//
//  Mark.swift
//  Mark
//
//  Author: Ainouche Abderahmane & Mythologic
//  On-device multilingual diacritic and tone restoration across 37 languages.
//  Core ML inference on Apple Neural Engine (Swift 6.3).
//

import CoreML
import Foundation

public actor Mark {
    public static let supportedLanguages: [String] = [
        "ak", "ar", "az", "ca", "cs", "cy", "ee", "es", "ff", "fr",
        "ga", "gn", "ha", "he", "hr", "ht", "hu", "ig", "ku", "ln",
        "lt", "lv", "mi", "pl", "pt", "qu", "ro", "sk", "sl", "sm",
        "sr", "tk", "tr", "uz", "vi", "wo", "yo",
    ]

    private let model: MLModel
    private let tags: [String]
    private let languageMap: [String: Int]
    private let splitRegex: Regex<AnyRegexOutput>

    public enum MarkError: LocalizedError {
        case modelNotFound
        case unsupportedLanguage(String)
        case predictionFailed(String)
        case resourceMissing(String)

        public var errorDescription: String? {
            switch self {
            case .modelNotFound:
                return "Mark Core ML model file not found in bundle or provided path."
            case .unsupportedLanguage(let lang):
                return "Unsupported language code '\(lang)'. Supported: \(Mark.supportedLanguages.joined(separator: ", "))"
            case .predictionFailed(let reason):
                return "Prediction failed: \(reason)"
            case .resourceMissing(let name):
                return "Required resource '\(name)' missing from bundle."
            }
        }
    }

    private static var defaultBundle: Bundle {
        #if SWIFT_PACKAGE
        return Bundle.module
        #else
        return Bundle.main
        #endif
    }

    /// Initialize Mark with a compiled MLModel or load from a custom URL.
    public init(modelURL: URL? = nil, configuration: MLModelConfiguration = MLModelConfiguration()) throws {
        let resolvedURL: URL
        if let customURL = modelURL {
            resolvedURL = customURL
        } else if let bundled = Self.defaultBundle.url(forResource: "mark", withExtension: "mlmodelc") {
            resolvedURL = bundled
        } else if let bundled = Bundle.main.url(forResource: "mark", withExtension: "mlmodelc") {
            resolvedURL = bundled
        } else {
            throw MarkError.modelNotFound
        }

        self.model = try MLModel(contentsOf: resolvedURL, configuration: configuration)

        guard let tagsURL = Self.defaultBundle.url(forResource: "tags", withExtension: "json") ??
                            Bundle.main.url(forResource: "tags", withExtension: "json") else {
            throw MarkError.resourceMissing("tags.json")
        }
        let data = try Data(contentsOf: tagsURL)
        self.tags = try JSONDecoder().decode([String].self, from: data)

        var map: [String: Int] = [:]
        for (index, code) in Mark.supportedLanguages.enumerated() {
            map[code] = index
        }
        self.languageMap = map

        // Precompile regex for whitespace/word tokenization
        self.splitRegex = try Regex(#"\S+|\s+"#)
    }

    /// Restore diacritics and tones on arbitrary input text.
    ///
    /// - Parameters:
    ///   - text: Raw unmarked input string.
    ///   - lang: Target ISO 639-1 language code (e.g. "yo", "ar", "es").
    ///   - margin: Confidence threshold over KEEP (default: 0.0). When set to 0.4+,
    ///     suppresses hypothesis flicker during streaming keyboard input.
    public func restore(_ text: String, lang: String, margin: Float = 0.0) throws -> String {
        guard !text.isEmpty else { return "" }

        let normalizedLang = lang.trimmingCharacters(in: .whitespacesAndNewlines).lowercased()
        guard let langId = languageMap[normalizedLang] else {
            throw MarkError.unsupportedLanguage(lang)
        }

        let chunks = splitIntoChunks(text, maxBytes: 480)
        var restoredChunks: [String] = []

        for chunk in chunks {
            let restored = try restoreChunk(chunk, langId: langId, margin: margin)
            restoredChunks.append(restored)
        }

        return restoredChunks.joined()
    }

    /// Single chunk restoration (under 512 bytes) using MLShapedArray.
    private func restoreChunk(_ chunk: String, langId: Int, margin: Float = 0.0) throws -> String {
        let utf8Bytes = Array(chunk.utf8)
        guard !utf8Bytes.isEmpty else { return "" }

        let seqLen = min(utf8Bytes.count, 512)

