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from dataclasses import dataclass, field
from typing import List
import numpy as np
 
@dataclass
class MultiInstanceState:
    """
    Complete state for multiple-instance deadlock detection.
    
    Invariants maintained:
    1. Available + sum(Allocation across all processes) = Total resources
    2. Request[i][j] >= 0 for all i, j
    3. Allocation[i][j] >= 0 for all i, j
    4. Available[j] >= 0 for all j
    """
    num_processes: int
    num_resource_types: int
    
    # Total instances of each resource type (for validation)
    total: np.ndarray = field(default_factory=lambda: np.array([]))
    
    # Currently available (unallocated)
    available: np.ndarray = field(default_factory=lambda: np.array([]))
    
    # allocation[i][j] = instances of Rj held by Pi
    allocation: np.ndarray = field(default_factory=lambda: np.array([]))
    
    # request[i][j] = instances of Rj that Pi is waiting for
    request: np.ndarray = field(default_factory=lambda: np.array([]))
    
    def __post_init__(self):
        n, m = self.num_processes, self.num_resource_types
        if self.total.size == 0:
            self.total = np.zeros(m, dtype=np.int32)
        if self.available.size == 0:
            self.available = np.zeros(m, dtype=np.int32)
        if self.allocation.size == 0:
            self.allocation = np.zeros((n, m), dtype=np.int32)
        if self.request.size == 0:
            self.request = np.zeros((n, m), dtype=np.int32)
    
    def validate_invariants(self) -> List[str]:
        """Check that state invariants hold."""
        errors = []
        
        # Check non-negativity
        if np.any(self.available < 0):
            errors.append("Available has negative values")
        if np.any(self.allocation < 0):
            errors.append("Allocation has negative values")
        if np.any(self.request < 0):
            errors.append("Request has negative values")
        
        # Check resource totals
        total_allocated = np.sum(self.allocation, axis=0)
        expected_available = self.total - total_allocated
        if not np.array_equal(self.available, expected_available):
            errors.append(
                f"Available mismatch: have {self.available}, "
                f"expected {expected_available}"
            )
        
        return errors
    
    def print_state(self, title: str = "System State"):
        """Pretty-print the current state."""
        print(f"
{'='*50}")
        print(f"{title}")
        print(f"{'='*50}")
        print(f"Total resources:     {self.total}")
        print(f"Available resources: {self.available}")
        print(f"
Allocation matrix:")
        print(f"{'Process':<10}", end="")
        for j in range(self.num_resource_types):
            print(f"R{j:<4}", end="")
        print()
        for i in range(self.num_processes):
            print(f"P{i:<9}", end="")
            for j in range(self.num_resource_types):
                print(f"{self.allocation[i][j]:<5}", end="")
            print()
        
        print(f"
Request matrix:")
        print(f"{'Process':<10}", end="")
        for j in range(self.num_resource_types):
            print(f"R{j:<4}", end="")
        print()
        for i in range(self.num_processes):
            print(f"P{i:<9}", end="")
            for j in range(self.num_resource_types):
                print(f"{self.request[i][j]:<5}", end="")
            print()
 
 
def create_example_state() -> MultiInstanceState:
    """Create an example state for demonstration."""
    state = MultiInstanceState(
        num_processes=5,
        num_resource_types=3,
    )
    
    # Total resources: [7, 2, 6]
    state.total = np.array([7, 2, 6], dtype=np.int32)
    
    # Current allocation
    state.allocation = np.array([
        [0, 1, 0],  # P0
        [2, 0, 0],  # P1
        [3, 0, 3],  # P2
        [2, 1, 1],  # P3
        [0, 0, 2],  # P4
    ], dtype=np.int32)
    
    # Current requests
    state.request = np.array([
        [0, 0, 0],  # P0: not waiting
        [2, 0, 2],  # P1: waiting for 2 of R0, 2 of R2
        [0, 0, 0],  # P2: not waiting
        [1, 0, 0],  # P3: waiting for 1 of R0
        [0, 0, 2],  # P4: waiting for 2 of R2
    ], dtype=np.int32)
    
    # Calculate available
    state.available = state.total - np.sum(state.allocation, axis=0)
    
    return state
 
 
if __name__ == "__main__":
    state = create_example_state()
    errors = state.validate_invariants()
    if errors:
        print("Invariant violations:", errors)
    else:
        state.print_state("Example Multi-Instance State")

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import numpy as np
from typing import List, Tuple, Set
from dataclasses import dataclass
 
@dataclass  
class DetectionResult:
    """Result of multiple-instance deadlock detection."""
    has_deadlock: bool
    deadlocked_processes: List[int]
    safe_sequence: List[int]  # Order in which safe processes can complete
    iterations: int           # Number of algorithm iterations
    
def detect_deadlock_multiple(
    num_processes: int,
    num_resources: int,
    available: np.ndarray,
    allocation: np.ndarray,
    request: np.ndarray,
    verbose: bool = False
) -> DetectionResult:
    """
    Detect deadlock in a system with multiple resource instances.
    
