import logging
import math
from typing import Any, Callable, Dict, List, Optional, Tuple, TypeVar, Union

import ray
from ray.data._internal.block_list import BlockList
from ray.data._internal.execution.interfaces import TaskContext
from ray.data._internal.progress_bar import ProgressBar
from ray.data._internal.remote_fn import cached_remote_fn
from ray.data._internal.shuffle import ShuffleOp
from ray.data.block import Block, BlockAccessor, BlockExecStats, BlockMetadata
from ray.data.context import DataContext
from ray.types import ObjectRef
from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy

logger = logging.getLogger(__name__)


T = TypeVar("T")
U = TypeVar("U")


class _MergeTaskSchedule:
    def __init__(self, output_num_blocks: int, num_merge_tasks_per_round: int):
        self.output_num_blocks = output_num_blocks
        self.num_merge_tasks_per_round = num_merge_tasks_per_round
        self.merge_partition_size = output_num_blocks // num_merge_tasks_per_round
        self._partitions_with_extra_task = output_num_blocks % num_merge_tasks_per_round

    def get_num_reducers_per_merge_idx(self, merge_idx: int) -> int:
        """
        Each intermediate merge task will produce outputs for a partition of P
        final reduce tasks. This helper function returns P based on the merge
        task index.
        """
        assert merge_idx < self.num_merge_tasks_per_round
        partition_size = self.merge_partition_size
        if merge_idx < self._partitions_with_extra_task:
            partition_size += 1
        return partition_size

    def get_merge_idx_for_reducer_idx(self, reducer_idx: int) -> int:
        if reducer_idx < self.merge_partition_size * self._partitions_with_extra_task:
            merge_idx = reducer_idx // (self.merge_partition_size + 1)
        else:
            reducer_idx -= (
                self.merge_partition_size + 1
            ) * self._partitions_with_extra_task
            merge_idx = (
                self._partitions_with_extra_task
                + reducer_idx // self.merge_partition_size
            )
        assert merge_idx < self.num_merge_tasks_per_round
        return merge_idx

    def round_robin_reduce_idx_iterator(self):
        """
        When there are multiple nodes, merge tasks are spread throughout the
        cluster to improve load-balancing. Each merge task produces outputs for
        a contiguous partition of reduce tasks. This method creates an iterator
        that returns reduce task indices round-robin across the merge tasks.
        This can be used to submit reduce tasks in a way that spreads the load
        evenly across the cluster.
        """
        idx = 0
        round_idx = 0
        while idx < self.output_num_blocks:
            for merge_idx in range(self.num_merge_tasks_per_round):
                if merge_idx < self._partitions_with_extra_task:
                    reduce_idx = merge_idx * (self.merge_partition_size + 1)
                    partition_size = self.merge_partition_size + 1
                else:
                    reduce_idx = self._partitions_with_extra_task * (
                        self.merge_partition_size + 1
                    )
                    merge_idx -= self._partitions_with_extra_task
                    reduce_idx += merge_idx * self.merge_partition_size
                    partition_size = self.merge_partition_size

                if round_idx >= partition_size:
                    continue

                reduce_idx += round_idx
                yield reduce_idx
                idx += 1
            round_idx += 1


class _PushBasedShuffleStage:
    def __init__(
        self,
        output_num_blocks: int,
        num_rounds: int,
        num_map_tasks_per_round: int,
        merge_task_placement: List[str],
    ):
        self.num_rounds = num_rounds
        self.num_map_tasks_per_round = num_map_tasks_per_round
        self.num_merge_tasks_per_round = len(merge_task_placement)

        node_strategies = {
            node_id: {
                "scheduling_strategy": NodeAffinitySchedulingStrategy(
                    node_id, soft=True
                )
            }
            for node_id in set(merge_task_placement)
        }
        self._merge_task_options = [
            node_strategies[node_id] for node_id in merge_task_placement
        ]

        self.merge_schedule = _MergeTaskSchedule(
            output_num_blocks, self.num_merge_tasks_per_round
        )

    def get_merge_task_options(self, merge_idx):
        return self._merge_task_options[merge_idx]


