from typing import Any, Dict
import random

# Import ray before psutil will make sure we use psutil's bundled version
import ray  # noqa F401
import psutil  # noqa E402

from ray.rllib.utils.annotations import ExperimentalAPI, override
from ray.rllib.utils.replay_buffers.replay_buffer import ReplayBuffer
from ray.rllib.utils.typing import SampleBatchType
from ray.rllib.execution.buffers.replay_buffer import warn_replay_capacity


@ExperimentalAPI
class ReservoirBuffer(ReplayBuffer):
    """This buffer implements reservoir sampling.

    The algorithm has been described by Jeffrey S. Vitter in "Random sampling
    with a reservoir". See https://www.cs.umd.edu/~samir/498/vitter.pdf for
    the full paper.
    """

    def __init__(self, capacity: int = 10000, storage_unit: str = "timesteps"):
        """Initializes a ReservoirBuffer instance.

        Args:
            capacity: Max number of timesteps to store in the FIFO
                buffer. After reaching this number, older samples will be
                dropped to make space for new ones.
            storage_unit: Either 'sequences' or 'timesteps'. Specifies how
                experiences are stored.
        """
        ReplayBuffer.__init__(self, capacity, storage_unit)
        self._num_add_calls = 0
        self._num_evicted = 0

    @ExperimentalAPI
    @override(ReplayBuffer)
    def add(self, batch: SampleBatchType, **kwargs) -> None:
        """Adds a batch of experiences.

        Args:
            batch: SampleBatch to add to this buffer's storage.
        """
        # Update add counts.
        self._num_add_calls += 1
        # Update our timesteps counts.
        self._num_timesteps_added += batch.count
        self._num_timesteps_added_wrap += batch.count

        if self._num_timesteps_added < self.capacity:
            ReplayBuffer.add(self, batch)
        else:
            # Eviction of older samples has already started (buffer is "full")
            self._eviction_started = True
            idx = random.randint(0, self._num_add_calls - 1)
            if idx < self.capacity:
                self._num_evicted += 1
                self._evicted_hit_stats.push(self._hit_count[idx])
                self._hit_count[idx] = 0
                self._storage[idx] = batch

                assert batch.count > 0, batch
                warn_replay_capacity(item=batch, num_items=self.capacity / batch.count)

    @ExperimentalAPI
    @override(ReplayBuffer)
    def stats(self, debug: bool = False) -> dict:
        """Returns the stats of this buffer.

        Args:
            debug: If True, adds sample eviction statistics to the returned
                stats dict.

        Returns:
            A dictionary of stats about this buffer.
        """
        data = {
            "num_evicted": self._num_evicted,
            "num_add_calls": self._num_add_calls,
        }
        parent = ReplayBuffer.stats(self, debug)
        parent.update(data)
        return parent

    @ExperimentalAPI
    @override(ReplayBuffer)
    def get_state(self) -> Dict[str, Any]:
        """Returns all local state.

        Returns:
            The serializable local state.
        """
        parent = ReplayBuffer.get_state(self)
        parent.update(self.stats())
        return parent

    @ExperimentalAPI
    @override(ReplayBuffer)
    def set_state(self, state: Dict[str, Any]) -> None:
        """Restores all local state to the provided `state`.

        Args:
            state: The new state to set this buffer. Can be
                obtained by calling `self.get_state()`.
        """
        self._num_evicted = state["num_evicted"]
        self._num_add_calls = state["num_add_calls"]
        ReplayBuffer.set_state(self, state)