    Time Complexity: O(n^2 * m) where n = processes, m = resource types
    Space Complexity: O(n + m)
    
    Args:
        num_processes: Number of processes in the system
        num_resources: Number of resource types
        available: Vector of available instances per resource type
        allocation: Matrix of currently allocated resources
        request: Matrix of current resource requests (blocking)
        verbose: If True, print step-by-step execution
        
    Returns:
        DetectionResult with deadlock information and safe sequence
    """
    # Work vector: available resources we can use
    work = available.copy()
    
    # Finish array: which processes have "completed" in simulation
    # Initially, processes not requesting anything are considered finished
    finish = np.array([
        np.all(request[i] == 0) 
        for i in range(num_processes)
    ])
    
    if verbose:
        print(f"Initial state:")
        print(f"  Work = {work}")
        print(f"  Finish = {finish}")
        print(f"  (True = not waiting or already processed)")
    
    safe_sequence = []
    iterations = 0
    
    # Add already-finished processes to safe sequence
    for i in range(num_processes):
        if finish[i]:
            safe_sequence.append(i)
    
    # Repeat until no progress can be made
    made_progress = True
    while made_progress:
        made_progress = False
        iterations += 1
        
        for i in range(num_processes):
            if finish[i]:
                continue  # Already finished
            
            # Check if Request[i] <= Work (element-wise)
            if np.all(request[i] <= work):
                # This process can complete
                if verbose:
                    print(f"
Iteration {iterations}: Process P{i} can complete")
                    print(f"  Request[{i}] = {request[i]} <= Work = {work}")
                
                # Simulate completion: reclaim allocated resources
                work = work + allocation[i]
                finish[i] = True
                safe_sequence.append(i)
                made_progress = True
                
                if verbose:
                    print(f"  Reclaim Allocation[{i}] = {allocation[i]}")
                    print(f"  New Work = {work}")
    
    # Any process still not finished is deadlocked
    deadlocked = [i for i in range(num_processes) if not finish[i]]
    
    if verbose:
        print(f"
Final state after {iterations} iterations:")
        print(f"  Finish = {finish}")
        print(f"  Safe sequence = {safe_sequence}")
        print(f"  Deadlocked = {deadlocked}")
    
    return DetectionResult(
        has_deadlock=len(deadlocked) > 0,
        deadlocked_processes=deadlocked,
        safe_sequence=safe_sequence,
        iterations=iterations
    )
 
 
def demo_detection():
    """Demonstrate the detection algorithm with examples."""
    
    print("="*60)
    print("EXAMPLE 1: No Deadlock")
    print("="*60)
    
    # State from Silberschatz example
    available1 = np.array([0, 0, 0])
    allocation1 = np.array([
        [0, 1, 0],
        [2, 0, 0],
        [3, 0, 3],
        [2, 1, 1],
        [0, 0, 2],
    ])
    request1 = np.array([
        [0, 0, 0],  # P0: not waiting (can complete immediately)
        [2, 0, 2],  # P1: waiting
        [0, 0, 0],  # P2: not waiting (can complete immediately)
        [1, 0, 0],  # P3: waiting
        [0, 0, 2],  # P4: waiting
    ])
    
    result1 = detect_deadlock_multiple(
        5, 3, available1, allocation1, request1, verbose=True
    )
    
    print(f"
Result: {'DEADLOCK' if result1.has_deadlock else 'NO DEADLOCK'}")
    print(f"Safe sequence: {result1.safe_sequence}")
    
    print("
" + "="*60)
    print("EXAMPLE 2: Deadlock Present")
    print("="*60)
    
    # Modified state that creates deadlock
    request2 = np.array([
        [0, 0, 0],  # P0: not waiting
        [2, 0, 2],  # P1: waiting for 2 of R0, 2 of R2
        [0, 0, 1],  # P2: now waiting for 1 of R2 (changed!)
        [1, 0, 0],  # P3: waiting
        [0, 0, 2],  # P4: waiting
    ])
    
    result2 = detect_deadlock_multiple(
        5, 3, available1, allocation1, request2, verbose=True
    )
    
    print(f"
Result: {'DEADLOCK' if result2.has_deadlock else 'NO DEADLOCK'}")
    if result2.has_deadlock:
        print(f"Deadlocked processes: {result2.deadlocked_processes}")
 
 
if __name__ == "__main__":
    demo_detection()

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#include <vector>
#include <bitset>
#include <algorithm>
#include <numeric>
#include <cstring>
#include <immintrin.h>  // For SIMD
 