class _PipelinedStageExecutor:
    def __init__(
        self,
        stage_iter,
        num_tasks_per_round: int,
        max_concurrent_rounds: int = 1,
        progress_bar: Optional[ProgressBar] = None,
    ):
        self._stage_iter = stage_iter
        self._num_tasks_per_round = num_tasks_per_round
        self._max_concurrent_rounds = max_concurrent_rounds
        self._progress_bar = progress_bar

        self._rounds: List[List[ObjectRef]] = []
        self._task_idx = 0

        self._submit_round()

    def __iter__(self):
        return self

    def __next__(self):
        """
        Submit one round of tasks. If we already have the max concurrent rounds
        in flight, first wait for the oldest round of tasks to finish.
        """
        prev_metadata = []
        if all(len(r) == 0 for r in self._rounds):
            raise StopIteration

        if len(self._rounds) >= self._max_concurrent_rounds:
            prev_metadata_refs = self._rounds.pop(0)
            if prev_metadata_refs:
                if self._progress_bar is not None:
                    prev_metadata = self._progress_bar.fetch_until_complete(
                        prev_metadata_refs
                    )
                else:
                    prev_metadata = ray.get(prev_metadata_refs)

        self._submit_round()

        return prev_metadata

    def _submit_round(self):
        assert len(self._rounds) < self._max_concurrent_rounds
        task_round = []
        for _ in range(self._num_tasks_per_round):
            try:
                task_round.append(next(self._stage_iter))
            except StopIteration:
                break
        self._rounds.append(task_round)


class _MapStageIterator:
    def __init__(self, input_blocks_list, shuffle_map, map_args):
        self._input_blocks_list = input_blocks_list
        self._shuffle_map = shuffle_map
        self._map_args = map_args

        self._mapper_idx = 0
        self._map_results = []

    def __iter__(self):
        return self

    def __next__(self):
        if not self._input_blocks_list:
            raise StopIteration

        block = self._input_blocks_list.pop(0)
        # NOTE(swang): Results are shuffled between map and merge tasks, so
        # there is no advantage to colocating specific map and merge tasks.
        # Therefore, we do not specify a node affinity policy for map tasks
        # in case the caller or Ray has a better scheduling strategy, e.g.,
        # based on data locality.
        map_result = self._shuffle_map.remote(
            self._mapper_idx,
            block,
            *self._map_args,
        )
        metadata_ref = map_result.pop(-1)
        self._map_results.append(map_result)
        self._mapper_idx += 1
        return metadata_ref

    def pop_map_results(self) -> List[List[ObjectRef]]:
        map_results = self._map_results
        self._map_results = []
        return map_results


class _MergeStageIterator:
    def __init__(
        self,
        map_stage_iter: _MapStageIterator,
        shuffle_merge,
        stage: _PushBasedShuffleStage,
        reduce_args,
    ):
        self._map_stage_iter = map_stage_iter
        self._shuffle_merge = shuffle_merge
        self._stage = stage
        self._reduce_args = reduce_args

        self._merge_idx = 0
        self._map_result_buffer = None
        # Final outputs from the map-merge stage.
        # This is a map from merge task index to a nested list of merge results
        # (ObjectRefs). Each merge task index corresponds to a partition of P
        # final reduce tasks.
        self._all_merge_results = [
            [] for _ in range(self._stage.num_merge_tasks_per_round)
        ]

    def __next__(self):
        if not self._map_result_buffer or not self._map_result_buffer[0]:
            assert self._merge_idx == 0
            self._map_result_buffer = self._map_stage_iter.pop_map_results()

        if not self._map_result_buffer:
            raise StopIteration

        # Shuffle the map results for the merge tasks.
        merge_args = [map_result.pop(0) for map_result in self._map_result_buffer]
        num_merge_returns = self._stage.merge_schedule.get_num_reducers_per_merge_idx(
            self._merge_idx
        )
        merge_result = self._shuffle_merge.options(
            num_returns=1 + num_merge_returns,
            **self._stage.get_merge_task_options(self._merge_idx),
        ).remote(
            *merge_args,
            reduce_args=self._reduce_args,
        )
        metadata_ref = merge_result.pop(-1)
        self._all_merge_results[self._merge_idx].append(merge_result)
        del merge_result

