#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@File    : utils.py
@Time    : 2023/8/8 下午4:26
@Author  : waytan
@Contact : waytan@tencent.com
@License : (C)Copyright 2023, Tencent
@Desc    : utils
"""
from contextlib import contextmanager
import math
import os
import tempfile
import typing as tp
import json
import subprocess

import torch
from torch.nn import functional as F
from torch.utils.data import Subset


def unfold(a, kernel_size, stride):
    """Given input of size [*OT, T], output Tensor of size [*OT, F, K]
    with K the kernel size, by extracting frames with the given stride.

    This will pad the input so that `F = ceil(T / K)`.

    see https://github.com/pytorch/pytorch/issues/60466
    """
    *shape, length = a.shape
    n_frames = math.ceil(length / stride)
    tgt_length = (n_frames - 1) * stride + kernel_size
    a = F.pad(a, (0, tgt_length - length))
    strides = list(a.stride())
    assert strides[-1] == 1, 'data should be contiguous'
    strides = strides[:-1] + [stride, 1]
    return a.as_strided([*shape, n_frames, kernel_size], strides)


def center_trim(tensor: torch.Tensor, reference: tp.Union[torch.Tensor, int]):
    """
    Center trim `tensor` with respect to `reference`, along the last dimension.
    `reference` can also be a number, representing the length to trim to.
    If the size difference != 0 mod 2, the extra sample is removed on the right side.
    """
    ref_size: int
    if isinstance(reference, torch.Tensor):
        ref_size = reference.size(-1)
    else:
        ref_size = reference
    delta = tensor.size(-1) - ref_size
    if delta < 0:
        raise ValueError("tensor must be larger than reference. " f"Delta is {delta}.")
    if delta:
        tensor = tensor[..., delta // 2:-(delta - delta // 2)]
    return tensor


def pull_metric(history: tp.List[dict], name: str):
    out = []
    for metrics in history:
        metric = metrics
        for part in name.split("."):
            metric = metric[part]
        out.append(metric)
    return out


def sizeof_fmt(num: float, suffix: str = 'B'):
    """
    Given `num` bytes, return human readable size.
    Taken from https://stackoverflow.com/a/1094933
    """
    for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']:
        if abs(num) < 1024.0:
            return "%3.1f%s%s" % (num, unit, suffix)
        num /= 1024.0
    return "%.1f%s%s" % (num, 'Yi', suffix)


@contextmanager
def temp_filenames(count: int, delete=True):
    names = []
    try:
        for _ in range(count):
            names.append(tempfile.NamedTemporaryFile(delete=False).name)
        yield names
    finally:
        if delete:
            for name in names:
                os.unlink(name)


def random_subset(dataset, max_samples: int, seed: int = 42):
    if max_samples >= len(dataset):
        return dataset

    generator = torch.Generator().manual_seed(seed)
    perm = torch.randperm(len(dataset), generator=generator)
    return Subset(dataset, perm[:max_samples].tolist())


class DummyPoolExecutor:
    class DummyResult:
        def __init__(self, func, *args, **kwargs):
            self.func = func
            self.args = args
            self.kwargs = kwargs

        def result(self):
            return self.func(*self.args, **self.kwargs)

    def __init__(self, workers=0):
        pass

    def submit(self, func, *args, **kwargs):
        return DummyPoolExecutor.DummyResult(func, *args, **kwargs)

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_value, exc_tb):
        return