use std::path::PathBuf;

#[derive(Debug)]
pub enum DatasetAction {
    Process {
        files: Vec<PathBuf>,
        output: Option<PathBuf>,
        format: Option<String>,
        algorithm: Option<String>,
        validate: bool,
    },
    Analyze {
        files: Vec<PathBuf>,
        detailed: bool
    },
    Convert {
        input: PathBuf,
        output: Option<PathBuf>,
        from_format: String,
        to_format: String,
    },
    Quality {
        files: Vec<PathBuf>,
        report: bool
    },
    Huggingface {
        dataset: String,
        split: Option<String>,
        output: Option<PathBuf>,
        cache_dir: Option<PathBuf>,
    },
}

pub async fn dataset_command(
    action: DatasetAction,
    verbose: bool,
) -> Result<(), Box<dyn std::error::Error>> {
    match action {
        DatasetAction::Process { files, output, format, algorithm, validate } => {
            if verbose {
                println!("🧠 Processing datasets with HLX-AI...");
                println!("  Files: {:?}", files);
                println!("  Output: {:?}", output);
                println!("  Format: {:?}", format);
                println!("  Algorithm: {:?}", algorithm);
                println!("  Validate: {}", validate);
            }
            use crate::dna::map::core::{GenericJSONDataset, DataFormat};
            for file in &files {
                if verbose {
                    println!("📊 Processing: {}", file.display());
                }
                let dataset = GenericJSONDataset::new(
                        &[file.clone()],
                        None,
                        DataFormat::Auto,
                    )
                    .map_err(|e| {
                        format!("Failed to load dataset {}: {}", file.display(), e)
                    })?;
                let training_dataset = dataset
                    .to_training_dataset()
                    .map_err(|e| {
                        format!("Failed to convert dataset {}: {}", file.display(), e)
                    })?;
                if validate {
                    let quality = training_dataset.quality_assessment();
                    println!("✅ Quality Score: {:.2}", quality.overall_score);
                    if !quality.issues.is_empty() {
                        println!("⚠️  Issues:");
                        for issue in &quality.issues {
                            println!("   - {}", issue);
                        }
                    }
                }
                if let Some(algo) = &algorithm {
                    if training_dataset.to_algorithm_format(algo).is_ok() {
                        println!("✅ Converted to {} format", algo.to_uppercase());
                    } else {
                        println!(
                            "❌ Failed to convert to {} format", algo.to_uppercase()
                        );
                    }
                }
                println!(
                    "📈 Dataset stats: {} samples", training_dataset.samples.len()
                );
            }
            println!("🎉 Dataset processing completed!");
            Ok(())
        }
        DatasetAction::Analyze { files, detailed } => {
            if verbose {
                println!("🔍 Analyzing datasets...");
            }
            use crate::dna::map::core::{GenericJSONDataset, DataFormat};
            for file in files {
                if verbose {
                    println!("📊 Analyzing: {}", file.display());
                }
                let dataset = GenericJSONDataset::new(
                        &[file.clone()],
                        None,
                        DataFormat::Auto,
                    )
                    .map_err(|e| {
                        format!("Failed to load dataset {}: {}", file.display(), e)
                    })?;
                println!("\n--- Dataset Analysis: {} ---", file.display());
                for (key, value) in dataset.stats() {
                    println!("{:15}: {}", key, value);
                }
                if detailed {
                    let training_dataset = dataset
                        .to_training_dataset()
                        .map_err(|e| {
                            format!(
                                "Failed to convert dataset {}: {}", file.display(), e
                            )
                        })?;
                    println!("\n--- Training Format Analysis ---");
                    println!("Format: {:?}", training_dataset.format);
                    println!("Samples: {}", training_dataset.samples.len());
                    println!(
                        "Avg Prompt Length: {:.1}", training_dataset.statistics
                        .avg_prompt_length
                    );
                    println!(
                        "Avg Completion Length: {:.1}", training_dataset.statistics
                        .avg_completion_length
                    );
                    println!("\n--- Field Coverage ---");
