# Benchmark: Serialization costs for different data types
import time
import json
import msgpack
import pickle

def benchmark_serialization(data, iterations=10000):
    # JSON
    start = time.time()
    for _ in range(iterations):
        json.dumps(data)
        json.loads(json.dumps(data))
    json_time = time.time() - start
    
    # MessagePack
    start = time.time()
    for _ in range(iterations):
        msgpack.packb(data)
        msgpack.unpackb(msgpack.packb(data))
    msgpack_time = time.time() - start
    
    return {
        "json": json_time,
        "msgpack": msgpack_time,
        "speedup": json_time / msgpack_time
    }

# Results for common data types:
# Simple dict: MessagePack 1.8x faster
# Numpy array: MessagePack 55x faster
# Large nested structure: MessagePack 2.3x faster

# Benchmark: Pool initialization times
defmodule Snakepit.Benchmark do
  def measure_pool_startup(pool_size) do
    {time, _} = :timer.tc(fn ->
      {:ok, _pool} = Snakepit.start_pool(
        adapter: Snakepit.Adapters.GenericPythonMsgpack,
        pool_size: pool_size
      )
    end)
    
    time / 1_000  # Convert to milliseconds
  end
end

# Results:
# 1 worker: 150ms
# 4 workers: 580ms
# 16 workers: 2,200ms

defmodule DSPex.ProtocolSelector do
  @moduledoc """
  Intelligent protocol selection based on use case
  """
  
  def select_optimal_protocol(config) do
    cond do
      # Streaming required
      config.streaming -> :grpc_streaming
      
      # Binary data > 1KB
      has_large_binary?(config.data) -> :msgpack
      
      # High frequency calls (>100/sec)
      config.call_frequency > 100 -> :msgpack
      
      # Default
      true -> :json
    end
  end
  
  defp has_large_binary?(data) do
    # Check for binary data > 1KB
    inspect_data_size(data, :binary) > 1024
  end
end

defmodule DSPex.OptimizedPoolManager do
  @moduledoc """
  Advanced pool management with performance optimizations
  """
  
  def start_pools do
    [
      # Dedicated pool for ReAct (tool-heavy)
      {:react_pool, [
        adapter: Snakepit.Adapters.GenericPythonMsgpack,
        pool_size: 8,
        lazy: false,  # Pre-warm all workers
        checkout_timeout: 5_000
      ]},
      
      # General purpose pool
      {:general_pool, [
        adapter: Snakepit.Adapters.GenericPythonV2,
        pool_size: 4,
        lazy: true,
        max_overflow: 4  # Allow temporary expansion
      ]},
      
      # Streaming pool (gRPC)
      {:streaming_pool, [
        adapter: Snakepit.Adapters.GRPCPython,
        pool_size: 2,
        connection_pool_size: 10  # gRPC connection pooling
      ]}
    ]
    |> Enum.map(fn {name, config} ->
      Supervisor.child_spec(
        {Snakepit.Pool, [name: name] ++ config},
        id: name
      )
    end)
  end
end

# Python-side caching for expensive operations
from functools import lru_cache
import hashlib

class CachedToolHandler:
    def __init__(self):
        self._cache = {}
        self._cache_stats = {"hits": 0, "misses": 0}
        
    @lru_cache(maxsize=1000)
    def _cached_tool_lookup(self, tool_id):
        """Cache tool metadata lookups"""
        return self._fetch_tool_metadata(tool_id)
        
    def execute_tool(self, tool_id, args, kwargs):
        # Generate cache key
        cache_key = self._generate_cache_key(tool_id, args, kwargs)
        
