#pragma once // Step 501: Scenario - Cross-Language Port // End-to-end scenario for porting a Python ML pipeline to Rust. #include "BehavioralEquivalence.h" #include "ConcurrencyTranslator.h" #include "IntentTranslator.h" #include "MemoryModelTranslator.h" #include "TranspilationConfidence.h" #include #include #include #include struct PortedFunctionResult { std::string name; std::string source; std::string intent; bool hotLoop = false; std::vector annotations; TranslationChoice translation; ConfidenceScore confidence; BehavioralCheckResult equivalence; }; struct CrossLanguagePortScenarioResult { std::string sourceLanguage; std::string targetLanguage; std::string pipelineSource; std::vector functions; IntentTranslationResult intentTranslations; MemoryAnalysisResult memoryAnalysis; ConcurrencyAnalysisResult concurrencyAnalysis; std::vector reviewRequiredFunctions; std::vector notes; float averageConfidence() const { if (functions.empty()) return 0.0f; float sum = 0.0f; for (const auto& f : functions) sum += f.confidence.score; return sum / static_cast(functions.size()); } }; class CrossLanguagePortScenarioRunner { public: static CrossLanguagePortScenarioResult run() { CrossLanguagePortScenarioResult out; out.sourceLanguage = "python"; out.targetLanguage = "rust"; const auto funcs = sampleFunctions(); out.pipelineSource = composePipelineSource(funcs); out.intentTranslations = IntentTranslator::translate(funcs, out.sourceLanguage, out.targetLanguage); out.memoryAnalysis = MemoryModelTranslator::analyze( out.pipelineSource, out.sourceLanguage, out.targetLanguage); out.concurrencyAnalysis = ConcurrencyTranslator::analyze( out.pipelineSource, out.sourceLanguage, out.targetLanguage); for (size_t i = 0; i < funcs.size(); ++i) { PortedFunctionResult pf; pf.name = funcs[i].name; pf.source = funcs[i].source; pf.intent = funcs[i].intent; pf.hotLoop = isHotLoop(funcs[i].source); if (pf.hotLoop) pf.annotations.push_back("@HotLoop(true)"); if (!funcs[i].intent.empty()) pf.annotations.push_back("@Intent(" + funcs[i].intent + ")"); pf.translation = out.intentTranslations.getTranslation(funcs[i].name); pf.confidence = TranspilationScorer::score( funcs[i].name, funcs[i].source, pf.translation.targetCode, out.sourceLanguage, out.targetLanguage, !funcs[i].intent.empty()); pf.equivalence = BehavioralChecker::check( funcs[i].name, funcs[i].source, pf.translation.targetCode, out.sourceLanguage, out.targetLanguage, {}); if (pf.confidence.reviewRequired) out.reviewRequiredFunctions.push_back(pf.name); out.functions.push_back(std::move(pf)); } out.notes.push_back("Cross-language Python->Rust scenario executed"); out.notes.push_back("Includes hot-loop tagging, transpilation confidence, and review surfacing"); return out; } private: static std::vector sampleFunctions() { return { {"train_epoch", "reduce gradient sum", "def train_epoch(samples):\n" " total = 0\n" " for i in range(len(samples)):\n" " total += samples[i]\n" " return total\n"}, {"normalize_scores", "map transform normalize", "def normalize_scores(scores):\n" " return [s / 255.0 for s in scores]\n"}, {"async_fetch_batch", "", "import asyncio\n" "async def async_fetch_batch(client, urls):\n" " out = []\n" " for u in urls:\n" " out.append(await client.get(u))\n" " return out\n"} }; } static std::string composePipelineSource(const std::vector& funcs) { std::string out; for (const auto& f : funcs) { if (!out.empty()) out += "\n"; out += f.source; } return out; } static bool isHotLoop(const std::string& source) { std::string s = source; std::transform(s.begin(), s.end(), s.begin(), [](unsigned char c) { return static_cast(std::tolower(c)); }); return s.find("for ") != std::string::npos || s.find("while ") != std::string::npos || s.find("range(") != std::string::npos; } };