                    for (field, coverage) in &training_dataset.statistics.field_coverage
                    {
                        println!("{:12}: {:.1}%", field, coverage * 100.0);
                    }
                }
            }
            Ok(())
        }
        DatasetAction::Convert { input, output: _output, from_format, to_format } => {
            if verbose {
                println!("🔄 Converting dataset format...");
                println!("  Input: {}", input.display());
                println!("  From: {}", from_format);
                println!("  To: {}", to_format);
            }
            println!("🔄 Format conversion: {} → {}", from_format, to_format);
            println!("✅ Conversion completed (placeholder)");
            Ok(())
        }
        DatasetAction::Quality { files, report } => {
            if verbose {
                println!("📊 Assessing dataset quality...");
            }
            use crate::dna::map::core::{GenericJSONDataset, DataFormat};
            for file in files {
                let dataset = GenericJSONDataset::new(
                        &[file.clone()],
                        None,
                        DataFormat::Auto,
                    )
                    .map_err(|e| {
                        format!("Failed to load dataset {}: {}", file.display(), e)
                    })?;
                let training_dataset = dataset
                    .to_training_dataset()
                    .map_err(|e| {
                        format!("Failed to convert dataset {}: {}", file.display(), e)
                    })?;
                let quality = training_dataset.quality_assessment();
                if report {
                    println!("\n=== Quality Report: {} ===", file.display());
                    println!("Overall Score: {:.2}/1.0", quality.overall_score);
                    println!("\nIssues:");
                    if quality.issues.is_empty() {
                        println!("  ✅ No issues found");
                    } else {
                        for issue in &quality.issues {
                            println!("  ⚠️  {}", issue);
                        }
                    }
                    println!("\nRecommendations:");
                    for rec in &quality.recommendations {
                        println!("  💡 {}", rec);
                    }
                } else {
                    println!(
                        "📊 {}: Quality Score {:.2}", file.display(), quality
                        .overall_score
                    );
                }
            }
            Ok(())
        }
        DatasetAction::Huggingface { dataset, split, output, cache_dir } => {
            if verbose {
                println!("🤗 Loading HuggingFace dataset...");
                println!("  Dataset: {}", dataset);
                println!(
                    "  Split: {:?}", split.as_ref().unwrap_or(& "train".to_string())
                );
                println!("  Cache: {:?}", cache_dir);
                println!("  Output: {:?}", output);
            }
            let processor = crate::dna::map::HfProcessor::new(
                cache_dir.unwrap_or_else(|| PathBuf::from("./hf_cache")),
            );
            let config = crate::dna::map::HfDatasetConfig {
                source: dataset.clone(),
                split: split.unwrap_or_else(|| "train".to_string()),
                format: None,
                rpl_filter: None,
                revision: None,
                streaming: false,
                trust_remote_code: false,
                num_proc: None,
            };
            match processor.process_dataset(&dataset, &config).await {
                Ok(training_dataset) => {
                    println!("✅ HuggingFace dataset loaded successfully");
                    println!("📊 Samples: {}", training_dataset.samples.len());
                    println!("📝 Format: {:?}", training_dataset.format);
                    if let Some(output_path) = output {
                        let json_output = serde_json::to_string_pretty(
                                &training_dataset.samples,
                            )
                            .map_err(|e| format!("Failed to serialize output: {}", e))?;
                        std::fs::write(&output_path, json_output)
                            .map_err(|e| {
                                format!(
                                    "Failed to write output file {}: {}", output_path.display(),
                                    e
                                )
                            })?;
                        println!(
                            "💾 Saved processed dataset to: {}", output_path.display()
                        );
                    }
                }
                Err(e) => {
                    println!("❌ Failed to load HuggingFace dataset: {}", e);
                    return Err(e.into());
                }
            }
            Ok(())
        }
    }
}