        self._merge_idx += 1
        self._merge_idx %= self._stage.num_merge_tasks_per_round
        return metadata_ref

    def pop_merge_results(self) -> List[List[ObjectRef]]:
        all_merge_results = self._all_merge_results
        self._all_merge_results = []
        return all_merge_results


class _ReduceStageIterator:
    def __init__(
        self,
        stage: _PushBasedShuffleStage,
        shuffle_reduce,
        all_merge_results: List[List[List[ObjectRef]]],
        ray_remote_args,
        reduce_args: List[Any],
    ):
        self._shuffle_reduce = shuffle_reduce
        self._stage = stage
        self._reduce_arg_blocks: List[Tuple[int, List[ObjectRef]]] = []
        self._ray_remote_args = ray_remote_args
        self._reduce_args = reduce_args

        for reduce_idx in self._stage.merge_schedule.round_robin_reduce_idx_iterator():
            merge_idx = self._stage.merge_schedule.get_merge_idx_for_reducer_idx(
                reduce_idx
            )
            reduce_arg_blocks = [
                merge_results.pop(0) for merge_results in all_merge_results[merge_idx]
            ]
            self._reduce_arg_blocks.append((reduce_idx, reduce_arg_blocks))
        assert len(self._reduce_arg_blocks) == stage.merge_schedule.output_num_blocks

        for merge_idx, merge_results in enumerate(all_merge_results):
            assert all(len(merge_result) == 0 for merge_result in merge_results), (
                "Reduce stage did not process outputs from merge tasks at index: "
                f"{merge_idx}"
            )

        self._reduce_results: List[Tuple[int, ObjectRef]] = []

    def __iter__(self):
        return self

    def __next__(self):
        if not self._reduce_arg_blocks:
            raise StopIteration

        reduce_idx, reduce_arg_blocks = self._reduce_arg_blocks.pop(0)
        merge_idx = self._stage.merge_schedule.get_merge_idx_for_reducer_idx(reduce_idx)
        # Submit one partition of reduce tasks, one for each of the P
        # outputs produced by the corresponding merge task.
        # We also add the merge task arguments so that the reduce task
        # is colocated with its inputs.
        block, meta = self._shuffle_reduce.options(
            **self._ray_remote_args,
            **self._stage.get_merge_task_options(merge_idx),
            num_returns=2,
        ).remote(*self._reduce_args, *reduce_arg_blocks, partial_reduce=False)
        self._reduce_results.append((reduce_idx, block))
        return meta

    def pop_reduce_results(self):
        reduce_results = self._reduce_results
        self._reduce_results = []
        return reduce_results


class PushBasedShufflePlan(ShuffleOp):
    """
    Push-based shuffle merges intermediate map outputs on the reducer nodes
    while other map tasks are executing. The merged outputs are merged again
    during a final reduce stage. This works as follows:

    1. Submit rounds of concurrent map and merge tasks until all map inputs
    have been processed. In each round, we execute:

       M map tasks
         Each produces N outputs. Each output contains P blocks.
       N merge tasks
         Takes 1 output from each of M map tasks.
         Each produces P outputs.
       Where M and N are chosen to maximize parallelism across CPUs. Note that
       this assumes that all CPUs in the cluster will be dedicated to the
       shuffle job.

       Map and merge tasks are pipelined so that we always merge the previous
       round of map outputs while executing the next round of map tasks.

    2. In the final reduce stage:
       R reduce tasks
         Takes 1 output from one of the merge tasks from every round.

