scirs2-text 0.4.1

//! Hugging Face Hub integration for model discovery and download
//!
//! This module provides functionality for interacting with the Hugging Face
//! model hub to discover, download, and manage models.

use crate::error::{Result, TextError};
use std::collections::HashMap;
use std::path::{Path, PathBuf};

#[cfg(feature = "serde-support")]
use serde::{Deserialize, Serialize};

/// Hugging Face Hub interface
#[derive(Debug)]
pub struct HfHub {
    /// Cache directory for downloaded models
    cache_dir: PathBuf,
    /// API token for authenticated requests
    token: Option<String>,
    /// Model repository cache
    model_cache: HashMap<String, HfModelInfo>,
}

impl HfHub {
    /// Create new HF Hub interface
    pub fn new() -> Self {
        let cache_dir = std::env::var("HF_HOME")
            .or_else(|_| std::env::var("HUGGINGFACE_HUB_CACHE"))
            .map(PathBuf::from)
            .unwrap_or_else(|_| {
                let mut home = std::env::var("HOME")
                    .map(PathBuf::from)
                    .unwrap_or_else(|_| PathBuf::from("."));
                home.push(".cache");
                home.push("huggingface");
                home.push("hub");
                home
            });

        Self {
            cache_dir,
            token: None,
            model_cache: HashMap::new(),
        }
    }

    /// Set authentication token
    pub fn with_token(mut self, token: String) -> Self {
        self.token = Some(token);
        self
    }

    /// Set cache directory
    pub fn with_cache_dir<P: AsRef<Path>>(mut self, cache_dir: P) -> Self {
        self.cache_dir = cache_dir.as_ref().to_path_buf();
        self
    }

    /// List available models
    pub fn list_models(&self, filter: Option<&str>) -> Result<Vec<String>> {
        // Simulated model list (in practice, this would make HTTP requests)
        let models = vec![
            "bert-base-uncased",
            "bert-large-uncased",
            "distilbert-base-uncased",
            "roberta-base",
            "roberta-large",
            "gpt2",
            "gpt2-medium",
            "gpt2-large",
            "t5-small",
            "t5-base",
            "t5-large",
            "facebook/bart-base",
            "facebook/bart-large",
            "microsoft/DialoGPT-medium",
            "microsoft/DialoGPT-large",
        ];

        let filtered_models: Vec<String> = models
            .into_iter()
            .filter(|model| filter.is_none_or(|f| model.to_lowercase().contains(&f.to_lowercase())))
            .map(|s| s.to_string())
            .collect();

        Ok(filtered_models)
    }

    /// Get model information
    pub fn model_info(&mut self, model_id: &str) -> Result<HfModelInfo> {
        if let Some(info) = self.model_cache.get(model_id) {
            return Ok(info.clone());
        }

        // Create mock model info (in practice, this would fetch from API)
        let info = HfModelInfo {
            model_id: model_id.to_string(),
            tags: vec!["pytorch".to_string(), "transformers".to_string()],
            pipeline_tag: Some(self.infer_pipeline_tag(model_id)),
            downloads: 1000000,
            likes: 500,
            library_name: Some("transformers".to_string()),
        };

        self.model_cache.insert(model_id.to_string(), info.clone());
        Ok(info)
    }

    /// Download model files
    pub fn download_model<P: AsRef<Path>>(
        &self,
        model_id: &str,
        cache_dir: Option<P>,
    ) -> Result<PathBuf> {
        let download_dir = cache_dir
            .map(|p| p.as_ref().to_path_buf())
            .unwrap_or_else(|| self.cache_dir.join(model_id));

        // Create download directory
        std::fs::create_dir_all(&download_dir)
            .map_err(|e| TextError::IoError(format!("Failed to create download directory: {e}")))?;

        // In a real implementation, this would download files from the hub
        // For now, create placeholder files
        let files = [
            "config.json",
            "pytorch_model.bin",
            "tokenizer.json",
            "vocab.txt",
        ];

        for file in &files {
            let file_path = download_dir.join(file);
            if !file_path.exists() {
                let content = if file == &"config.json" {
                    // Create a valid JSON config for testing
                    r#"{
  "architectures": ["BertModel"],
  "model_type": "bert",
  "num_attention_heads": 12,
  "hidden_size": 768,
  "intermediate_size": 3072,
  "num_hidden_layers": 12,
  "vocab_size": 30522,
  "max_position_embeddings": 512,
  "extraconfig": {}
}"#
                    .to_string()
                } else {
                    format!("# Placeholder {file} for {model_id}")
                };
                std::fs::write(&file_path, content)
                    .map_err(|e| TextError::IoError(format!("Failed to create {file}: {e}")))?;
            }
        }

        Ok(download_dir)
    }

    /// Upload model to hub
    pub fn upload_model<P: AsRef<Path>>(
        &self,
        model_path: P,
        repo_id: &str,
        commit_message: Option<&str>,
    ) -> Result<()> {
        let model_path = model_path.as_ref();

        if !model_path.exists() {
            return Err(TextError::InvalidInput(
                "Model path does not exist".to_string(),
            ));
        }

        // Validate required files
        let required_files = ["config.json"];
        for file in &required_files {
            if !model_path.join(file).exists() {
                return Err(TextError::InvalidInput(format!(
                    "Required file {file} not found"
                )));
            }
        }

        println!(
            "Would upload model from {} to {} with message: {}",
            model_path.display(),
            repo_id,
            commit_message.unwrap_or("Upload model")
        );

        Ok(())
    }

    /// Create model repository
    pub fn create_repo(&self, repo_id: &str, private: bool) -> Result<()> {
        if self.token.is_none() {
            return Err(TextError::InvalidInput(
                "Authentication token required".to_string(),
            ));
        }

        println!("Would create repository {} (private: {})", repo_id, private);

        Ok(())
    }

    /// Get cached model path
    pub fn get_cached_model_path(&self, model_id: &str) -> PathBuf {
        self.cache_dir.join(model_id)
    }

    fn infer_pipeline_tag(&self, model_id: &str) -> String {
        if model_id.contains("bert") || model_id.contains("roberta") {
            "text-classification".to_string()
        } else if model_id.contains("gpt") || model_id.contains("t5") {
            "text-generation".to_string()
        } else if model_id.contains("bart") {
            "summarization".to_string()
        } else {
            "feature-extraction".to_string()
        }
    }
}

impl Default for HfHub {
    fn default() -> Self {
        Self::new()
    }
}

/// Model information from Hugging Face Hub
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde-support", derive(Serialize, Deserialize))]
pub struct HfModelInfo {
    /// Model identifier
    pub model_id: String,
    /// Model tags
    pub tags: Vec<String>,
    /// Pipeline task type
    pub pipeline_tag: Option<String>,
    /// Download count
    pub downloads: u64,
    /// Like count
    pub likes: u64,
    /// Library name (e.g., "transformers")
    pub library_name: Option<String>,
}