# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Example command:
```shell
python src/lerobot/scripts/server/robot_client.py \
    --robot.type=so100_follower \
    --robot.port=/dev/tty.usbmodem58760431541 \
    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 1920, height: 1080, fps: 30}}" \
    --robot.id=black \
    --task="dummy" \
    --server_address=127.0.0.1:8080 \
    --policy_type=act \
    --pretrained_name_or_path=user/model \
    --policy_device=mps \
    --actions_per_chunk=50 \
    --chunk_size_threshold=0.5 \
    --aggregate_fn_name=weighted_average \
    --debug_visualize_queue_size=True
```
"""

import logging
import pickle  # nosec
import threading
import time
from collections.abc import Callable
from dataclasses import asdict
from pprint import pformat
from queue import Queue
from typing import Any

import draccus
import grpc
import torch

from lerobot.common.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
from lerobot.common.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
from lerobot.common.cameras.dummy.configuration_dummy import DummyCameraConfig  # noqa: F401
from lerobot.configs.policies import PreTrainedConfig
# from lerobot.robots import (  # noqa: F401
#     Robot,
#     RobotConfig,
#     koch_follower,
#     make_robot_from_config,
#     so100_follower,
#     so101_follower,
# )
from sendrobot.robot import Robot
from sendrobot.config import RobotConfig
from sendrobot.utils import make_robot_from_config
from sendrobot.send_robot import SendRobotConfig, SendRobotEndEffectorConfig  # noqa: F401

from lerobot.scripts.server.configs import RobotClientConfig
from lerobot.scripts.server.constants import SUPPORTED_ROBOTS
from lerobot.scripts.server.helpers import (
    Action,
    FPSTracker,
    Observation,
    RawObservation,
    RemotePolicyConfig,
    TimedAction,
    TimedObservation,
    get_logger,
    map_robot_keys_to_lerobot_features,
    validate_robot_cameras_for_policy,
    visualize_action_queue_size,
)
from lerobot.common.transport import (
    services_pb2,  # type: ignore
    services_pb2_grpc,  # type: ignore
)
from lerobot.common.utils.utils import (
    get_safe_torch_device,
    init_logging,
    log_say,
)
from lerobot.common.transport.utils import grpc_channel_options, send_bytes_in_chunks

import rclpy
from rclpy.node import Node


class RobotClient:
    prefix = "robot_client"
    logger = get_logger(prefix)

    def __init__(self, config: RobotClientConfig):
        """Initialize RobotClient with unified configuration.

        Args:
            config: RobotClientConfig containing all configuration parameters
        """

        # ROS2 initialization
        rclpy.init()

        # Store configuration
        self.config = config
        #这一步就是把 send_robot 实例化成 RobotClient 的成员self.robot
        #所以后面所有对机器人的操作，都是对这个对象的方法调用。
        self.robot = make_robot_from_config(config.robot)

        self.executor = None
        self.spin_thread = None
        if isinstance(self.robot, Node):
            executor = rclpy.executors.MultiThreadedExecutor()
            executor.add_node(self.robot)
            spin_thread = threading.Thread(target=executor.spin, daemon=True)
            spin_thread.start()

        #若是ROS2节点，创建executor 并启动spin

        self.robot.connect()


        lerobot_features = map_robot_keys_to_lerobot_features(self.robot)

        if config.verify_robot_cameras:
            # Load policy config for validation
            policy_config = PreTrainedConfig.from_pretrained(config.pretrained_name_or_path)
            policy_image_features = policy_config.image_features

            # The cameras specified for inference must match the one supported by the policy chosen
            validate_robot_cameras_for_policy(lerobot_features, policy_image_features)

