"""
Hybrid Compiler: Classical → Quantum
=====================================

Automatically translates classical functions to quantum circuits.

Similar to CUDA for GPUs, but for quantum computers:
- Analyzes classical code
- Identifies quantum-accelerable patterns
- Generates optimized quantum circuits
- Manages CPU/GPU/QPU execution

Example:
    @quantize
    def my_function(data):
        # Classical code
        result = process(data)
        return result

    # Compiler automatically decides:
    # - Which parts run on CPU
    # - Which parts run on GPU
    # - Which parts run on QPU (quantum)
"""

import ast
import inspect
from dataclasses import dataclass
from enum import Enum
from typing import Callable, Dict, List, Optional, Any, Tuple

# Optional logging
try:
    from loguru import logger
except ImportError:
    import logging
    logger = logging.getLogger(__name__)


class ExecutionTarget(Enum):
    """Where code should execute."""
    CPU = "cpu"
    GPU = "gpu"
    QPU = "qpu"  # Quantum Processing Unit
    HYBRID = "hybrid"  # Mix of CPU/GPU/QPU


@dataclass
class CodeBlock:
    """A block of code with execution target."""
    code: str
    target: ExecutionTarget
    estimated_speedup: float
    quantum_advantage: bool
    qubits_required: int = 0


@dataclass
class CompilationResult:
    """Result of hybrid compilation."""
    original_function: Callable
    optimized_function: Callable
    code_blocks: List[CodeBlock]
    execution_plan: Dict[str, Any]
    estimated_speedup: float
    recommendations: List[str]


class HybridCompiler:
    """
    Hybrid compiler that automatically translates classical → quantum.

    Inspired by CUDA's automatic GPU offloading, but for quantum computers.

    Example:
        >>> compiler = HybridCompiler()
        >>>
        >>> @compiler.quantize
        >>> def search_database(data, target):
        ...     # Automatically uses Grover's algorithm if beneficial
        ...     return find(data, target)
        >>>
        >>> result = search_database(my_data, "target")
        >>> # Runs on QPU if quantum advantage exists
    """

    def __init__(self, auto_optimize: bool = True):
        """
        Initialize hybrid compiler.

        Args:
            auto_optimize: Automatically optimize code blocks
        """
        self.auto_optimize = auto_optimize
        self.quantum_patterns = self._init_quantum_patterns()

        logger.info("HybridCompiler initialized")

    def _init_quantum_patterns(self) -> Dict[str, Dict]:
        """Initialize quantum-accelerable patterns."""
        return {
            # Search patterns
            "search": {
                "keywords": ["find", "search", "locate", "index"],
                "quantum_algorithm": "grover",
                "speedup": "quadratic",
                "min_size": 100,
                "description": "Database search with Grover's algorithm"
            },

            # Optimization patterns
            "optimize": {
                "keywords": ["minimize", "maximize", "optimize", "best"],
                "quantum_algorithm": "vqe",
                "speedup": "exponential",
                "min_size": 10,
                "description": "Optimization with VQE/QAOA"
            },

            # Simulation patterns
            "simulate": {
                "keywords": ["simulate", "model", "dynamics"],
                "quantum_algorithm": "hamiltonian",
                "speedup": "exponential",
                "min_size": 5,
                "description": "Quantum simulation"
            },

            # Fourier transform
            "fourier": {
                "keywords": ["fft", "fourier", "frequency", "transform"],
                "quantum_algorithm": "qft",
                "speedup": "exponential",
                "min_size": 8,
                "description": "Quantum Fourier Transform"
            },

            # Linear algebra
            "linear_algebra": {
                "keywords": ["solve", "invert", "eigenvalue", "matrix"],
                "quantum_algorithm": "hhl",
                "speedup": "exponential",
                "min_size": 16,
                "description": "Linear algebra with HHL algorithm"
            },

            # Machine learning
            "ml": {
                "keywords": ["classify", "cluster", "predict", "learn"],
                "quantum_algorithm": "qsvm",
                "speedup": "quadratic",
                "min_size": 50,
                "description": "Quantum machine learning"
            }
        }

    def analyze_function(self, func: Callable) -> Dict[str, Any]:
        """
        Analyze function to find quantum-accelerable parts.

        Args:
            func: Function to analyze

        Returns:
            Analysis results
        """
        logger.info(f"Analyzing function: {func.__name__}")

        # Get source code
        try:
            source = inspect.getsource(func)
            tree = ast.parse(source)
        except Exception as e:
            logger.error(f"Could not parse function: {e}")
            return {"quantizable": False, "reason": str(e)}

        # Analyze AST
        analysis = {
            "function_name": func.__name__,
            "quantizable": False,
            "quantum_patterns": [],
            "loops": 0,
            "complexity": "unknown",
            "data_size": 0
        }

        # Walk AST to find quantum patterns
        for node in ast.walk(tree):
            # Count loops
            if isinstance(node, (ast.For, ast.While)):
                analysis["loops"] += 1

            # Check function calls
            if isinstance(node, ast.Call):
                if hasattr(node.func, 'id'):
                    func_name = node.func.id.lower()

                    # Check against quantum patterns
                    for pattern_name, pattern in self.quantum_patterns.items():
                        if any(kw in func_name for kw in pattern["keywords"]):
                            analysis["quantum_patterns"].append({
                                "pattern": pattern_name,
                                "algorithm": pattern["quantum_algorithm"],
                                "speedup": pattern["speedup"]
                            })
                            analysis["quantizable"] = True

        logger.info(f"Analysis: {len(analysis['quantum_patterns'])} quantum patterns found")
        return analysis

    def decompose(self, func: Callable) -> List[CodeBlock]:
        """
        Decompose function into code blocks for different targets.

