from contextlib import contextmanager
from enum import Enum
from functools import partial
from typing import Dict, List

import torch
import torch.distributed as dist

from colossalai.logging import DistributedLogger
from colossalai.tensor.param_op_hook import ColoParamOpHook
from colossalai.utils import is_ddp_ignored
from colossalai.zero.gemini import TensorState
from colossalai.zero.gemini.gemini_mgr import GeminiManager

from .chunk import Chunk


class TrainingPhase(Enum):
    FORWARD = 0
    BACKWARD = 1


logger = DistributedLogger("gemini_hook")


class GeminiZeROHook(ColoParamOpHook):
    def __init__(self, gemini_manager: GeminiManager, max_prefetch: int = 0) -> None:
        super().__init__()
        self._gemini_manager = gemini_manager
        self._chunk_manager = gemini_manager.chunk_manager
        self._training_phase = TrainingPhase.FORWARD
        self._max_prefetch = max_prefetch
        self._async_works: Dict[Chunk, dist.work] = {}

    def wait_chunks(self, chunks: List[Chunk]) -> List[Chunk]:
        non_prefetched_chunks = []
        for chunk in chunks:
            if chunk in self._async_works:
                print(f"prefetched {chunk.count_id}")
                self._async_works[chunk].wait()
                del self._async_works[chunk]
            else:
                non_prefetched_chunks.append(chunk)
        return non_prefetched_chunks

    def pre_op(self, params):
        params = [p for p in params if not is_ddp_ignored(p)]
        all_chunks = self._chunk_manager.get_chunks(params)
        # wait for prefetched chunks, filter those are not prefetched
        chunks_fetch_sync = tuple(self.wait_chunks(all_chunks))
        for p in params:
            self._chunk_manager.trans_tensor_state(p, TensorState.COMPUTE)
        self._gemini_manager.sample_overall_data()
        self._gemini_manager.adjust_layout(all_chunks, record_anyway=self._max_prefetch > 0)
        # fetch the rest chunks synchronously
        for chunk in chunks_fetch_sync:
            self._chunk_manager.access_chunk(chunk)
        chunks_fetch_async = self._gemini_manager.placement_policy.get_prefetch_chunks(max_prefetch=self._max_prefetch)
        for chunk in chunks_fetch_async:
            maybe_work = self._chunk_manager.access_chunk(chunk, async_access=True)
            if maybe_work is not None:
                self._async_works[chunk] = maybe_work

        # record cuda model data of the current OP, including memory for prefetched chunks
        self._gemini_manager.record_model_data_volume()

    def post_op(self, params):
        params = [p for p in params if not is_ddp_ignored(p)]
        for p in params:
            tensor_state = (
                TensorState.HOLD
                if self._training_phase == TrainingPhase.FORWARD or not p.requires_grad
                else TensorState.HOLD_AFTER_BWD
            )
            self._chunk_manager.trans_tensor_state(p, tensor_state)

    def pre_forward(self, params: List[torch.Tensor]) -> None:
        self.pre_op(params)

    def post_forward(self, params: List[torch.Tensor]) -> None:
        self.post_op(params)

    def pre_backward(self, params: List[torch.Tensor]) -> None:
        self.pre_op(params)

    def post_backward(self, params: List[torch.Tensor]) -> None:
        self.post_op(params)

    @contextmanager
    def switch_training_phase(self, training_phase: TrainingPhase = TrainingPhase.BACKWARD):
        if training_phase == TrainingPhase.FORWARD:
            self._cur_param_idx = 0
        else:
            self._cur_param_idx = len(self._param_visited_order) - 1

        old_training_phase = self._training_phase
        try:
            self._training_phase = training_phase
            yield
        finally:
            self._training_phase = old_training_phase

    switch_to_backward = switch_training_phase
    switch_to_forward = partial(switch_to_backward, training_phase=TrainingPhase.FORWARD)