        # Use environment variable if server_address is not provided in config
        self.server_address = config.server_address

        self.policy_config = RemotePolicyConfig(
            config.policy_type,
            config.pretrained_name_or_path,
            lerobot_features,
            config.actions_per_chunk,
            config.policy_device,
            rtc_enabled=config.rtc_enabled,
            rtc_num_steps=config.rtc_num_steps,
            rtc_execution_horizon=config.rtc_execution_horizon,
            rtc_max_guidance_weight=config.rtc_max_guidance_weight,
        )
        self.channel = grpc.insecure_channel(
            self.server_address, grpc_channel_options(initial_backoff=f"{config.environment_dt:.4f}s")
        )
        self.stub = services_pb2_grpc.AsyncInferenceStub(self.channel)
        self.logger.info(f"Initializing client to connect to server at {self.server_address}")

        self.shutdown_event = threading.Event()

        # Initialize client side variables
        self.latest_action_lock = threading.Lock()
        self.latest_action = -1
        self.action_chunk_size = -1

        self._chunk_size_threshold = config.chunk_size_threshold

        self.action_queue = Queue()
        self.action_queue_lock = threading.Lock()  # Protect queue operations
        self.action_queue_size = []
        self.start_barrier = threading.Barrier(2)  # 2 threads: action receiver, control loop

        # FPS measurement
        self.fps_tracker = FPSTracker(target_fps=self.config.fps)

        # Linear interpolation between policy actions (default multiplier=1 = off)
        from lerobot.common.policies.rtc.action_interpolator import ActionInterpolator

        self.action_interpolator = ActionInterpolator(
            multiplier=getattr(self.config, "interpolation_multiplier", 1)
        )
        self._interp_underruns = 0
        if self.action_interpolator.enabled:
            self.logger.info(
                "ActionInterpolator enabled multiplier=%d → control_fps=%.1f",
                self.action_interpolator.multiplier,
                self.config.fps * self.action_interpolator.multiplier,
            )

        self.logger.info("Robot connected and ready")

        # Use an event for thread-safe coordination
        self.must_go = threading.Event()
        self.must_go.set()  # Initially set - observations qualify for direct processing

        self.rtc_enabled = bool(config.rtc_enabled)
        self._rtc_inflight = threading.Event()
        self._rtc_delay_estimate = int(config.rtc_delay_steps)
        if self.rtc_enabled:
            self.logger.info(
                "RTC client on: prior_delay=%d steps, horizon=%d, denoise_steps=%d",
                self._rtc_delay_estimate,
                config.rtc_execution_horizon,
                config.rtc_num_steps,
            )

    @property
    def running(self):
        return not self.shutdown_event.is_set()

    def start(self):
        """Start the robot client and connect to the policy server"""
        try:
            # client-server handshake
            start_time = time.perf_counter()
            self.stub.Ready(services_pb2.Empty())
            end_time = time.perf_counter()
            self.logger.debug(f"Connected to policy server in {end_time - start_time:.4f}s") #调试用，不会显示

            # send policy instructions
            policy_config_bytes = pickle.dumps(self.policy_config)
            policy_setup = services_pb2.PolicySetup(data=policy_config_bytes)

            self.logger.info("Sending policy instructions to policy server")
            self.logger.debug(
                f"Policy type: {self.policy_config.policy_type} | "
                f"Pretrained name or path: {self.policy_config.pretrained_name_or_path} | "
                f"Device: {self.policy_config.device}"
            )#调试用

            self.stub.SendPolicyInstructions(policy_setup)

            self.shutdown_event.clear()

            return True

        except grpc.RpcError as e:
            self.logger.error(f"Failed to connect to policy server: {e}")
            return False

    def stop(self):
        """Stop the robot client"""
        self.shutdown_event.set()

        self.robot.disconnect()
        self.logger.debug("Robot disconnected") #调试用

        # If the robot is a ROS2 node, shutdown the executor and spin thread
        if self.executor is not None:
            self.executor.shutdown()
            self.executor.remove_node(self.robot)
            rclpy.shutdown()

        self.channel.close()
        self.logger.debug("Client stopped, channel closed") #调试用

    def send_observation(
        self,
        obs: TimedObservation,
    ) -> bool:
        """Send observation to the policy server.
        Returns True if the observation was sent successfully, False otherwise."""
        if not self.running:
            raise RuntimeError("Client not running. Run RobotClient.start() before sending observations.")