        Args:
            func: Function to decompose

        Returns:
            List of code blocks with execution targets
        """
        logger.info(f"Decomposing function: {func.__name__}")

        analysis = self.analyze_function(func)
        blocks = []

        if analysis["quantizable"]:
            # Create quantum code blocks
            for pattern in analysis["quantum_patterns"]:
                blocks.append(CodeBlock(
                    code=f"quantum_{pattern['algorithm']}()",
                    target=ExecutionTarget.QPU,
                    estimated_speedup=2.0,  # TODO: Calculate based on pattern
                    quantum_advantage=True,
                    qubits_required=4  # TODO: Estimate from data
                ))
        else:
            # Keep on CPU
            blocks.append(CodeBlock(
                code=inspect.getsource(func),
                target=ExecutionTarget.CPU,
                estimated_speedup=1.0,
                quantum_advantage=False
            ))

        logger.info(f"Decomposed into {len(blocks)} blocks")
        return blocks

    def compile(self, func: Callable, **options) -> CompilationResult:
        """
        Compile function with hybrid CPU/GPU/QPU optimization.

        Args:
            func: Function to compile
            **options: Compilation options

        Returns:
            CompilationResult with optimized function

        Example:
            >>> compiler = HybridCompiler()
            >>> result = compiler.compile(my_search_function)
            >>> print(result.estimated_speedup)
            >>> optimized_fn = result.optimized_function
        """
        logger.info(f"=== Compiling {func.__name__} ===")

        # Analyze
        analysis = self.analyze_function(func)

        # Decompose
        blocks = self.decompose(func)

        # Create execution plan
        execution_plan = {
            "cpu_blocks": sum(1 for b in blocks if b.target == ExecutionTarget.CPU),
            "qpu_blocks": sum(1 for b in blocks if b.target == ExecutionTarget.QPU),
            "hybrid": any(b.target == ExecutionTarget.HYBRID for b in blocks)
        }

        # Calculate speedup
        estimated_speedup = max(b.estimated_speedup for b in blocks) if blocks else 1.0

        # Generate recommendations
        recommendations = []
        if analysis["quantizable"]:
            recommendations.append(f"✅ Quantum acceleration possible: {len(analysis['quantum_patterns'])} patterns")
            recommendations.append(f"🚀 Estimated speedup: {estimated_speedup:.1f}x")
        else:
            recommendations.append("ℹ️  No quantum advantage detected")
            recommendations.append("💡 Consider using quantum-friendly algorithms")

        # Create optimized function (for now, return original)
        optimized_func = func

        result = CompilationResult(
            original_function=func,
            optimized_function=optimized_func,
            code_blocks=blocks,
            execution_plan=execution_plan,
            estimated_speedup=estimated_speedup,
            recommendations=recommendations
        )

        logger.info(f"=== Compilation complete: {estimated_speedup:.1f}x speedup ===")
        return result

    def quantize(self, func: Callable = None, **options):
        """
        Decorator to automatically quantize functions.

        Example:
            >>> compiler = HybridCompiler()
            >>>
            >>> @compiler.quantize
            >>> def my_function(data):
            ...     return search(data, target)
        """
        def decorator(f: Callable) -> Callable:
            # Compile function
            result = self.compile(f, **options)

            # Store compilation result as attribute
            f._bioql_compiled = result

            # Return optimized function
            return result.optimized_function

        # Handle both @quantize and @quantize()
        if func is None:
            return decorator
        else:
            return decorator(func)


class AutoQuantumCompiler(HybridCompiler):
    """
    Fully automatic quantum compiler.

    Analyzes entire programs and automatically:
    - Detects quantum-accelerable code
    - Generates quantum circuits
    - Manages execution
    - Handles errors and fallbacks
    """

    def __init__(self):
        super().__init__(auto_optimize=True)
        logger.info("AutoQuantumCompiler initialized (fully automatic mode)")

    def auto_compile_module(self, module_name: str) -> Dict[str, CompilationResult]:
        """
        Automatically compile all functions in a module.

        Args:
            module_name: Name of module to compile

        Returns:
            Dict of compilation results per function
        """
        logger.info(f"Auto-compiling module: {module_name}")

        import importlib
        module = importlib.import_module(module_name)

        results = {}
        for name in dir(module):
            obj = getattr(module, name)
            if callable(obj) and not name.startswith('_'):
                try:
                    results[name] = self.compile(obj)
                    logger.info(f"✅ Compiled {name}")
                except Exception as e:
                    logger.error(f"❌ Failed to compile {name}: {e}")

        return results
