import time
from typing import List, Optional

import torch

from colossalai.gemini.memory_tracer import SyncCudaMemoryMonitor
from colossalai.gemini.stateful_tensor import StatefulTensor
from colossalai.utils.memory import colo_device_memory_used

from .memory_stats import MemStats


class MemStatsCollector:
    """
    A Memory statistic collector.
    It works in two phases.
    Phase 1. Collection Phase: collect memory usage statistics of CPU and GPU.
    The first iteration of DNN training.
    Phase 2. Runtime Phase: use the read-only collected stats
    The rest iterations of DNN training.

    It has a Sampling counter which is reset after DNN training iteration.
    """

    def __init__(self, memstats: Optional[MemStats] = None) -> None:
        self._mem_monitor = SyncCudaMemoryMonitor()
        self._sampling_time = []

        self._start_flag = False
        self._step_idx = 0
        self._step_total = 0
        if memstats is not None:
            self.use_outside_memstats = True
            self._memstats = memstats
        else:
            self.use_outside_memstats = False
            self._memstats = MemStats()

    def next_period_non_model_data_usage(self, device_type: str) -> int:
        """Get max non model data memory usage of current sampling period

        Args:
            device_type (str): device type, can be 'cpu' or 'cuda'.

        Returns:
            int: max non model data memory usage of current sampling period
        """
        assert not self._start_flag, 'Cannot get mem stats info during collection phase.'
        assert self._step_total > 0, 'Cannot get mem stats info before collection phase.'
        next_non_model_data = self._memstats.non_model_data_list(device_type)[self._step_idx]
        self._step_idx = (self._step_idx + 1) % self._step_total
        return next_non_model_data

    @property
    def sampling_time(self):
        return [t - self._sampling_time[0] for t in self._sampling_time]

    def start_collection(self):
        self._start_flag = True
        self._mem_monitor.start()

    def finish_collection(self):
        self.sample_overall_data()
        self._step_total = len(self._sampling_time)
        self._start_flag = False
        self._mem_monitor.finish()

    def sample_model_data(self) -> None:
        """Sampling model data statistics.
        """
        if self._start_flag and not self.use_outside_memstats:
            cuda_mem = StatefulTensor.GST_MGR.total_mem['cuda']
            cpu_mem = StatefulTensor.GST_MGR.total_mem['cpu']
            self._memstats.append_model_data('cuda', cuda_mem)
            self._memstats.append_model_data('cpu', cpu_mem)

    def sample_overall_data(self) -> None:
        """Sampling non model data statistics.
        """
        if self._start_flag and not self.use_outside_memstats:
            # overall data recording is after model data recording
            if len(self._memstats._model_data_cuda_list) == 0:
                return

            self._memstats.append_overall_data('cuda', self._mem_monitor.finish())
            self._memstats.append_overall_data('cpu', colo_device_memory_used(torch.device('cpu')))

            assert len(self._memstats._model_data_cuda_list) == len(self._memstats._overall_cuda_list)

            self._memstats.append_non_model_data('cuda')
            self._memstats.append_non_model_data('cpu')
            self._mem_monitor.start()

        if self._start_flag:
            self._sampling_time.append(time.time())

    def clear(self) -> None:
        self._memstats.clear()
        self._start_flag = False
        self._step_idx = 0
        self._step_total = 0