        if not isinstance(obs, TimedObservation):
            raise ValueError("Input observation needs to be a TimedObservation!")

        start_time = time.perf_counter()
        observation_bytes = pickle.dumps(obs)
        serialize_time = time.perf_counter() - start_time
        self.logger.debug(f"Observation serialization time: {serialize_time:.6f}s")

        try:
            observation_iterator = send_bytes_in_chunks(
                observation_bytes,
                services_pb2.Observation,
                log_prefix="[CLIENT] Observation",
                silent=True,
            )
            _ = self.stub.SendObservations(observation_iterator)
            obs_timestep = obs.get_timestep()
            # self.logger.info(f"Sent observation #{obs_timestep} | ")

            return True

        except grpc.RpcError as e:
            self.logger.error(f"Error sending observation #{obs.get_timestep()}: {e}")
            return False

    def _inspect_action_queue(self):
        with self.action_queue_lock:
            queue_size = self.action_queue.qsize()
            timestamps = sorted([action.get_timestep() for action in self.action_queue.queue])
        self.logger.debug(f"Queue size: {queue_size}, Queue contents: {timestamps}")
        return queue_size, timestamps
    

    # Aggregates incoming actions based on their timesteps.
    # 聚合重叠部分的动作
    # 基于时间步timestep对新传入的动作队列与当前内部的动作队列进行聚合
    def _aggregate_action_queues(
        self,
        incoming_actions: list[TimedAction],
        aggregate_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] | None = None,
    ):
        """Finds the same timestep actions in the queue and aggregates them using the aggregate_fn"""
        if aggregate_fn is None:
            # default aggregate function: take the latest action
            def aggregate_fn(x1, x2):
                return x2

        future_action_queue = Queue()
        with self.action_queue_lock:
            internal_queue = self.action_queue.queue

        current_action_queue = {action.get_timestep(): action.get_action() for action in internal_queue}

        for new_action in incoming_actions:
            with self.latest_action_lock:
                latest_action = self.latest_action

            # New action is older than the latest action in the queue, skip it
            # 时间步小于等于最新动作时间步的动作，跳过（删除）
            if new_action.get_timestep() <= latest_action:
                continue

            # If the new action's timestep is not in the current action queue, add it directly
            # 如果新动作的时间步不在当前动作队列中，直接添加
            elif new_action.get_timestep() not in current_action_queue:
                future_action_queue.put(new_action)
                continue

            # If the new action's timestep is in the current action queue, aggregate it
            # TODO: There is probably a way to do this with broadcasting of the two action tensors
            future_action_queue.put(
                TimedAction(
                    timestamp=new_action.get_timestamp(),
                    timestep=new_action.get_timestep(),
                    action=aggregate_fn(
                        current_action_queue[new_action.get_timestep()], new_action.get_action()
                    ),
                )
            )

        with self.action_queue_lock:
            self.action_queue = future_action_queue

    def _rtc_snapshot(self) -> tuple[int, torch.Tensor | None]:
        """Next timestep plus the unexecuted commands that RTC should match."""
        with self.action_queue_lock:
            with self.latest_action_lock:
                latest = self.latest_action
            pending = list(self.action_queue.queue)
        if not pending:
            return max(latest, -1) + 1, None
        actions = torch.stack(
            [item.get_action().detach().cpu().float().reshape(-1) for item in pending]
        )
        return pending[0].get_timestep(), actions

