"""Stub file for tmp.aggregator directory."""
from typing import Any, Dict, List, Optional, Set, Tuple

from collections import defaultdict, deque
from confluent_kafka import Producer
from datetime import datetime, timezone
from matrice_common.session import Session
from matrice_common.stream.kafka_stream import MatriceKafkaDeployment
from matrice_inference.tmp.aggregator.aggregator import ResultsAggregator
from matrice_inference.tmp.aggregator.analytics import AnalyticsSummarizer
from matrice_inference.tmp.aggregator.ingestor import ResultsIngestor
from matrice_inference.tmp.aggregator.latency import LatencyTracker
from matrice_inference.tmp.aggregator.publisher import ResultsPublisher
from matrice_inference.tmp.aggregator.synchronizer import ResultsSynchronizer
from queue import Empty, PriorityQueue, Full
from queue import Queue
from queue import Queue, Empty
from queue import Queue, Empty, PriorityQueue
from statistics import mean, median, stdev
import base64
import copy
import heapq
import itertools
import json
import logging
import threading
import time

# Classes
# From aggregator
class ResultsAggregator:
    """
    Optimized aggregation and combination of synchronized results from multiple deployments.
    This component takes synchronized results and combines them into meaningful aggregated outputs
    while maintaining consistent structure with individual deployment results.
    """

    def __init__(self, synchronized_results_queue, aggregate_by_location: bool = False) -> None: ...
        """
        Initialize the results aggregator.
        
        Args:
            synchronized_results_queue: Queue containing synchronized results from synchronizer
            aggregate_by_location: Whether to aggregate by location
        """

    def cleanup(self) -> None: ...
        """
        Clean up resources.
        """

    def get_health_status(self) -> Dict[str, Any]: ...
        """
        Get health status of the aggregator.
        """

    def get_stats(self) -> Dict[str, Any]: ...
        """
        Get current aggregation statistics.
        """

    def start_aggregation(self) -> bool: ...
        """
        Start the results aggregation process.
        
        Returns:
            bool: True if aggregation started successfully, False otherwise
        """

    def stop_aggregation(self) -> Any: ...
        """
        Stop the results aggregation process.
        """


# From analytics
class AnalyticsSummarizer:
    """
    Buffers aggregated camera_results and emits 5-minute rollups per camera
    focusing on tracking_stats per application.
    
    Output structure example per camera:
        {
          "camera_name": "camera_1",
          "inferencePipelineId": "pipeline-xyz",
          "camera_group": "group_a",
          "location": "Lobby",
          "agg_apps": [
            {
              "application_name": "People Counting",
              "application_key_name": "People_Counting",
              "application_version": "1.3",
              "tracking_stats": {
                "input_timestamp": "00:00:09.9",          # last seen
                "reset_timestamp": "00:00:00",             # earliest seen in window
                "current_counts": [{"category": "person", "count": 2}],         # NEW people who appeared for first time
                "total_current_counts": [{"category": "person", "count": 7}],   # ALL people in frame (existing + new)
                "total_counts": [{"category": "person", "count": 37}]           # cumulative unique since reset
              }
            }
          ],
          "summary_metadata": {
            "window_seconds": 300,
            "messages_aggregated": 123,
            "start_time": 1710000000.0,
            "end_time": 1710000300.0
          }
        }
    
    Counts explanation (OUTPUT keys):
        - current_counts: NEW track IDs appearing for first time in this aggregation window (e.g., 2)
        - total_current_counts: Unique present in window = baseline + new arrivals (e.g., 5 at start + 2 new = 7)
        - total_counts: Cumulative unique people since tracking reset (e.g., 37)
    """

    def __init__(self, session: Session, inference_pipeline_id: str, flush_interval_seconds: int = 300) -> None: ...

    def cleanup(self) -> None: ...

    def get_health_status(self) -> Dict[str, Any]: ...

    def get_stats(self) -> Dict[str, Any]: ...

    def ingest_result(self, aggregated_result: Dict[str, Any]) -> None: ...
        """
        Receive a single aggregated camera_results payload for buffering.
        This is intended to be called by the publisher after successful publish.
        """

    def start(self) -> bool: ...

    def stop(self) -> None: ...


