#!/usr/bin/env python3
"""
NUITKA RAY REPLACEMENT - MAXIMUM PERFORMANCE VERSION

A Ray-API-compatible processing solution optimized for maximum performance.
Uses intelligent task scheduling, enhanced GPU detection, and process pools.

Features:
- Ray-like @ray.remote decorators
- Intelligent task distribution (CPU vs I/O vs GPU)
- Enhanced GPU detection (CUDA + OpenCL)
- Process pool for CPU-intensive tasks
- Thread pool for I/O and GPU tasks
- Maximum resource utilization
"""

import os
import sys
import multiprocessing
import threading
import concurrent.futures
import queue
import time
import functools
from typing import Any, Callable, Dict, List, Optional, Union

class NuitkaRayReplacement:
    """
    Maximum performance Ray replacement with intelligent task scheduling.
    """
    
    def __init__(self):
        self.initialized = False
        self.num_cpus = multiprocessing.cpu_count()
        self.num_gpus = self._detect_gpus()
        self.thread_pool = None
        self.process_pool = None
        self._remote_functions = {}
        self._is_shutting_down = False
        self._shutdown_lock = threading.Lock()
        
    def _detect_gpus(self):
        """Detect available GPUs with CUDA and OpenCL support"""
        gpu_count = 0
        
        # Try CUDA detection first (most common for ML/image processing)
        try:
            import torch
            if torch.cuda.is_available():
                gpu_count = torch.cuda.device_count()
                print(f"🎮 CUDA GPUs detected: {gpu_count}", flush=True)
                import sys
                sys.stdout.flush()
                return gpu_count
        except ImportError:
            pass
        except Exception as e:
            pass
        
        # Try OpenCL detection as fallback
        try:
            import pyopencl as cl
            platforms = cl.get_platforms()
            for platform in platforms:
                devices = platform.get_devices(cl.device_type.GPU)
                gpu_count += len(devices)
            if gpu_count > 0:
                return gpu_count
        except ImportError:
            pass
        except Exception as e:
            pass
        
        return 0
    
    def _print_gpu_info(self):
        """Print detailed GPU information for user visibility"""
        pass  # GPU info is printed during detection

    def init(self, num_cpus=None, num_gpus=None, local_mode=None, **kwargs):
        """Initialize the Ray replacement system with MAXIMUM PERFORMANCE"""
        if self.initialized:
            return
        
        # Reset shutdown flag on initialization
        with self._shutdown_lock:
            self._is_shutting_down = False

        # MAXIMUM PERFORMANCE: Use all available resources
        available_cpus = multiprocessing.cpu_count()
        self.num_cpus = num_cpus or available_cpus
        self.num_gpus = num_gpus or self._detect_gpus()

        # Ensure we use maximum available cores (no artificial limits)
        self.num_cpus = max(4, min(self.num_cpus, available_cpus))

        # PERFORMANCE OPTIMIZATION: Create both thread AND process pools
        # Thread pool for I/O-bound and GPU-accelerated tasks
        self.thread_pool = concurrent.futures.ThreadPoolExecutor(
            max_workers=self.num_cpus,
            thread_name_prefix="ChlorosThread"
        )

        # Process pool for CPU-intensive parallel processing
        try:
            # Use half the cores for processes to avoid oversubscription
            process_workers = max(2, self.num_cpus // 2)
            self.process_pool = concurrent.futures.ProcessPoolExecutor(
                max_workers=process_workers,
                mp_context=multiprocessing.get_context('spawn')  # Safer for Nuitka
            )
        except Exception as e:
            self.process_pool = None
            
        self.initialized = True
    
    def is_initialized(self):
        """Check if Ray replacement is initialized"""
        return self.initialized
    
    def available_resources(self):
        """Return available resources"""
        return {
            'CPU': self.num_cpus,
            'GPU': self.num_gpus
        }
    
    def shutdown(self):
        """Shutdown the Ray replacement system"""
        with self._shutdown_lock:
            if self._is_shutting_down:
                return
            self._is_shutting_down = True
        
        if self.thread_pool:
            self.thread_pool.shutdown(wait=True)
        if self.process_pool:
            self.process_pool.shutdown(wait=True)
        self.initialized = False
    
    def remote(self, *args, **kwargs):
        """Ray-compatible @ray.remote decorator"""
        if args and callable(args[0]):
            # Direct decoration: @ray.remote
            func = args[0]
            return RemoteWrapper(func, self)
        else:
            # Parameterized decoration: @ray.remote(...)
            def decorator(func):
                return RemoteWrapper(func, self)
            return decorator

class ObjectRef:
    """Ray-compatible object reference"""
    
    def __init__(self, future):
        self._future = future
        self._completed = False
        self._result = None
    
    def get(self, timeout=None):
        """Get the result (blocking)"""
        if not self._completed:
            self._result = self._future.result(timeout=timeout)
            self._completed = True
        return self._result
    
    def ready(self):
        """Check if result is ready"""
        if self._completed:
            return True
        return self._future.done()

class RemoteWrapper:
    """Wrapper for remote functions"""
    
    def __init__(self, func, ray_system):
        self.func = func
        self.ray_system = ray_system
        
    def remote(self, *args, **kwargs):
        """Execute the function remotely"""
        return RemoteFunction(self.func, self.ray_system, *args, **kwargs)

class RemoteFunction:
    """Ray-compatible remote function execution"""
    
    def __init__(self, func, ray_system, *args, **kwargs):
        self.func = func
        self.ray_system = ray_system
        self.args = args
        self.kwargs = kwargs
        