    Notes:
        N * P = R = total number of output blocks
        M / N = merge factor - the ratio of map : merge tasks is to improve
          pipelined parallelism. For example, if map takes twice as long to
          execute as merge, then we should set this to 2.
        See paper at https://arxiv.org/abs/2203.05072 for more details.
    """

    def execute(
        self,
        input_blocks: BlockList,
        output_num_blocks: int,
        clear_input_blocks: bool,
        *,
        map_ray_remote_args: Optional[Dict[str, Any]] = None,
        reduce_ray_remote_args: Optional[Dict[str, Any]] = None,
        merge_factor: int = 2,
        ctx: Optional[TaskContext] = None,
    ) -> Tuple[BlockList, Dict[str, List[BlockMetadata]]]:
        logger.info("Using experimental push-based shuffle.")
        # TODO(swang): For jobs whose reduce work is heavier than the map work,
        # we should support fractional merge factors.
        # TODO(swang): For large jobs, we should try to choose the merge factor
        # automatically, e.g., by running one test round of map and merge tasks
        # and comparing their run times.
        # TODO(swang): Add option to automatically reduce write amplification
        # during map-merge stage, by limiting how many partitions can be
        # processed concurrently.
        input_blocks_list = input_blocks.get_blocks()
        owned_by_consumer = input_blocks._owned_by_consumer
        # Preemptively clear the blocks list since we will incrementally delete
        # the last remaining references as we submit the dependent map tasks
        # during the map-merge stage.
        if clear_input_blocks:
            input_blocks.clear()

        if map_ray_remote_args is None:
            map_ray_remote_args = {}
        if reduce_ray_remote_args is None:
            reduce_ray_remote_args = {}
        # The placement strategy for reduce tasks is overwritten to colocate
        # them with their inputs from the merge stage, so remove any
        # pre-specified scheduling strategy here.
        reduce_ray_remote_args = reduce_ray_remote_args.copy()
        reduce_ray_remote_args.pop("scheduling_strategy", None)

        # Compute all constants used for task scheduling.
        num_cpus_per_node_map = _get_num_cpus_per_node_map()
        stage = self._compute_shuffle_schedule(
            num_cpus_per_node_map,
            len(input_blocks_list),
            merge_factor,
            output_num_blocks,
        )

        map_fn = self._map_partition
        merge_fn = self._merge

        def map_partition(*args, **kwargs):
            return map_fn(self.map, *args, **kwargs)

        def merge(*args, **kwargs):
            return merge_fn(self.reduce, *args, **kwargs)

        shuffle_map = cached_remote_fn(map_partition)
        shuffle_map = shuffle_map.options(
            **map_ray_remote_args,
            num_returns=1 + stage.num_merge_tasks_per_round,
        )

        map_stage_iter = _MapStageIterator(
            input_blocks_list,
            shuffle_map,
            [output_num_blocks, stage.merge_schedule, *self._map_args],
        )

        should_close_bar = True
        if ctx is not None and ctx.sub_progress_bar_dict is not None:
            bar_name = "ShuffleMap"
            assert bar_name in ctx.sub_progress_bar_dict, ctx.sub_progress_bar_dict
            map_bar = ctx.sub_progress_bar_dict[bar_name]
            should_close_bar = False
        else:
            map_bar = ProgressBar(
                "Shuffle Map", position=0, total=len(input_blocks_list)
            )

        map_stage_executor = _PipelinedStageExecutor(
            map_stage_iter, stage.num_map_tasks_per_round, progress_bar=map_bar
        )

        shuffle_merge = cached_remote_fn(merge)
        merge_stage_iter = _MergeStageIterator(
            map_stage_iter, shuffle_merge, stage, self._reduce_args
        )
        merge_stage_executor = _PipelinedStageExecutor(
            merge_stage_iter, stage.num_merge_tasks_per_round, max_concurrent_rounds=2
        )

        # Execute the map-merge stage. This submits tasks in rounds of M map
        # tasks and N merge tasks each. Task execution between map and merge is
        # pipelined, so that while executing merge for one round of inputs, we
        # also execute the map tasks for the following round.
        map_done = False
        merge_done = False
        map_stage_metadata = []
        merge_stage_metadata = []
        while not (map_done and merge_done):
            try:
                map_stage_metadata += next(map_stage_executor)
            except StopIteration:
                map_done = True
                break

            try:
                merge_stage_metadata += next(merge_stage_executor)
            except StopIteration:
                merge_done = True
                break

        if should_close_bar:
            map_bar.close()
        all_merge_results = merge_stage_iter.pop_merge_results()