# From ingestor
class ResultsIngestor:
    """
    Streams and manages results from multiple deployments.
    Handles result collection, queuing, and distribution with enhanced structure consistency.
    """

    def __init__(self, deployment_ids: List[str], session: Session, consumer_timeout: float = 300, action_id: str = '') -> None: ...
        """
        Initialize the results streamer.
        
        Args:
            deployment_ids: List of deployment IDs
            session: Session object for authentication
            consumer_timeout: Timeout for consuming results from deployments
        """

    def cleanup(self) -> None: ...
        """
        Clean up all resources.
        """

    def get_all_results(self, timeout: float = 1.0) -> List[Dict]: ...
        """
        Get results from all deployment queues.
        
        Args:
            timeout: Timeout for getting results
        
        Returns:
            List[Dict]: List of result dictionaries
        """

    def get_health_status(self) -> Dict: ...
        """
        Get health status of the results streamer.
        """

    def get_results(self, deployment_id: str, timeout: float = 1.0) -> Optional[Dict]: ...
        """
        Get a result from a specific deployment's priority queue.
        
        Args:
            deployment_id: ID of the deployment
            timeout: Timeout for getting the result
        
        Returns:
            Dict: Result dictionary or None if timeout/no result
        """

    def get_stats(self) -> Dict: ...
        """
        Get current statistics.
        """

    def start_streaming(self) -> bool: ...
        """
        Start streaming results from all deployments.
        
        Returns:
            bool: True if streaming started successfully, False otherwise
        """

    def stop_streaming(self) -> None: ...
        """
        Stop all streaming operations.
        """


# From latency
class LatencyTracker:
    """
    Tracks and analyzes latency metrics from multiple deployments in real-time.
    
    Provides detailed timing analysis including:
    - Model inference times
    - Post-processing times
    - End-to-end latencies
    - Client-side timings
    - Server-side breakdown
    - Cross-deployment comparisons
    """

    def __init__(self, session: Session, inference_pipeline_id: str, flush_interval_seconds: int = 60, max_samples: int = 1000) -> None: ...
        """
        Initialize latency tracker.
        
                Args:
                    session: Session object for authentication
                    inference_pipeline_id: ID of the inference pipeline
                    flush_interval_seconds: Interval for publishing latency reports
                    max_samples: Maximum number of samples to keep per metric
        """

    def cleanup(self) -> None: ...
        """
        Clean up resources.
        """

    def get_health_status(self) -> Dict[str, Any]: ...
        """
        Get health status of the latency tracker.
        """

    def get_stats(self) -> Dict[str, Any]: ...
        """
        Get current tracker statistics.
        """

    def ingest_result(self, deployment_id: str, aggregated_result: Dict[str, Any]) -> None: ...
        """
        Ingest a result for latency analysis.
        
                Args:
                    deployment_id: ID of the deployment that produced this result
                    aggregated_result: Result payload containing latency data
        """

    def start(self) -> bool: ...
        """
        Start the latency tracker.
        """

    def stop(self) -> None: ...
        """
        Stop the latency tracker.
        """


# From pipeline
class ResultsAggregationPipeline:
    """
    Enhanced deployments aggregator that handles multiple streams, synchronizes results,
    and outputs aggregated results to Kafka topics with consistent structure.
    
    This class orchestrates the complete pipeline for collecting, synchronizing, and
    publishing results from multiple ML model deployments in an inference pipeline,
    ensuring all results follow the same structure as individual deployment results.
    
    Usage Example:
        ```python
        from matrice import Session
        from matrice_inference.tmp.aggregator import ResultsAggregationPipeline
    
        # Initialize session
        session = Session(account_number="...", access_key="...", secret_key="...")
    
        # Create aggregator for an inference pipeline
        aggregator = ResultsAggregationPipeline(session, "your-inference-pipeline-id")
    
        # Setup the aggregation pipeline
        if aggregator.setup_components():
            print(f"Setup complete for {len(aggregator.deployment_ids)} deployments")
    
            # Start streaming and run until keyboard interrupt
            try:
                aggregator.start_streaming()
            except KeyboardInterrupt:
                print("Pipeline stopped by user")
            finally:
                aggregator.cleanup()
        ```
    """

    def __init__(self, session: Session, action_record_id: str) -> None: ...
        """
        Initialize the deployments aggregator.
        
        Args:
            session: Session object for authentication
            action_record_id: Action Record ID
        """

    def cleanup(self) -> None: ...
        """
        Clean up all resources.
        """

    def force_sync_pending_results(self) -> int: ...
        """
        Force synchronization of all pending results.
        