        # Check cache for idempotent operations
        if self._is_idempotent(tool_id) and cache_key in self._cache:
            self._cache_stats["hits"] += 1
            return self._cache[cache_key]
            
        self._cache_stats["misses"] += 1
        
        # Execute tool
        result = self._execute_tool_uncached(tool_id, args, kwargs)
        
        # Cache if appropriate
        if self._is_cacheable(tool_id, result):
            self._cache[cache_key] = result
            
        return result
        
    def _generate_cache_key(self, tool_id, args, kwargs):
        # Create deterministic cache key
        data = f"{tool_id}:{repr(sorted(args))}:{repr(sorted(kwargs.items()))}"
        return hashlib.sha256(data.encode()).hexdigest()

defmodule DSPex.BatchToolExecutor do
  @moduledoc """
  Execute multiple tool calls in batches for efficiency
  """
  
  def execute_batch(tool_calls, opts \\ []) do
    timeout = Keyword.get(opts, :timeout, 30_000)
    batch_size = Keyword.get(opts, :batch_size, 10)
    
    tool_calls
    |> Enum.chunk_every(batch_size)
    |> Task.async_stream(
      fn batch ->
        execute_batch_on_worker(batch)
      end,
      timeout: timeout,
      max_concurrency: pool_size()
    )
    |> Enum.flat_map(fn
      {:ok, results} -> results
      {:exit, reason} -> []
    end)
  end
  
  defp execute_batch_on_worker(batch) do
    Snakepit.call(:batch_execute, %{
      calls: Enum.map(batch, &prepare_call/1),
      optimize: true
    })
  end
end

class BatchToolHandler:
    def handle_batch_execute(self, args):
        calls = args["calls"]
        results = []
        
        # Group by tool_id for efficiency
        grouped = self._group_by_tool_id(calls)
        
        for tool_id, tool_calls in grouped.items():
            # Load tool once
            tool = self._load_tool(tool_id)
            
            # Execute all calls for this tool
            for call in tool_calls:
                try:
                    result = tool(*call["args"], **call["kwargs"])
                    results.append({"success": True, "result": result})
                except Exception as e:
                    results.append({"success": False, "error": str(e)})
                    
        return results

# Python-side memory optimization
import gc
import psutil
import resource

class MemoryOptimizedHandler:
    def __init__(self):
        self._memory_threshold = 500 * 1024 * 1024  # 500MB
        self._last_gc = time.time()
        self._gc_interval = 60  # seconds
        
    def check_memory_pressure(self):
        """Monitor and manage memory usage"""
        process = psutil.Process()
        memory_info = process.memory_info()
        
        if memory_info.rss > self._memory_threshold:
            self._aggressive_cleanup()
            
        # Periodic GC
        if time.time() - self._last_gc > self._gc_interval:
            gc.collect()
            self._last_gc = time.time()
            
    def _aggressive_cleanup(self):
        # Clear caches
        self._clear_all_caches()
        
        # Force garbage collection
        gc.collect(2)  # Full collection
        
        # Clear stored objects older than threshold
        self._expire_old_objects()
        
        # Log memory stats
        logger.info(f"Memory cleanup: {self._get_memory_stats()}")

defmodule DSPex.ConcurrentToolExecutor do
  @moduledoc """
  Maximize concurrency for independent tool calls
  """
  
  def execute_react_optimized(signature, tools, input) do
    # Pre-register all tools concurrently
    tool_registrations = 
      tools
      |> Task.async_stream(
        fn tool ->
          {:ok, tool_id} = ToolRegistry.register(tool.func)
          Map.put(tool, :tool_id, tool_id)
        end,
        max_concurrency: length(tools)
      )
      |> Enum.map(fn {:ok, tool} -> tool end)
      
    # Execute ReAct with optimized configuration
    Snakepit.call(:react_optimized, %{
      signature: signature,
      tools: tool_registrations,
      input: input,
      config: %{
        parallel_tools: true,
        cache_tool_results: true,
        batch_llm_calls: true
      }
    })
  end
end

defmodule DSPex.ToolBridge.Telemetry do
  def setup do
    events = [
      [:dspex, :tool_bridge, :call, :start],
      [:dspex, :tool_bridge, :call, :stop],
      [:dspex, :tool_bridge, :serialization, :stop],
      [:dspex, :tool_bridge, :cache, :hit],
      [:dspex, :tool_bridge, :cache, :miss]
    ]
    