    def _install_rtc_chunk(self, timed_actions: list[TimedAction]) -> None:
        """Replace the queue with the new chunk's not-yet-executed suffix."""
        with self.action_queue_lock:
            with self.latest_action_lock:
                latest = self.latest_action
            kept = [item for item in timed_actions if item.get_timestep() > latest]
            skipped = len(timed_actions) - len(kept)
            fresh = Queue()
            for item in kept:
                fresh.put(item)
            self.action_queue = fresh
        if skipped > 0:
            self._rtc_delay_estimate = skipped
        self.logger.info(
            "RTC merge skipped=%d kept=%d next_delay=%d latest=%s",
            skipped,
            len(kept),
            self._rtc_delay_estimate,
            latest,
        )
        if not kept:
            self.logger.warning("RTC merge kept no actions; inference outran the chunk")

    def receive_actions(self, verbose: bool = False):
        """Receive actions from the policy server"""
        # Wait at barrier for synchronized start
        self.start_barrier.wait()
        # self.logger.info("Action receiving thread starting")

        while self.running:
            try:
                # Use StreamActions to get a stream of actions from the server
                actions_chunk = self.stub.GetActions(services_pb2.Empty())
                if len(actions_chunk.data) == 0:
                    # debug: 客户端一直接受空的动作块
                    self.logger.info("Received empty actions chunk, waiting for next call")
                    if self.rtc_enabled:
                        self._rtc_inflight.clear()
                    continue  # received `Empty` from server, wait for next call

                receive_time = time.time()

                # Deserialize bytes back into list[TimedAction]
                deserialize_start = time.perf_counter()
                timed_actions = pickle.loads(actions_chunk.data)  # nosec
                deserialize_time = time.perf_counter() - deserialize_start

                self.action_chunk_size = max(self.action_chunk_size, len(timed_actions))

                # Calculate network latency if we have matching observations
                if len(timed_actions) > 0 and verbose:
                    with self.latest_action_lock:
                        latest_action = self.latest_action

                    self.logger.debug(f"Current latest action: {latest_action}")

                    # Get queue state before changes
                    old_size, old_timesteps = self._inspect_action_queue()
                    if not old_timesteps:
                        old_timesteps = [latest_action]  # queue was empty

                    # Get queue state before changes
                    old_size, old_timesteps = self._inspect_action_queue()
                    if not old_timesteps:
                        old_timesteps = [latest_action]  # queue was empty

                    # Log incoming actions
                    incoming_timesteps = [a.get_timestep() for a in timed_actions]

                    first_action_timestep = timed_actions[0].get_timestep()
                    server_to_client_latency = (receive_time - timed_actions[0].get_timestamp()) * 1000

                    # self.logger.info(
                    #     f"Received action chunk for step #{first_action_timestep} | "
                    #     f"Latest action: #{latest_action} | "
                    #     f"Incoming actions: {incoming_timesteps[0]}:{incoming_timesteps[-1]} | "
                    #     f"Network latency (server->client): {server_to_client_latency:.2f}ms | "
                    #     f"Deserialization time: {deserialize_time * 1000:.2f}ms"
                    # )
                
                first_action_timestep = timed_actions[0].get_timestep()
                self.logger.info(f"Received first action chunk for step #{first_action_timestep}")
                # Update action queue
                start_time = time.perf_counter()
                if self.rtc_enabled:
                    self._install_rtc_chunk(timed_actions)
                    self._rtc_inflight.clear()
                else:
                    self._aggregate_action_queues(timed_actions, self.config.aggregate_fn)
                queue_update_time = time.perf_counter() - start_time

                self.must_go.set()  # after receiving actions, next empty queue triggers must-go processing!

                if verbose:
                    # Get queue state after changes
                    new_size, new_timesteps = self._inspect_action_queue()

                    with self.latest_action_lock:
                        latest_action = self.latest_action