        Returns:
            int: Number of pending results that were synchronized
        """

    def get_deployment_info(self) -> Dict: ...
        """
        Get information about the deployments in this aggregator.
        
        Returns:
            Dict: Deployment information including IDs, count, and status
        """

    def get_health_status(self) -> Dict: ...
        """
        Get health status of all components.
        """

    def get_stats(self) -> Dict: ...
        """
        Get current statistics from all components.
        """

    def setup_components(self) -> bool: ...
        """
        Setup all components and initialize the aggregation pipeline.
        
        Returns:
            bool: True if all components initialized successfully, False otherwise
        """

    def start_logging(self, status_interval: int = 30) -> None: ...
        """
        Start the pipeline logging and run until interrupted.
        Args:
            status_interval: Interval in seconds between status log messages
        """

    def start_streaming(self, block: bool = True) -> bool: ...
        """
        Start the complete streaming pipeline: ingestion, synchronization, aggregation, and publishing.
        
        Returns:
            bool: True if streaming started successfully, False otherwise
        """

    def stop_streaming(self) -> None: ...
        """
        Stop all streaming operations in reverse order.
        """

    def update_status(self, step_code: str, status: str, status_description: str) -> None: ...
        """
        Update status of data preparation.
        
                Args:
                    step_code: Code indicating current step
                    status: Status of step
                    status_description: Description of status
        """

    def wait_for_ready(self, timeout: int = 300, poll_interval: int = 10) -> bool: ...
        """
        Wait for the aggregator to be ready and processing results.
        
        Args:
            timeout: Maximum time to wait in seconds
            poll_interval: Time between checks in seconds
        
        Returns:
            bool: True if aggregator is ready, False if timeout
        """


# From publisher
class ResultsPublisher:
    """
    Optimized streaming of final aggregated results from inference pipeline to Kafka.
    Processes results immediately for low latency.
    """

    def __init__(self, inference_pipeline_id: str, session: Session, final_results_queue, analytics_summarizer: Optional[AnalyticsSummarizer] = None, latency_tracker: Optional[LatencyTracker] = None) -> None: ...
        """
        Initialize the final results streamer.
        
        Args:
            inference_pipeline_id: ID of the inference pipeline
            session: Session object for authentication
            final_results_queue: Queue containing final aggregated results
            analytics_summarizer: Optional analytics summarizer for forwarding results
            latency_tracker: Optional latency tracker for performance monitoring
        """

    def get_health_status(self) -> Dict[str, Any]: ...
        """
        Get health status of the publisher.
        """

    def get_stats(self) -> Dict[str, Any]: ...
        """
        Get streaming statistics.
        
        Returns:
            Dict containing statistics
        """

    def is_running(self) -> bool: ...
        """
        Check if the streamer is currently running.
        """

    def start_streaming(self) -> bool: ...
        """
        Start streaming final results to Kafka.
        
        Returns:
            bool: True if streaming started successfully, False otherwise
        """

    def stop_streaming(self) -> None: ...
        """
        Stop streaming final results.
        """


# From synchronizer
class ResultsSynchronizer:
    """
    Optimized synchronization of results from multiple deployments by stream_key and input_order.
    Ensures consistent structure and proper error handling for the aggregation pipeline.
    """

    def __init__(self, results_queues: Dict[str, PriorityQueue], sync_timeout: float = 300) -> None: ...
        """
        Initialize the results synchronizer.
        
        Args:
            results_queues: Dictionary of priority queues containing results from deployments
            sync_timeout: Maximum time to wait for input_order synchronization (in seconds)
        """

    def cleanup(self) -> None: ...
        """
        Clean up resources.
        """

    def force_sync_pending(self) -> int: ...
        """
        Force synchronization of all pending results regardless of completeness.
        """

    def get_health_status(self) -> Dict: ...
        """
        Get health status of the synchronizer.
        """

    def get_stats(self) -> Dict: ...
        """
        Get current synchronization statistics.
        """

    def start_synchronization(self) -> bool: ...
        """
        Start the results synchronization process.
        
        Returns:
            bool: True if synchronization started successfully, False otherwise
        """

    def stop_synchronization(self) -> Any: ...
        """
        Stop the results synchronization process.
        """


from . import aggregator, analytics, ingestor, latency, pipeline, publisher, synchronizer