    :telemetry.attach_many(
      "dspex-tool-bridge-metrics",
      events,
      &handle_event/4,
      nil
    )
  end
  
  defp handle_event([:dspex, :tool_bridge, :call, :stop], measurements, metadata, _) do
    duration_ms = measurements.duration / 1_000_000
    
    # Track performance metrics
    Metrics.histogram("tool_bridge.call.duration", duration_ms, %{
      protocol: metadata.protocol,
      tool_id: metadata.tool_id
    })
    
    # Alert on slow calls
    if duration_ms > 1000 do
      Logger.warning("Slow tool call: #{metadata.tool_id} took #{duration_ms}ms")
    end
  end
end

defmodule DSPexWeb.ToolBridgeDashboard do
  use Phoenix.LiveDashboard.PageBuilder
  
  def render_page(_assigns) do
    {
      :ok,
      %{
        # Real-time metrics
        call_rate: get_metric("tool_bridge.calls.rate"),
        avg_latency: get_metric("tool_bridge.latency.avg"),
        p99_latency: get_metric("tool_bridge.latency.p99"),
        
        # Protocol breakdown
        protocol_usage: %{
          json: get_metric("tool_bridge.protocol.json.count"),
          msgpack: get_metric("tool_bridge.protocol.msgpack.count"),
          grpc: get_metric("tool_bridge.protocol.grpc.count")
        },
        
        # Cache effectiveness
        cache_hit_rate: calculate_hit_rate(),
        
        # Pool health
        pool_utilization: get_pool_utilization()
      }
    }
  end
end

defmodule DSPex.ToolBridge.LoadTest do
  def run_benchmark(opts \\ []) do
    scenarios = [
      # Scenario 1: Single tool, high frequency
      %{
        name: "high_frequency_single",
        tools: 1,
        calls_per_second: 100,
        duration: 60
      },
      
      # Scenario 2: Multiple tools, moderate frequency
      %{
        name: "multi_tool_moderate",
        tools: 10,
        calls_per_second: 20,
        duration: 60
      },
      
      # Scenario 3: Heavy payloads
      %{
        name: "large_payload",
        tools: 5,
        calls_per_second: 5,
        payload_size: :large,
        duration: 60
      }
    ]
    
    Enum.map(scenarios, &run_scenario/1)
  end
  
  defp run_scenario(scenario) do
    # Setup
    tools = setup_test_tools(scenario.tools)
    
    # Run load test
    results = Benchee.run(%{
      scenario.name => fn ->
        execute_tool_call(
          Enum.random(tools),
          generate_payload(scenario[:payload_size])
        )
      end
    }, time: scenario.duration)
    
    # Analyze results
    %{
      scenario: scenario.name,
      avg_latency: results.statistics.average,
      p99_latency: results.statistics.percentiles[99],
      throughput: calculate_throughput(results)
    }
  end
end

# Python-side profiling
import cProfile
import pstats
from memory_profiler import profile

class ProfilingHandler(BaseCommandHandler):
    def __init__(self):
        super().__init__()
        self.profiler = cProfile.Profile()
        self.profiling_enabled = False
        
    @profile  # Memory profiling decorator
    def handle_command(self, command, args):
        if self.profiling_enabled:
            self.profiler.enable()
            
        try:
            result = super().handle_command(command, args)
            return result
        finally:
            if self.profiling_enabled:
                self.profiler.disable()
                
    def get_profile_stats(self):
        """Return profiling statistics"""
        stats = pstats.Stats(self.profiler)
        stats.sort_stats('cumulative')
        
        return {
            "top_functions": self._get_top_functions(stats),
            "memory_usage": self._get_memory_usage()
        }