        // Type-safe MLShapedArray inputs (no manual memory manipulation)
        let inputScalars: [Int32] = (0..<512).map { i in
            i < seqLen ? Int32(utf8Bytes[i]) : Int32(0)
        }
        let inputShaped = MLShapedArray<Int32>(scalars: inputScalars, shape: [1, 512])
        let langShaped = MLShapedArray<Int32>(scalars: [Int32(langId)], shape: [1])

        let featureProvider = try MLDictionaryFeatureProvider(dictionary: [
            "input_ids": MLFeatureValue(multiArray: MLMultiArray(inputShaped)),
            "lang_ids": MLFeatureValue(multiArray: MLMultiArray(langShaped)),
        ])

        let prediction = try model.prediction(from: featureProvider)
        guard let logitsFeature = prediction.featureValue(for: "logits")?.multiArrayValue else {
            throw MarkError.predictionFailed("Output logits tensor missing")
        }

        let shapedLogits = MLShapedArray<Float>(converting: logitsFeature)
        let scalars = shapedLogits.scalars
        let numTags = self.tags.count
        var preds: [Int] = []
        preds.reserveCapacity(seqLen)

        // Fast contiguous scalar access for calibrated margin or argmax
        for pos in 0..<seqLen {
            let offset = pos * numTags
            if margin > 0.0 && numTags > 1 {
                let keepVal = scalars[offset + 0]
                var bestVal = -Float.infinity
                var bestIdx = 0
                for tagIdx in 1..<numTags {
                    let val = scalars[offset + tagIdx]
                    if val > bestVal {
                        bestVal = val
                        bestIdx = tagIdx
                    }
                }
                preds.append((bestVal - keepVal >= margin) ? bestIdx : 0)
            } else {
                var maxVal = -Float.infinity
                var maxIdx = 0
                for tagIdx in 0..<numTags {
                    let val = scalars[offset + tagIdx]
                    if val > maxVal {
                        maxVal = val
                        maxIdx = tagIdx
                    }
                }
                preds.append(maxIdx)
            }
        }

        return decodePrediction(chunk: chunk, utf8Bytes: utf8Bytes, predTags: preds)
    }

    /// Decode raw character bytes and predicted tag sequence into marked text.
    private func decodePrediction(chunk: String, utf8Bytes: [UInt8], predTags: [Int]) -> String {
        var result = ""
        var byteOffset = 0

        for char in chunk {
            let charByteCount = String(char).utf8.count
            let tagStr: String
            if byteOffset < predTags.count {
                let tagId = predTags[byteOffset]
                tagStr = (tagId >= 0 && tagId < tags.count) ? tags[tagId] : "KEEP"
            } else {
                tagStr = "KEEP"
            }

            if tagStr.hasPrefix("P:") && tagStr.contains("->") {
                let parts = tagStr.dropFirst(2).components(separatedBy: "->")
                if parts.count == 2 {
                    result.append(parts[1])
                } else {
                    result.append(char)
                }
            } else if tagStr.hasPrefix("M:") {
                let hexMarks = tagStr.dropFirst(2).components(separatedBy: "+")
                var combiningString = String(char)
                for hex in hexMarks {
                    if let scalarVal = UInt32(hex, radix: 16), let scalar = UnicodeScalar(scalarVal) {
                        combiningString.append(String(scalar))
                    }
                }
                result.append(combiningString.precomposedStringWithCanonicalMapping)
            } else {
                result.append(char)
            }

            byteOffset += charByteCount
        }

        return result
    }

    /// Swift Regex tokenization preserving whitespace and boundaries.
    private func splitIntoChunks(_ text: String, maxBytes: Int) -> [String] {
        guard text.utf8.count > maxBytes else { return [text] }

        let matches = text.matches(of: splitRegex)
        let tokens = matches.map { String(text[$0.range]) }
        var chunks: [String] = []
        var currentChunk = ""

        for token in tokens {
            if (currentChunk + token).utf8.count <= maxBytes {
                currentChunk += token
            } else {
                if !currentChunk.isEmpty {
                    chunks.append(currentChunk)
                    currentChunk = ""
                }
                if token.utf8.count <= maxBytes {
                    currentChunk = token
                } else {
                    // Extremely long token exceeding maxBytes
                    for char in token {
                        let s = String(char)
                        if (currentChunk + s).utf8.count > maxBytes {
                            chunks.append(currentChunk)
                            currentChunk = s
                        } else {
                            currentChunk += s
                        }
                    }
                }
            }
        }

        if !currentChunk.isEmpty {
            chunks.append(currentChunk)
        }

        return chunks
    }
}