value `{to}` for .gguf files \
                         (supported: `safetensors`, `bf16`)"
                    ),
                });
            }
            run_remember_gguf(path, output)
        }
    }
}

fn run_remember_safetensors(
    path: &std::path::Path,
    to: &str,
    output: Option<&std::path::Path>,
) -> anamnesis::Result<()> {
    let target: TargetDtype = to.parse()?;

    let model = parse(path)?;
    let info = InspectInfo::from(&model.header);

    let total = model.header.tensors.len();
    let quantized = model.header.quantized_count();
    println!("Parsing...  {total} tensors, {}", model.header.scheme);

    let output_path = match output {
        Some(p) => p.to_owned(),
        None => derive_output_path(path, target),
    };

    #[cfg(feature = "indicatif")]
    {
        use indicatif::{ProgressBar, ProgressStyle};

        // CAST: usize → u64, tensor count fits in u64
        #[allow(clippy::as_conversions)]
        let pb = ProgressBar::new(quantized as u64);
        let style = ProgressStyle::with_template("Recalling... {pos} tensors [{bar:20}] {elapsed}")
            .unwrap_or_else(|_| ProgressStyle::default_bar())
            .progress_chars("=> ");
        pb.set_style(style);
        model.remember_with_progress(&output_path, target, || pb.inc(1))?;
        pb.finish();
        println!();
    }

    #[cfg(not(feature = "indicatif"))]
    {
        println!("Recalling... {quantized} tensors");
        model.remember(&output_path, target)?;
    }

    println!(
        "Output: {} ({})",
        output_path.display(),
        format_bytes(info.dequantized_size),
    );
    Ok(())
}

#[cfg(feature = "pth")]
fn run_remember_pth(
    path: &std::path::Path,
    output: Option<&std::path::Path>,
) -> anamnesis::Result<()> {
    let parsed = anamnesis::parse_pth(path)?;
    let info = parsed.inspect();

    let output_path = if let Some(p) = output {
        p.to_owned()
    } else {
        // Replace extension: model.pth → model.safetensors
        let mut out = path.to_owned();
        out.set_extension("safetensors");
        out
    };

    println!(
        "Converting {} → {}",
        path.file_name()
            .and_then(|n| n.to_str())
            .unwrap_or("(input)"),
        output_path
            .file_name()
            .and_then(|n| n.to_str())
            .unwrap_or("(output)")
    );
    println!(
        "  {} tensors, {}",
        info.tensor_count,
        format_bytes(info.total_bytes)
    );

    parsed.to_safetensors(&output_path)?;
    println!("  Done.");
    Ok(())
}

#[cfg(feature = "gguf")]
fn run_parse_gguf(path: &std::path::Path) -> anamnesis::Result<()> {
    let parsed = anamnesis::parse_gguf(path)?;
    let info = parsed.inspect();
    let tensor_info = parsed.tensor_info();

    println!(
        "Parsed {} (GGUF v{})",
        path.file_name()
            .and_then(|n| n.to_str())
            .unwrap_or("(unknown)"),
        info.version
    );
    if let Some(arch) = info.architecture.as_deref() {
        println!("  Arch:       {arch}");
    }
    println!("  Tensors:    {}", info.tensor_count);
    println!("  Total size: {}", format_bytes(info.total_bytes));
    let dtype_list: String = info
        .dtypes
        .iter()
        .map(ToString::to_string)
        .collect::<Vec<_>>()
        .join(", ");
    println!("  Dtypes:     {dtype_list}");
    println!("  Alignment:  {} bytes", info.alignment);
    println!();

    for t in tensor_info {
        let shape_str = format!("{:?}", t.shape);
        let byte_len_str = t.byte_len.map_or_else(|| "?".into(), format_bytes);
        println!(
            "  {:<40} {:<8} {:<15} {}",
            t.name, t.dtype, shape_str, byte_len_str
        );
    }
    Ok(())
}

#[cfg(feature = "gguf")]
fn run_remember_gguf(
    path: &std::path::Path,
    output: Option<&std::path::Path>,
) -> anamnesis::Result<()> {
    let parsed = anamnesis::parse_gguf(path)?;
    let info = parsed.inspect();

    let output_path = if let Some(p) = output {
        p.to_owned()
    } else {
        let mut out = path.to_owned();
        out.set_extension("safetensors");
        out
    };

    println!(
        "Converting {} → {}",
        path.file_name()
            .and_then(|n| n.to_str())
            .unwrap_or("(input)"),
        output_path
            .file_name()
            .and_then(|n| n.to_str())
            .unwrap_or("(output)")
    );
    println!("  {} tensors", info.tensor_count);

    // Dequantize quantized tensors to BF16; pass through non-quantized
    // tensors (F32, F16, BF16, integer types) with their original dtype.
    // Collect owned data because TensorView borrows data and all views
    // must be alive simultaneously for serialize_to_file.
    let mut tensor_data: Vec<(String, Vec<u8>, Vec<usize>, safetensors::Dtype)> =
        Vec::with_capacity(info.tensor_count);
    let mut dequantized_count: usize = 0;

    for tensor in parsed.tensors() {
        // GGUF shape is most-significant-first; safetensors expects
        // row-major (NumPy-style). Reverse the dimensions.
        let mut shape: Vec<usize> = tensor.shape.to_vec();
        shape.reverse();

