//! CoreML 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;

/// CoreML format converter
pub struct CoreMLConverter;

impl MLFrameworkConverter for CoreMLConverter {
    fn save_model(&self, model: &MLModel, path: &Path) -> Result<()> {
        // CoreML uses a specific protobuf format, simplified here
        let coreml_model = serde_json::json!({
            "format": "coreml",
            "specificationVersion": 5,
            "description": {
                "metadata": {
                    "userDefined": model.metadata.parameters,
                    "author": "SciRS2",
                    "license": "Apache-2.0",
                    "shortDescription": model.metadata.model_name.clone().unwrap_or_default()
                },
                "input": model.metadata.inputshapes.iter().map(|(name, shape)| {
                    serde_json::json!({
                        "name": name,
                        "type": {
                            "multiArrayType": {
                                "shape": shape,
                                "dataType": "FLOAT32"
                            }
                        }
                    })
                }).collect::<Vec<_>>(),
                "output": model.metadata.outputshapes.iter().map(|(name, shape)| {
                    serde_json::json!({
                        "name": name,
                        "type": {
                            "multiArrayType": {
                                "shape": shape,
                                "dataType": "FLOAT32"
                            }
                        }
                    })
                }).collect::<Vec<_>>()
            },
            "neuralNetwork": {
                "layers": [],
                "preprocessing": []
            },
            "weights": model.weights.iter().map(|(name, tensor)| {
                (name.clone(), serde_json::json!({
                    "shape": tensor.metadata.shape,
                    "floatValue": tensor.data.as_slice().expect("Operation failed").to_vec()
                }))
            }).collect::<serde_json::Map<String, serde_json::Value>>()
        });

        let file = File::create(path).map_err(IoError::Io)?;
        serde_json::to_writer_pretty(file, &coreml_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 coreml_model: serde_json::Value = serde_json::from_reader(file)
            .map_err(|e| IoError::SerializationError(e.to_string()))?;

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

        // Parse CoreML metadata
        if let Some(description) = coreml_model.get("description") {
            if let Some(metadata) = description.get("metadata") {
                if let Some(short_desc) = metadata.get("shortDescription").and_then(|v| v.as_str())
                {
                    model.metadata.model_name = Some(short_desc.to_string());
                }
            }

            // Parse inputs
            if let Some(inputs) = description.get("input").and_then(|v| v.as_array()) {
                for input in inputs {
                    if let Some(input_obj) = input.as_object() {
                        if let (Some(name), Some(shape)) = (
                            input_obj.get("name").and_then(|v| v.as_str()),
                            input_obj
                                .get("type")
                                .and_then(|t| t.get("multiArrayType"))
                                .and_then(|mat| mat.get("shape"))
                                .and_then(|s| s.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.to_string(), shape_vec);
                        }
                    }
                }
            }

            // Parse outputs similarly
            if let Some(outputs) = description.get("output").and_then(|v| v.as_array()) {
                for output in outputs {
                    if let Some(output_obj) = output.as_object() {
                        if let (Some(name), Some(shape)) = (
                            output_obj.get("name").and_then(|v| v.as_str()),
                            output_obj
                                .get("type")
                                .and_then(|t| t.get("multiArrayType"))
                                .and_then(|mat| mat.get("shape"))
                                .and_then(|s| s.as_array()),
                        ) {
                            let shape_vec: Vec<usize> = shape
                                .iter()
                                .filter_map(|v| v.as_u64().map(|u| u as usize))
                                .collect();
                            model
                                .metadata
                                .outputshapes
                                .insert(name.to_string(), shape_vec);
                        }
                    }
                }
            }
        }

        // Parse weights
        if let Some(weights) = coreml_model.get("weights").and_then(|v| v.as_object()) {
            for (name, weight_data) in weights {
                let shape: Vec<usize> = serde_json::from_value(weight_data["shape"].clone())
                    .map_err(|e| IoError::SerializationError(e.to_string()))?;

                let data: Vec<f32> = serde_json::from_value(weight_data["floatValue"].clone())
                    .map_err(|e| IoError::SerializationError(e.to_string()))?;

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

                model
                    .weights
                    .insert(name.clone(), MLTensor::new(array, Some(name.clone())));
            }
        }

        Ok(model)
    }

    fn save_tensor(&self, tensor: &MLTensor, path: &Path) -> Result<()> {
        let tensor_data = serde_json::json!({
            "coreml_multiarray": {
                "shape": tensor.metadata.shape,
                "dataType": "FLOAT32",
                "floatValue": tensor.data.as_slice().expect("Operation failed").to_vec()
            }
        });

        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(multiarray) = tensor_data.get("coreml_multiarray") {
            let shape: Vec<usize> = serde_json::from_value(multiarray["shape"].clone())
                .map_err(|e| IoError::SerializationError(e.to_string()))?;

            let data: Vec<f32> = serde_json::from_value(multiarray["floatValue"].clone())
                .map_err(|e| IoError::SerializationError(e.to_string()))?;

            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 CoreML tensor format".to_string()))
    }
}