        # Execute and wait for the reduce stage.
        should_close_bar = True
        if ctx is not None and ctx.sub_progress_bar_dict is not None:
            bar_name = "ShuffleReduce"
            assert bar_name in ctx.sub_progress_bar_dict, ctx.sub_progress_bar_dict
            reduce_bar = ctx.sub_progress_bar_dict[bar_name]
            should_close_bar = False
        else:
            reduce_bar = ProgressBar("Shuffle Reduce", total=output_num_blocks)

        shuffle_reduce = cached_remote_fn(self.reduce)
        reduce_stage_iter = _ReduceStageIterator(
            stage,
            shuffle_reduce,
            all_merge_results,
            reduce_ray_remote_args,
            self._reduce_args,
        )

        max_reduce_tasks_in_flight = output_num_blocks
        ctx = DataContext.get_current()
        if ctx.pipeline_push_based_shuffle_reduce_tasks:
            # If pipelining is enabled, we should still try to utilize all
            # cores.
            max_reduce_tasks_in_flight = min(
                max_reduce_tasks_in_flight, sum(num_cpus_per_node_map.values())
            )

        reduce_stage_executor = _PipelinedStageExecutor(
            reduce_stage_iter,
            max_reduce_tasks_in_flight,
            max_concurrent_rounds=2,
            progress_bar=reduce_bar,
        )
        reduce_stage_metadata = []
        while True:
            try:
                reduce_stage_metadata += next(reduce_stage_executor)
            except StopIteration:
                break

        new_blocks = reduce_stage_iter.pop_reduce_results()
        sorted_blocks = [
            (block[0], block[1], reduce_stage_metadata[i])
            for i, block in enumerate(new_blocks)
        ]
        sorted_blocks.sort(key=lambda x: x[0])
        _, new_blocks, reduce_stage_metadata = zip(*sorted_blocks)
        del sorted_blocks

        assert (
            len(new_blocks) == output_num_blocks
        ), f"Expected {output_num_blocks} outputs, produced {len(new_blocks)}"

        if should_close_bar:
            reduce_bar.close()

        stats = {
            "map": map_stage_metadata,
            "merge": merge_stage_metadata,
            "reduce": reduce_stage_metadata,
        }

        return (
            BlockList(
                list(new_blocks),
                list(reduce_stage_metadata),
                owned_by_consumer=owned_by_consumer,
            ),
            stats,
        )

    @staticmethod
    def _map_partition(
        map_fn,
        idx: int,
        block: Block,
        output_num_blocks: int,
        schedule: _MergeTaskSchedule,
        *map_args: List[Any],
    ) -> List[Union[BlockMetadata, Block]]:
        mapper_outputs = map_fn(idx, block, output_num_blocks, *map_args)
        meta = mapper_outputs.pop(-1)

        parts = []
        merge_idx = 0
        while mapper_outputs:
            partition_size = schedule.get_num_reducers_per_merge_idx(merge_idx)
            parts.append(mapper_outputs[:partition_size])
            mapper_outputs = mapper_outputs[partition_size:]
            merge_idx += 1
        assert len(parts) == schedule.num_merge_tasks_per_round, (
            len(parts),
            schedule.num_merge_tasks_per_round,
        )
        return parts + [meta]

    @staticmethod
    def _merge(
        reduce_fn,
        *all_mapper_outputs: List[List[Block]],
        reduce_args: Optional[List[Any]] = None,
    ) -> List[Union[BlockMetadata, Block]]:
        """
        Returns list of [BlockMetadata, O1, O2, O3, ...output_num_blocks].
        """
        assert (
            len({len(mapper_outputs) for mapper_outputs in all_mapper_outputs}) == 1
        ), "Received different number of map inputs"
        stats = BlockExecStats.builder()
        if not reduce_args:
            reduce_args = []

        num_rows = 0
        size_bytes = 0
        schema = None
        for i, mapper_outputs in enumerate(zip(*all_mapper_outputs)):
            block, meta = reduce_fn(*reduce_args, *mapper_outputs, partial_reduce=True)
            yield block

            block = BlockAccessor.for_block(block)
            num_rows += block.num_rows()
            size_bytes += block.size_bytes()
            schema = block.schema()
            del block

        yield BlockMetadata(
            num_rows=num_rows,
            size_bytes=size_bytes,
            schema=schema,
            input_files=None,
            exec_stats=stats.build(),
        )