        # Check if system is shutting down
        if ray_system._is_shutting_down:
            # Create a dummy future that will return None
            dummy_future = concurrent.futures.Future()
            dummy_future.set_result(None)
            self._object_ref = ObjectRef(dummy_future)
            return
        
        # PERFORMANCE OPTIMIZATION: Intelligent pool selection
        # Use process pool for CPU-intensive tasks, thread pool for I/O and GPU tasks
        use_process_pool = self._should_use_process_pool(func)
        
        try:
            if use_process_pool and ray_system.process_pool is not None:
                try:
                    # Try process pool for maximum CPU parallelism
                    future = ray_system.process_pool.submit(func, *args, **kwargs)
                except Exception as e:
                    # Fallback to thread pool if process pool fails (pickling issues)
                    future = ray_system.thread_pool.submit(func, *args, **kwargs)
            else:
                # Use thread pool for I/O-bound and GPU-accelerated tasks
                future = ray_system.thread_pool.submit(func, *args, **kwargs)
            
            self._object_ref = ObjectRef(future)
        except RuntimeError as e:
            if "cannot schedule new futures after shutdown" in str(e):
                # Create a dummy future that will return None
                dummy_future = concurrent.futures.Future()
                dummy_future.set_result(None)
                self._object_ref = ObjectRef(dummy_future)
            else:
                raise
    
    def _should_use_process_pool(self, func):
        """Determine if function should use process pool based on heuristics"""
        # CPU-intensive function names that benefit from process parallelism
        cpu_intensive_keywords = [
            'process', 'compute', 'calculate', 'transform', 'debayer',
            'calibrate', 'correct', 'enhance', 'filter', 'analyze'
        ]
        
        func_name = func.__name__.lower()
        
        # Use process pool for CPU-intensive operations
        return any(keyword in func_name for keyword in cpu_intensive_keywords)
    
    def get(self, timeout=None):
        """Get the result"""
        return self._object_ref.get(timeout=timeout)
    
    def ready(self):
        """Check if result is ready"""
        return self._object_ref.ready()

# Global instance
_nuitka_ray = NuitkaRayReplacement()

# Ray-compatible API
def init(*args, **kwargs):
    """Ray-compatible ray.init()"""
    return _nuitka_ray.init(*args, **kwargs)

def is_initialized():
    """Ray-compatible ray.is_initialized()"""
    return _nuitka_ray.is_initialized()

def available_resources():
    """Ray-compatible ray.available_resources()"""
    return _nuitka_ray.available_resources()

def shutdown():
    """Ray-compatible ray.shutdown()"""
    return _nuitka_ray.shutdown()

def remote(*args, **kwargs):
    """Ray-compatible @ray.remote decorator"""
    return _nuitka_ray.remote(*args, **kwargs)

def get(object_refs):
    """Ray-compatible ray.get()"""
    if isinstance(object_refs, list):
        return [obj_ref.get() for obj_ref in object_refs]
    else:
        return object_refs.get()

def put(value):
    """Ray-compatible ray.put() - for object store compatibility"""
    # In the replacement, we don't have a distributed object store,
    # so just return the value wrapped in a simple ref object
    class ObjectRef:
        def __init__(self, val):
            self.value = val
        def get(self):
            return self.value
    return ObjectRef(value)

def wait(object_refs, num_returns=1, timeout=None):
    """Ray-compatible ray.wait() - waits for futures to complete
    
    Args:
        object_refs: List of futures or single future
        num_returns: Number of futures to wait for (default 1)
        timeout: Timeout in seconds (ignored in this implementation)
    
    Returns:
        Tuple of (ready, not_ready) lists
    """
    import time
    
    # Handle single ref
    if not isinstance(object_refs, list):
        object_refs = [object_refs]
    
    ready = []
    not_ready = []
    
    # Simple implementation: check if futures are done
    for ref in object_refs:
        if hasattr(ref, 'done') and callable(ref.done):
            if ref.done():
                ready.append(ref)
            else:
                not_ready.append(ref)
        else:
            # If no done() method, assume it's ready
            ready.append(ref)
    
    # If we need more ready futures and there are unready ones, wait a bit
    start_time = time.time()
    while len(ready) < num_returns and not_ready:
        if timeout and (time.time() - start_time) > timeout:
            break
        
        # Check again
        time.sleep(0.01)  # Small sleep to avoid busy waiting
        still_not_ready = []
        for ref in not_ready:
            if hasattr(ref, 'done') and callable(ref.done):
                if ref.done():
                    ready.append(ref)
                else:
                    still_not_ready.append(ref)
            else:
                ready.append(ref)
        not_ready = still_not_ready
    
    return ready, not_ready

# Mode detection (compiled vs standard Python)
# Removed unnecessary debug prints - system works in both modes
