# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# License: MIT
# Misc stuff used by Demucs
import functools
import math
import torch
from torch.nn import functional as F
import typing as tp


# ############################################################################################################################
# utils.py
# ############################################################################################################################


def unfold(a, kernel_size, stride):
    """Given input of size [*X, T], output Tensor of size [*X, 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


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 shutdown(self, wait=True, cancel_futures=True):
        return

    def __enter__(self):
        return self

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


# ############################################################################################################################
# state.py
# ############################################################################################################################


def capture_init(init):
    @functools.wraps(init)
    def __init__(self, *args, **kwargs):
        self._init_args_kwargs = (args, kwargs)
        init(self, *args, **kwargs)

    return __init__