                    # self.logger.info(
                    #     f"Latest action: {latest_action} | "
                    #     f"Old action steps: {old_timesteps[0]}:{old_timesteps[-1]} | "
                    #     f"Incoming action steps: {incoming_timesteps[0]}:{incoming_timesteps[-1]} | "
                    #     f"Updated action steps: {new_timesteps[0]}:{new_timesteps[-1]}"
                    # )
                    self.logger.debug(
                        f"Queue update complete ({queue_update_time:.6f}s) | "
                        f"Before: {old_size} items | "
                        f"After: {new_size} items | "
                    )

            except grpc.RpcError as e:
                self.logger.error(f"Error receiving actions: {e}")
                if self.rtc_enabled:
                    self._rtc_inflight.clear()

    def actions_available(self):
        """Check if control tick can emit an action (queue and/or interpolator)."""
        if not self.action_interpolator.needs_new_action():
            return True
        with self.action_queue_lock:
            return not self.action_queue.empty()

    def _action_tensor_to_action_dict(self, action_tensor: torch.Tensor) -> dict[str, float]:
        action = {key: action_tensor[i].item() for i, key in enumerate(self.robot.action_features)}
        return action

    def control_loop_action(self, verbose: bool = False) -> dict[str, Any]:
        """Reading and performing actions in local queue (+ optional linear interpolation)."""

        get_start = time.perf_counter()
        timed_action = None

        if self.action_interpolator.needs_new_action():
            with self.action_queue_lock:
                self.action_queue_size.append(self.action_queue.qsize())
                if self.action_queue.empty():
                    self._interp_underruns += 1
                    self.logger.warning(
                        "Action queue underrun (interpolator needs policy action); total=%d",
                        self._interp_underruns,
                    )
                    return {}
                timed_action = self.action_queue.get_nowait()
                with self.latest_action_lock:
                    self.latest_action = timed_action.get_timestep()
            self.action_interpolator.add(timed_action.get_action().detach().cpu())

        action_tensor = self.action_interpolator.get()
        if action_tensor is None:
            self._interp_underruns += 1
            return {}

        get_end = time.perf_counter() - get_start

        _performed_action = self.robot.send_action(self._action_tensor_to_action_dict(action_tensor))

        if timed_action is not None and verbose:
            with self.action_queue_lock:
                current_queue_size = self.action_queue.qsize()
            self.logger.debug(
                "Ts=%s | Action #%s performed | Queue size: %s | pop=%.6fs | interp_policy=%s",
                timed_action.get_timestamp(),
                timed_action.get_timestep(),
                current_queue_size,
                get_end,
                self.action_interpolator.emitted_policy_action,
            )

        return _performed_action
    
    def _ready_to_send_observation(self):
        """Flags when the client is ready to send an observation"""
        if self.rtc_enabled:
            if self._rtc_inflight.is_set():
                return False
            with self.action_queue_lock:
                # Ask early enough that `delay` steps are still queued when the chunk returns.
                return self.action_queue.qsize() <= self._rtc_delay_estimate + 4
        with self.action_queue_lock:
            # 计算动作队列的当前大小与动作块大小的比值，小于等于阈值时，发送观察结果，进行下一次推理
            return self.action_queue.qsize() / self.action_chunk_size <= self._chunk_size_threshold

    def control_loop_observation(self, task: str, verbose: bool = False) -> RawObservation:


        try:
            # Get serialized observation bytes from the function
            start_time = time.perf_counter()

            raw_observation: RawObservation = self.robot.get_observation()
            # 为什么打印为空？
            # self.logger.info(f"Raw observation: {raw_observation}")  # debug: 打印原始观测结果
            raw_observation["task"] = task

            #时间步与观测封装
            with self.latest_action_lock:
                latest_action = self.latest_action