/**
 * Optimized multiple-instance deadlock detector.
 * 
 * Optimizations:
 * 1. Bitset for finish tracking (cache-friendly)
 * 2. Skip processes with zero requests upfront
 * 3. SIMD comparison for vector operations
 * 4. Track candidate processes to avoid full scans
 */
class OptimizedDetector {
public:
    static constexpr int MAX_PROCESSES = 4096;
    static constexpr int MAX_RESOURCES = 256;
    
    struct DetectionInput {
        int num_processes;
        int num_resources;
        int* available;           // [num_resources]
        int* allocation;          // [num_processes * num_resources]
        int* request;             // [num_processes * num_resources]
    };
    
    struct DetectionOutput {
        bool has_deadlock;
        int num_deadlocked;
        int deadlocked[MAX_PROCESSES];
        int num_safe;
        int safe_sequence[MAX_PROCESSES];
    };
    
    void detect(const DetectionInput& in, DetectionOutput& out) {
        // Local work vector
        alignas(64) int work[MAX_RESOURCES];
        std::memcpy(work, in.available, in.num_resources * sizeof(int));
        
        // Bitset for finished processes (very cache-efficient)
        std::bitset<MAX_PROCESSES> finished;
        
        // Pre-compute processes with zero requests (immediately "done")
        std::vector<int> candidates;
        candidates.reserve(in.num_processes);
        
        out.num_safe = 0;
        
        for (int i = 0; i < in.num_processes; i++) {
            bool zero_request = true;
            const int* req = &in.request[i * in.num_resources];
            
            for (int j = 0; j < in.num_resources; j++) {
                if (req[j] > 0) {
                    zero_request = false;
                    break;
                }
            }
            
            if (zero_request) {
                // Not waiting - add to safe sequence immediately
                finished.set(i);
                out.safe_sequence[out.num_safe++] = i;
                
                // Reclaim its resources
                const int* alloc = &in.allocation[i * in.num_resources];
                for (int j = 0; j < in.num_resources; j++) {
                    work[j] += alloc[j];
                }
            } else {
                // Potential candidate
                candidates.push_back(i);
            }
        }
        
        // Main detection loop
        bool progress = true;
        while (progress) {
            progress = false;
            
            for (auto it = candidates.begin(); it != candidates.end(); ) {
                int i = *it;
                
                // Compare Request[i] <= Work
                const int* req = &in.request[i * in.num_resources];
                bool can_satisfy = compare_le_vector(req, work, in.num_resources);
                
                if (can_satisfy) {
                    // Process can complete
                    finished.set(i);
                    out.safe_sequence[out.num_safe++] = i;
                    
                    // Reclaim resources
                    const int* alloc = &in.allocation[i * in.num_resources];
                    for (int j = 0; j < in.num_resources; j++) {
                        work[j] += alloc[j];
                    }
                    
                    // Remove from candidates
                    it = candidates.erase(it);
                    progress = true;
                } else {
                    ++it;
                }
            }
        }
        
        // Remaining candidates are deadlocked
        out.has_deadlock = !candidates.empty();
        out.num_deadlocked = 0;
        for (int i : candidates) {
            out.deadlocked[out.num_deadlocked++] = i;
        }
    }
    
private:
    // SIMD-optimized vector comparison: returns true if a[i] <= b[i] for all i
    inline bool compare_le_vector(const int* a, const int* b, int len) {
        // Simple version (compiler may auto-vectorize)
        for (int i = 0; i < len; i++) {
            if (a[i] > b[i]) return false;
        }
        return true;
        
        // For explicit SIMD with AVX2:
        // Process 8 ints at a time using _mm256_cmpgt_epi32
    }
};
 
/**
 * Further optimization: incremental detection.
 * 
 * Instead of re-running full detection, maintain a "potentially satisfiable"
 * set that updates as resources are allocated/released.
 */
class IncrementalDetector {
public:
    // When resources are released, check if any blocked process can now proceed
    void on_resource_release(int resource_type, int count) {
        // Only processes waiting for this resource type need rechecking
        for (int pid : processes_waiting_for[resource_type]) {
            if (can_satisfy(pid)) {
                mark_ready(pid);
            }
        }
    }
    
    // When a process blocks, check for immediate deadlock
    bool on_process_block(int pid, int resource_type) {
        add_to_waiting(pid, resource_type);
        
        // Quick check: is there an obvious cycle?
        // (Only for single-instance or as heuristic)
        // Full detection may be triggered if quick check fails
        return quick_deadlock_check(pid);
    }
    
private:
    std::vector<std::set<int>> processes_waiting_for; // [resource_type] -> pids
    
    bool can_satisfy(int pid) { /* ... */ return true; }
    void mark_ready(int pid) { /* ... */ }
    void add_to_waiting(int pid, int resource_type) { /* ... */ }
    bool quick_deadlock_check(int pid) { return false; }
};

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def mixed_resource_detection(
    single_instance_resources: list,   # [(resource_id, holder_pid or None), ...]
    multi_instance_resources: list,    # [(resource_id, total_count), ...]
    allocations: dict,                 # pid -> {rid: count, ...}
    requests: dict,                    # pid -> [(rid, count), ...] (blocked on)
) -> dict:
    """
    Handle detection with mixed single and multiple instance resources.
    