        if tensor.dtype.is_quantized() {
            let n_elements: usize = tensor
                .shape
                .iter()
                .try_fold(1usize, |acc, &d| acc.checked_mul(d))
                .ok_or_else(|| anamnesis::AnamnesisError::Parse {
                    reason: format!(
                        "GGUF tensor `{}` shape {:?} element count overflows usize",
                        tensor.name, tensor.shape
                    ),
                })?;
            let bf16_data =
                anamnesis::dequantize_gguf_to_bf16(&tensor.data, tensor.dtype, n_elements)?;
            tensor_data.push((
                tensor.name.to_owned(),
                bf16_data,
                shape,
                safetensors::Dtype::BF16,
            ));
            dequantized_count += 1;
        } else {
            let st_dtype = gguf_type_to_safetensors_dtype(tensor.dtype)?;
            // BORROW: `.into_owned()` copies borrowed mmap bytes into an
            // owned Vec so the data outlives the parsed borrow.
            tensor_data.push((
                tensor.name.to_owned(),
                tensor.data.into_owned(),
                shape,
                st_dtype,
            ));
        }
    }

    println!(
        "  {} dequantized to BF16, {} passed through",
        dequantized_count,
        tensor_data.len() - dequantized_count
    );

    // Build TensorView list and serialize to file.
    let views: Vec<(String, safetensors::tensor::TensorView<'_>)> =
        tensor_data
            .iter()
            .map(|(name, data, shape, dtype)| {
                let view = safetensors::tensor::TensorView::new(*dtype, shape.clone(), data)
                    .map_err(|e| anamnesis::AnamnesisError::Parse {
                        reason: format!("failed to create TensorView for `{name}`: {e}"),
                    })?;
                Ok((name.clone(), view))
            })
            .collect::<anamnesis::Result<Vec<_>>>()?;

    safetensors::tensor::serialize_to_file(views, &None, output_path.as_ref()).map_err(
        // EXHAUSTIVE: SafeTensorError is a foreign type that may gain variants
        #[allow(clippy::wildcard_enum_match_arm)]
        |e| match e {
            safetensors::SafeTensorError::IoError(io_err) => anamnesis::AnamnesisError::Io(io_err),
            other => anamnesis::AnamnesisError::Parse {
                reason: format!("failed to write safetensors file: {other}"),
            },
        },
    )?;

    println!("  Output: {}", output_path.display());
    Ok(())
}

/// Maps a non-quantized [`GgufType`](anamnesis::GgufType) to the
/// corresponding `safetensors::Dtype`.
#[cfg(feature = "gguf")]
fn gguf_type_to_safetensors_dtype(
    dtype: anamnesis::GgufType,
) -> anamnesis::Result<safetensors::Dtype> {
    // EXHAUSTIVE: GgufType is a foreign #[non_exhaustive] enum — new
    // variants may be added. The wildcard covers future types.
    #[allow(clippy::wildcard_enum_match_arm)]
    match dtype {
        anamnesis::GgufType::F32 => Ok(safetensors::Dtype::F32),
        anamnesis::GgufType::F16 => Ok(safetensors::Dtype::F16),
        anamnesis::GgufType::BF16 => Ok(safetensors::Dtype::BF16),
        anamnesis::GgufType::F64 => Ok(safetensors::Dtype::F64),
        anamnesis::GgufType::I8 => Ok(safetensors::Dtype::I8),
        anamnesis::GgufType::I16 => Ok(safetensors::Dtype::I16),
        anamnesis::GgufType::I32 => Ok(safetensors::Dtype::I32),
        anamnesis::GgufType::I64 => Ok(safetensors::Dtype::I64),
        other => Err(anamnesis::AnamnesisError::Unsupported {
            format: "GGUF".into(),
            detail: format!("no safetensors equivalent for {other}"),
        }),
    }
}

// ---------------------------------------------------------------------------
// Output path derivation
// ---------------------------------------------------------------------------

/// Known quantization suffixes stripped from input filenames when deriving output paths.
///
/// Case-sensitive: common conventions use lowercase, uppercase, or mixed-case.
/// Ordered longest-first so that e.g. `-GPTQ-Int4` is tried before `-gptq`.
const QUANT_SUFFIXES: &[&str] = &[
    // GPTQ
    "-GPTQ-Int4",
    "-GPTQ-Int8",
    "-gptq-int4",
    "-gptq-int8",
    "-gptq4",
    "-gptq8",
    "-GPTQ",
    "-gptq",
    "_gptq",
    // AWQ
    "-AWQ",
    "-awq",
    "_awq",
    // BitsAndBytes
    "-bnb-4bit",
    "-bnb-int8",
    "-bnb",
    "_bnb",
    "-4bit",
    "-int4",
    "-int8",
    // FP8
    "-fp8",
    "_fp8",
    "-FP8",
];

/// Derive an output path from the input path and target dtype.
///
/// `model-fp8.safetensors`  → `model-bf16.safetensors`
/// `model-GPTQ-Int4.safetensors` → `model-bf16.safetensors`
/// `weights.safetensors`    → `weights-bf16.safetensors`
fn derive_output_path(input: &std::path::Path, target: TargetDtype) -> PathBuf {
    let stem = input
        .file_stem()
        .and_then(|s| s.to_str())
        .unwrap_or("output");
    let suffix = target.to_string().to_lowercase();

    // Strip known quantization suffixes before appending target.
    let clean_stem = QUANT_SUFFIXES
        .iter()
        .find_map(|qs| stem.strip_suffix(qs))
        .unwrap_or(stem);

    let new_name = format!("{clean_stem}-{suffix}.safetensors");
    input
        .parent()
        .map_or_else(|| PathBuf::from(&new_name), |p| p.join(&new_name))
}

fn main() {
    if let Err(e) = run() {
        eprintln!("error: {e}");
        process::exit(1);
    }
}