            #对时间戳和时间步进行封装
            if self.rtc_enabled:
                next_timestep, leftover = self._rtc_snapshot()
                delay = self._rtc_delay_estimate if leftover is not None else 0
                observation = TimedObservation(
                    timestamp=time.time(),
                    observation=raw_observation,
                    timestep=next_timestep,
                    must_go=True,
                    rtc_prev_chunk_left_over=leftover,
                    rtc_inference_delay=delay,
                )
            else:
                observation = TimedObservation(
                    timestamp=time.time(),  # need time.time() to compare timestamps across client and server
                    observation=raw_observation,
                    timestep=max(latest_action, 0),
                )

            obs_capture_time = time.perf_counter() - start_time

            # If there are no actions left in the queue, the observation must go through processing!
            # RTC observations always carry the leftover prefix, so the server must not drop them.
            with self.action_queue_lock:
                if not self.rtc_enabled:
                    observation.must_go = self.must_go.is_set() and self.action_queue.empty()
                current_queue_size = self.action_queue.qsize()

            if self.rtc_enabled:
                self._rtc_inflight.set()
            sent = self.send_observation(observation)
            if self.rtc_enabled and not sent:
                self._rtc_inflight.clear()

            self.logger.debug(f"QUEUE SIZE: {current_queue_size} (Must go: {observation.must_go})")
            if observation.must_go:
                # must-go event will be set again after receiving actions
                self.must_go.clear()

            if verbose:
                # Calculate comprehensive FPS metrics
                fps_metrics = self.fps_tracker.calculate_fps_metrics(observation.get_timestamp())

                # self.logger.info(
                #     f"Obs #{observation.get_timestep()} | "
                #     f"Avg FPS: {fps_metrics['avg_fps']:.2f} | "
                #     f"Target: {fps_metrics['target_fps']:.2f}"
                # )

                self.logger.debug(
                    f"Ts={observation.get_timestamp():.6f} | Capturing observation took {obs_capture_time:.6f}s"
                )
            
            # self.logger.info(f"Obs timestep #{observation.get_timestep()} ")
            
            return raw_observation

        except Exception as e:
            self.logger.error(f"Error in observation sender: {e}")

    def control_loop(self, task: str, verbose: bool = False) -> tuple[Observation, Action]:
        """Combined function for executing actions and streaming observations"""
        # Wait at barrier for synchronized start
        self.start_barrier.wait()
        self.logger.info("Control loop thread starting")

        _performed_action = None
        _captured_observation = None

        while self.running:
            control_loop_start = time.perf_counter()
            """Control loop: (1) Performing actions, when available"""
            if self.actions_available(): #检查是否有可执行的动作
                self.logger.debug("Actions available, performing action")
                _performed_action = self.control_loop_action(verbose)

            """Control loop: (2) Streaming observations to the remote policy server"""
            if self._ready_to_send_observation(): #检查是否准备好发送观察结果
                _captured_observation = self.control_loop_observation(task, verbose)

            # self.logger.info(f"Control loop (ms): {(time.perf_counter() - control_loop_start) * 1000:.2f}") #记录单次循环控制时间
            # Dynamically adjust sleep time to maintain the desired control frequency
            time.sleep(max(0, self.config.environment_dt - (time.perf_counter() - control_loop_start)))

        return _captured_observation, _performed_action


@draccus.wrap()
def async_client(cfg: RobotClientConfig):
    '''
    one thread for receiving actions from the server
    one thread for control loop (performing actions and sending observations)
    '''
    logging.info(pformat(asdict(cfg)))

    if cfg.robot.type not in SUPPORTED_ROBOTS:
        raise ValueError(f"Robot {cfg.robot.type} not yet supported!")

    client = RobotClient(cfg)

    if client.start():
        client.logger.info("Starting action receiver thread...")

        # Create and start action receiver thread
        # 创建动作接收线程
        action_receiver_thread = threading.Thread(target=client.receive_actions, daemon=True)

        # Start action receiver thread
        action_receiver_thread.start()

        try:
            # The main thread runs the control loop
            # 调用主线程运行控制循环
            client.control_loop(task=cfg.task)

        finally:
            client.stop()
            action_receiver_thread.join()
            if cfg.debug_visualize_queue_size:
                visualize_action_queue_size(client.action_queue_size)
            client.logger.info("Client stopped")


if __name__ == "__main__":
    async_client()  # run the client