    Strategy: Unify representation, then use matrix-based detection.
    """
    # Build unified resource representation
    # For single-instance: treat as multi with count=1
    all_resources = {}
    
    for rid, holder in single_instance_resources:
        all_resources[rid] = {
            'total': 1,
            'type': 'single'
        }
    
    for rid, count in multi_instance_resources:
        all_resources[rid] = {
            'total': count,
            'type': 'multi'
        }
    
    # Build matrices
    processes = list(set(allocations.keys()) | set(requests.keys()))
    process_idx = {pid: i for i, pid in enumerate(processes)}
    resource_idx = {rid: j for j, rid in enumerate(all_resources.keys())}
    
    n = len(processes)
    m = len(all_resources)
    
    import numpy as np
    
    allocation_matrix = np.zeros((n, m), dtype=np.int32)
    request_matrix = np.zeros((n, m), dtype=np.int32)
    total_vector = np.array([all_resources[rid]['total'] 
                              for rid in resource_idx.keys()], dtype=np.int32)
    
    # Fill allocation matrix
    for pid, resources in allocations.items():
        if pid not in process_idx:
            continue
        i = process_idx[pid]
        for rid, count in resources.items():
            if rid in resource_idx:
                j = resource_idx[rid]
                allocation_matrix[i, j] = count
    
    # Fill request matrix
    for pid, resource_requests in requests.items():
        if pid not in process_idx:
            continue
        i = process_idx[pid]
        for rid, count in resource_requests:
            if rid in resource_idx:
                j = resource_idx[rid]
                request_matrix[i, j] = count
    
    # Calculate available
    available_vector = total_vector - np.sum(allocation_matrix, axis=0)
    
    # Run standard detection algorithm
    result = detect_deadlock_multiple(
        n, m, available_vector, allocation_matrix, request_matrix
    )
    
    # Map back to process IDs
    deadlocked_pids = [processes[i] for i in result.deadlocked_processes]
    
    return {
        'has_deadlock': result.has_deadlock,
        'deadlocked_pids': deadlocked_pids,
        'safe_sequence_pids': [processes[i] for i in result.safe_sequence],
    }
 
 
# For even better performance, we can use a hybrid approach:
def hybrid_detection(state):
    """
    Hybrid approach:
    1. Quick check using wait-for graph (O(V+E))
    2. If no cycle, no deadlock (quick exit)
    3. If cycle found with only single-instance resources, deadlock confirmed
    4. If cycle found with multi-instance, run full matrix detection
    
    This optimizes for the common case where there's no cycle at all.
    """
    cycle = find_wait_for_cycle(state)
    
    if not cycle:
        return {'has_deadlock': False}
    
    # Check if cycle involves only single-instance resources
    if all_single_instance_in_cycle(cycle, state):
        return {
            'has_deadlock': True,
            'deadlocked_pids': list(cycle)
        }
    
    # Multi-instance involved: need full analysis
    return run_matrix_detection(state)

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// Example: Database connection pool deadlock
// Pool size: 10 connections
 
public class ConnectionPoolDeadlock {
    private ConnectionPool pool = new ConnectionPool(10);
    
    // Transaction A needs 6 connections total
    public void transactionA() {
        List<Connection> conns = pool.acquire(4);  // Gets 4
        // ... do some work ...
        
        // Needs 2 more to complete, but...
        List<Connection> more = pool.acquire(2);  // BLOCKS
        // If pool is exhausted, we're stuck
        
        // ... release all ...
    }
    
    // Transaction B needs 6 connections total  
    public void transactionB() {
        List<Connection> conns = pool.acquire(4);  // Gets 4
        // ... do some work ...
        
        // Needs 2 more to complete
        List<Connection> more = pool.acquire(2);  // BLOCKS
        // ... release all ...
    }
    
    // If both run concurrently:
    // - A holds 4, needs 2 more (only 2 left)
    // - B holds 4, needs 2 more (only 2 left)
    // A waits for B's resources, B waits for A's -> DEADLOCK (if pool size 8, not 10)
    
    // Detection result:
    // Available: [2] (connections)
    // Allocation: A=[4], B=[4]
    // Request: A=[2], B=[2]
    // 
    // Try A: Request[A]=2, Work=2 -> A can complete!
    //        Work = 2 + 4 = 6
    // Try B: Request[B]=2, Work=6 -> B can complete!
    // 
    // No deadlock with pool size 10.
    // With pool size 8: Available=[0], instant deadlock!
}