ipfrs-cli 0.2.0

//! Gradient operations commands
//!
//! This module provides gradient operations for federated learning:
//! - `gradient_push` - Publish gradient
//! - `gradient_pull` - Fetch gradient
//! - `gradient_aggregate` - Aggregate gradients
//! - `gradient_history` - View gradient history

use anyhow::Result;

use crate::output::{self, print_cid, print_header, print_kv};
use crate::progress;

/// Publish gradient
#[allow(dead_code)]
pub async fn gradient_push(path: &str, model_cid: Option<&str>, format: &str) -> Result<()> {
    let pb = progress::spinner("Preparing gradient upload...");
    progress::finish_spinner_success(&pb, "Gradient preparation complete");

    output::warning("Gradient operations require federated learning system integration");

    match format {
        "json" => {
            println!("{{");
            println!("  \"path\": \"{}\",", path);
            if let Some(mcid) = model_cid {
                println!("  \"model_cid\": \"{}\",", mcid);
            }
            println!("  \"status\": \"not_implemented\"");
            println!("}}");
        }
        _ => {
            print_header("Gradient Push");
            println!("Path: {}", path);
            if let Some(mcid) = model_cid {
                print_cid("Model CID", mcid);
            }
            println!();
            println!("Gradient push would upload the gradient to the network.");
        }
    }

    Ok(())
}

/// Fetch gradient
#[allow(dead_code)]
pub async fn gradient_pull(cid: &str, output_path: Option<&str>) -> Result<()> {
    let pb = progress::spinner("Preparing gradient download...");
    progress::finish_spinner_success(&pb, "Download preparation complete");

    output::warning("Gradient operations require federated learning system integration");

    print_header("Gradient Pull");
    print_cid("Gradient CID", cid);
    if let Some(out) = output_path {
        println!("  Output: {}", out);
    }
    println!();
    println!("Gradient pull would download the gradient from the network.");

    Ok(())
}

/// Aggregate gradients
#[allow(dead_code)]
pub async fn gradient_aggregate(
    cids: &[String],
    output: &str,
    method: &str,
    format: &str,
) -> Result<()> {
    let pb = progress::spinner("Preparing gradient aggregation...");
    progress::finish_spinner_success(&pb, "Aggregation preparation complete");

    output::warning("Gradient operations require federated learning system integration");

    match format {
        "json" => {
            println!("{{");
            println!("  \"gradient_cids\": [");
            for (i, cid) in cids.iter().enumerate() {
                if i < cids.len() - 1 {
                    println!("    \"{}\",", cid);
                } else {
                    println!("    \"{}\"", cid);
                }
            }
            println!("  ],");
            println!("  \"output\": \"{}\",", output);
            println!("  \"method\": \"{}\",", method);
            println!("  \"status\": \"not_implemented\"");
            println!("}}");
        }
        _ => {
            print_header("Gradient Aggregation");
            print_kv("Number of gradients", &cids.len().to_string());
            print_kv("Method", method);
            print_kv("Output", output);
            println!();
            println!(
                "Aggregation would combine gradients using the {} method.",
                method
            );
        }
    }

    Ok(())
}

/// View gradient history
#[allow(dead_code)]
pub async fn gradient_history(cid: &str, limit: usize, format: &str) -> Result<()> {
    let pb = progress::spinner("Retrieving gradient history...");
    progress::finish_spinner_success(&pb, "History retrieved");

    output::warning("Gradient operations require federated learning system integration");

    match format {
        "json" => {
            println!("{{");
            println!("  \"model_cid\": \"{}\",", cid);
            println!("  \"limit\": {},", limit);
            println!("  \"history\": [],");
            println!("  \"status\": \"not_implemented\"");
            println!("}}");
        }
        _ => {
            print_header("Gradient History");
            print_cid("Model CID", cid);
            print_kv("Limit", &limit.to_string());
            println!();
            println!("History would show gradient updates for this model.");
        }
    }

    Ok(())
}