    @staticmethod
    def _compute_shuffle_schedule(
        num_cpus_per_node_map: Dict[str, int],
        num_input_blocks: int,
        merge_factor: int,
        num_output_blocks: int,
    ) -> _PushBasedShuffleStage:
        num_cpus_total = sum(v for v in num_cpus_per_node_map.values())
        task_parallelism = min(num_cpus_total, num_input_blocks)

        num_tasks_per_map_merge_group = merge_factor + 1
        num_merge_tasks_per_round = 0
        merge_task_placement = []
        leftover_cpus = 0
        # Compute the total number of merge tasks and their node placement.
        # Each merge task should be grouped with `merge_factor` map tasks for
        # pipelining. These groups should then be spread across nodes according
        # to CPU availability for load-balancing.
        for node, num_cpus in num_cpus_per_node_map.items():
            node_parallelism = min(
                num_cpus, num_input_blocks // len(num_cpus_per_node_map)
            )
            num_merge_tasks = node_parallelism // num_tasks_per_map_merge_group
            for i in range(num_merge_tasks):
                merge_task_placement.append(node)
            num_merge_tasks_per_round += num_merge_tasks

            # Handle the case where a single node cannot fit a group of map and
            # merge tasks, but we can spread the group across multiple distinct
            # nodes.
            leftover_cpus += node_parallelism % num_tasks_per_map_merge_group
            if num_merge_tasks == 0 and leftover_cpus > num_tasks_per_map_merge_group:
                merge_task_placement.append(node)
                num_merge_tasks_per_round += 1
                leftover_cpus -= num_tasks_per_map_merge_group
        if num_merge_tasks_per_round == 0:
            merge_task_placement.append(list(num_cpus_per_node_map)[0])
            num_merge_tasks_per_round = 1

        assert num_merge_tasks_per_round == len(merge_task_placement)
        num_map_tasks_per_round = max(task_parallelism - num_merge_tasks_per_round, 1)

        num_rounds = math.ceil(num_input_blocks / num_map_tasks_per_round)
        return _PushBasedShuffleStage(
            num_output_blocks,
            num_rounds,
            num_map_tasks_per_round,
            merge_task_placement,
        )


def _execute_pipelined_stage(
    stage_fn: Callable[[T], Tuple[ObjectRef, U]],
    prev_metadata_refs: List[ObjectRef],
    stage_args: List[T],
    progress_bar: Optional[ProgressBar] = None,
) -> Tuple[List[BlockMetadata], List[ObjectRef], List[U]]:
    """
    Helper function to execute a stage of tasks. This will wait for the
    previous round of tasks to complete before submitting the next.
    """
    # TODO(swang): Straggler tasks can cause pipeline bubbles. Instead of
    # waiting for all previous tasks, we should wait for some tasks on each
    # node to finish.
    if progress_bar is not None:
        prev_metadata = progress_bar.fetch_until_complete(prev_metadata_refs)
    else:
        prev_metadata = ray.get(prev_metadata_refs)
    prev_metadata_refs.clear()

    metadata_refs = []
    data_outputs = []
    while stage_args:
        arg = stage_args.pop(0)
        metadata_ref, data_output = stage_fn(arg)
        metadata_refs.append(metadata_ref)
        data_outputs.append(data_output)
    return prev_metadata, metadata_refs, data_outputs


def _get_num_cpus_per_node_map() -> Dict[str, int]:
    nodes = ray.nodes()
    # Map from per-node resource name to number of CPUs available on that
    # node.
    num_cpus_per_node_map = {}
    for node in nodes:
        resources = node["Resources"]
        num_cpus = int(resources.get("CPU", 0))
        if num_cpus == 0:
            continue
        num_cpus_per_node_map[node["NodeID"]] = num_cpus
    return num_cpus_per_node_map
