//! TensorFlow format converter
#![allow(dead_code)]

use crate::error::{IoError, Result};
use crate::ml_framework::converters::MLFrameworkConverter;
use crate::ml_framework::types::{MLFramework, MLModel, MLTensor};
use scirs2_core::ndarray::{ArrayD, IxDyn};
use std::fs::File;
use std::path::Path;

/// TensorFlow format converter
pub struct TensorFlowConverter;

impl MLFrameworkConverter for TensorFlowConverter {
    fn save_model(&self, model: &MLModel, path: &Path) -> Result<()> {
        // TensorFlow SavedModel format
        let model_dir = path.parent().unwrap_or(Path::new("."));
        std::fs::create_dir_all(model_dir).map_err(IoError::Io)?;

        let tf_model = serde_json::json!({
            "saved_model_schema_version": 1,
            "meta_graphs": [{
                "meta_info_def": {
                    "meta_graph_version": "v2.0.0",
                    "tensorflow_version": "2.12.0",
                    "tags": ["serve"]
                },
                "graph_def": {
                    "versions": { "producer": 1982, "min_consumer": 12 }
                },
                "signature_def": {
                    "serving_default": {
                        "inputs": model.metadata.inputshapes,
                        "outputs": model.metadata.outputshapes,
                        "method_name": "tensorflow/serving/predict"
                    }
                }
            }],
            "variables": model.weights.iter().map(|(name, tensor)| {
                serde_json::json!({
                    "name": name,
                    "shape": tensor.metadata.shape,
                    "dtype": format!("{:?}", tensor.metadata.dtype),
                    "data": tensor.data.as_slice().expect("Operation failed").to_vec()
                })
            }).collect::<Vec<_>>()
        });

        let file = File::create(path).map_err(IoError::Io)?;
        serde_json::to_writer_pretty(file, &tf_model)
            .map_err(|e| IoError::SerializationError(e.to_string()))
    }

    fn load_model(&self, path: &Path) -> Result<MLModel> {
        let file = File::open(path).map_err(IoError::Io)?;
        let tf_model: serde_json::Value = serde_json::from_reader(file)
            .map_err(|e| IoError::SerializationError(e.to_string()))?;

        let mut model = MLModel::new(MLFramework::TensorFlow);

        // Parse TensorFlow metadata
        if let Some(meta_graphs) = tf_model.get("meta_graphs").and_then(|v| v.as_array()) {
            if let Some(meta_graph) = meta_graphs.first() {
                if let Some(signature_def) = meta_graph
                    .get("signature_def")
                    .and_then(|v| v.get("serving_default"))
                {
                    if let Some(inputs) = signature_def.get("inputs").and_then(|v| v.as_object()) {
                        for (name, input_info) in inputs {
                            if let Some(shape) = input_info.as_array() {
                                let shape_vec: Vec<usize> = shape
                                    .iter()
                                    .filter_map(|v| v.as_u64().map(|u| u as usize))
                                    .collect();
                                model.metadata.inputshapes.insert(name.clone(), shape_vec);
                            }
                        }
                    }
                }
            }
        }

        // Parse variables
        if let Some(variables) = tf_model.get("variables").and_then(|v| v.as_array()) {
            for var in variables {
                if let Some(var_obj) = var.as_object() {
                    if let (Some(name), Some(shape), Some(data)) = (
                        var_obj.get("name").and_then(|v| v.as_str()),
                        var_obj.get("shape").and_then(|v| v.as_array()),
                        var_obj.get("data").and_then(|v| v.as_array()),
                    ) {
                        let shape_vec: Vec<usize> = shape
                            .iter()
                            .filter_map(|v| v.as_u64().map(|u| u as usize))
                            .collect();

                        let data_vec: Vec<f32> = data
                            .iter()
                            .filter_map(|v| v.as_f64().map(|f| f as f32))
                            .collect();

                        if let Ok(array) = ArrayD::from_shape_vec(IxDyn(&shape_vec), data_vec) {
                            model.weights.insert(
                                name.to_string(),
                                MLTensor::new(array, Some(name.to_string())),
                            );
                        }
                    }
                }
            }
        }

        Ok(model)
    }

    fn save_tensor(&self, tensor: &MLTensor, path: &Path) -> Result<()> {
        let tensor_data = serde_json::json!({
            "tensor": {
                "dtype": format!("{:?}", tensor.metadata.dtype),
                "tensorshape": {
                    "dim": tensor.metadata.shape.iter().map(|&d| serde_json::json!({"size": d})).collect::<Vec<_>>()
                },
                "tensor_content": tensor.data.as_slice().expect("Operation failed")
                    .iter()
                    .flat_map(|f| f.to_le_bytes().to_vec())
                    .collect::<Vec<u8>>()
            }
        });

        let file = File::create(path).map_err(IoError::Io)?;
        serde_json::to_writer_pretty(file, &tensor_data)
            .map_err(|e| IoError::SerializationError(e.to_string()))
    }

    fn load_tensor(&self, path: &Path) -> Result<MLTensor> {
        let file = File::open(path).map_err(IoError::Io)?;
        let tensor_data: serde_json::Value = serde_json::from_reader(file)
            .map_err(|e| IoError::SerializationError(e.to_string()))?;

        if let Some(tensor) = tensor_data.get("tensor") {
            let shape: Vec<usize> = tensor
                .get("tensorshape")
                .and_then(|ts| ts.get("dim"))
                .and_then(|dims| dims.as_array())
                .map(|dims| {
                    dims.iter()
                        .filter_map(|d| d.get("size").and_then(|s| s.as_u64().map(|u| u as usize)))
                        .collect()
                })
                .unwrap_or_default();

            // Simplified: decode tensor_content as float array
            let content = tensor.get("tensor_content").and_then(|c| c.as_array());
            let data: Vec<f32> = if let Some(content_array) = content {
                content_array
                    .iter()
                    .filter_map(|v| v.as_f64().map(|f| f as f32))
                    .collect()
            } else {
                vec![0.0; shape.iter().product()]
            };

            let array = ArrayD::from_shape_vec(IxDyn(&shape), data)
                .map_err(|e| IoError::Other(e.to_string()))?;

            return Ok(MLTensor::new(array, None));
        }

        Err(IoError::Other(
            "Invalid TensorFlow tensor format".to_string(),
        ))
    }
}
