import logging
from pathlib import Path
from typing import Sequence
from typing import Union
import warnings

import torch
from typeguard import check_argument_types
from typing import Collection

from espnet2.train.reporter import Reporter


@torch.no_grad()
def average_nbest_models(
    output_dir: Path,
    reporter: Reporter,
    best_model_criterion: Sequence[Sequence[str]],
    nbest: Union[Collection[int], int],
) -> None:
    """Generate averaged model from n-best models

    Args:
        output_dir: The directory contains the model file for each epoch
        reporter: Reporter instance
        best_model_criterion: Give criterions to decide the best model.
            e.g. [("valid", "loss", "min"), ("train", "acc", "max")]
        nbest:
    """
    assert check_argument_types()
    if isinstance(nbest, int):
        nbests = [nbest]
    else:
        nbests = list(nbest)
    if len(nbests) == 0:
        warnings.warn("At least 1 nbest values are required")
        nbests = [1]
    # 1. Get nbests: List[Tuple[str, str, List[Tuple[epoch, value]]]]
    nbest_epochs = [
        (ph, k, reporter.sort_epochs_and_values(ph, k, m)[: max(nbests)])
        for ph, k, m in best_model_criterion
        if reporter.has(ph, k)
    ]

    _loaded = {}
    for ph, cr, epoch_and_values in nbest_epochs:
        _nbests = [i for i in nbests if i <= len(epoch_and_values)]
        if len(_nbests) == 0:
            _nbests = [1]

        for n in _nbests:
            if n == 0:
                continue
            elif n == 1:
                # The averaged model is same as the best model
                e, _ = epoch_and_values[0]
                op = output_dir / f"{e}epoch.pth"
                sym_op = output_dir / f"{ph}.{cr}.ave_1best.pth"
                if sym_op.is_symlink() or sym_op.exists():
                    sym_op.unlink()
                sym_op.symlink_to(op.name)
            else:
                op = output_dir / f"{ph}.{cr}.ave_{n}best.pth"
                logging.info(
                    f"Averaging {n}best models: " f'criterion="{ph}.{cr}": {op}'
                )

                avg = None
                # 2.a. Averaging model
                for e, _ in epoch_and_values[:n]:
                    if e not in _loaded:
                        _loaded[e] = torch.load(
                            output_dir / f"{e}epoch.pth",
                            map_location="cpu",
                        )
                    states = _loaded[e]

                    if avg is None:
                        avg = states
                    else:
                        # Accumulated
                        for k in avg:
                            avg[k] = avg[k] + states[k]
                for k in avg:
                    if str(avg[k].dtype).startswith("torch.int"):
                        # For int type, not averaged, but only accumulated.
                        # e.g. BatchNorm.num_batches_tracked
                        # (If there are any cases that requires averaging
                        #  or the other reducing method, e.g. max/min, for integer type,
                        #  please report.)
                        pass
                    else:
                        avg[k] = avg[k] / n

                # 2.b. Save the ave model and create a symlink
                torch.save(avg, op)

        # 3. *.*.ave.pth is a symlink to the max ave model
        op = output_dir / f"{ph}.{cr}.ave_{max(_nbests)}best.pth"
        sym_op = output_dir / f"{ph}.{cr}.ave.pth"
        if sym_op.is_symlink() or sym_op.exists():
            sym_op.unlink()
        sym_op.symlink_to(op.name)
