#!/usr/bin/env python3
"""Multi-Language Pipelines - Chain multiple languages for complex operations"""

import time
from typing import Any, Dict, List

class PolyglotPipeline:
    """Sequential multi-language pipelines"""

    def __init__(self):
        from whitemagic.optimization.polyglot_specialists import PolyglotSpecialists
        self.specialists = PolyglotSpecialists()

    def memory_analysis_pipeline(self, memory_id: str, content: str) -> Dict[str, Any]:
        """Pipeline: Python → Rust → Zig → Mojo → Julia → Python"""
        start = time.time()
        results = {"pipeline": "memory_analysis", "stages": []}

        # Stage 1: Rust extracts patterns
        r1 = self.specialists.extract_patterns(content, limit=100)
        results["stages"].append({"stage": 1, "language": r1.language, "time_ms": r1.execution_time_ms})

        # Stage 2: Zig computes similarity matrix
        if r1.success and len(r1.result) > 1:
            vectors = [[float(ord(c)) for c in p[:10].ljust(10)] for p in r1.result[:20]]
            r2 = self.specialists.distance_matrix(vectors)
            results["stages"].append({"stage": 2, "language": r2.language, "time_ms": r2.execution_time_ms})

        # Stage 3: Julia statistical analysis
        if r1.success:
            pattern_lengths = [len(p) for p in r1.result]
            r3 = self.specialists.statistical_analysis(pattern_lengths)
            results["stages"].append({"stage": 3, "language": r3.language, "time_ms": r3.execution_time_ms})

        results["total_time_ms"] = (time.time() - start) * 1000
        results["languages_used"] = len(set(s["language"] for s in results["stages"]))
        return results

    def batch_processing_pipeline(self, memories: List[dict]) -> Dict[str, Any]:
        """Pipeline: Python → Mojo → Haskell → Python"""
        start = time.time()
        results = {"pipeline": "batch_processing", "stages": []}

        # Stage 1: Mojo batch encoding
        r1 = self.specialists.batch_encode(memories, int(time.time()))
        results["stages"].append({"stage": 1, "language": r1.language, "time_ms": r1.execution_time_ms})

        # Stage 2: Haskell rule evaluation
        r2 = self.specialists.evaluate_rules("batch_process", {"count": len(memories)})
        results["stages"].append({"stage": 2, "language": r2.language, "time_ms": r2.execution_time_ms})

        results["total_time_ms"] = (time.time() - start) * 1000
        results["languages_used"] = len(set(s["language"] for s in results["stages"]))
        return results

    def concurrent_search_pipeline(self, query: str, corpus: List) -> Dict[str, Any]:
        """Pipeline: Python → Rust → Julia → Python"""
        start = time.time()
        results = {"pipeline": "concurrent_search", "stages": []}

        # Stage 1: Rust similarity search
        r1 = self.specialists.similarity_search(query, corpus, threshold=0.5, limit=50)
        results["stages"].append({"stage": 1, "language": r1.language, "time_ms": r1.execution_time_ms})

        # Stage 2: Julia analyze results
        if r1.success and r1.result:
            scores = [score for _, score in r1.result]
            r2 = self.specialists.statistical_analysis(scores)
            results["stages"].append({"stage": 2, "language": r2.language, "time_ms": r2.execution_time_ms})

        results["total_time_ms"] = (time.time() - start) * 1000
        results["languages_used"] = len(set(s["language"] for s in results["stages"]))
        return results
