# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

# pyre-unsafe


import torch
from pytorch3d import _C


def sample_pdf(
    bins: torch.Tensor,
    weights: torch.Tensor,
    n_samples: int,
    det: bool = False,
    eps: float = 1e-5,
) -> torch.Tensor:
    """
    Samples probability density functions defined by bin edges `bins` and
    the non-negative per-bin probabilities `weights`.

    Args:
        bins: Tensor of shape `(..., n_bins+1)` denoting the edges of the sampling bins.
        weights: Tensor of shape `(..., n_bins)` containing non-negative numbers
            representing the probability of sampling the corresponding bin.
        n_samples: The number of samples to draw from each set of bins.
        det: If `False`, the sampling is random. `True` yields deterministic
            uniformly-spaced sampling from the inverse cumulative density function.
        eps: A constant preventing division by zero in case empty bins are present.

    Returns:
        samples: Tensor of shape `(..., n_samples)` containing `n_samples` samples
            drawn from each probability distribution.

    Refs:
        [1] https://github.com/bmild/nerf/blob/55d8b00244d7b5178f4d003526ab6667683c9da9/run_nerf_helpers.py#L183  # noqa E501
    """
    if torch.is_grad_enabled() and (bins.requires_grad or weights.requires_grad):
        raise NotImplementedError("sample_pdf differentiability.")
    if weights.min() <= -eps:
        raise ValueError("Negative weights provided.")
    batch_shape = bins.shape[:-1]
    n_bins = weights.shape[-1]
    if n_bins + 1 != bins.shape[-1] or weights.shape[:-1] != batch_shape:
        shapes = f"{bins.shape}{weights.shape}"
        raise ValueError("Inconsistent shapes of bins and weights: " + shapes)
    output_shape = batch_shape + (n_samples,)

    if det:
        u = torch.linspace(0.0, 1.0, n_samples, device=bins.device, dtype=torch.float32)
        output = u.expand(output_shape).contiguous()
    else:
        output = torch.rand(output_shape, dtype=torch.float32, device=bins.device)

    # pyre-fixme[16]: Module `pytorch3d` has no attribute `_C`.
    _C.sample_pdf(
        bins.reshape(-1, n_bins + 1),
        weights.reshape(-1, n_bins),
        output.reshape(-1, n_samples),
        eps,
    )

    return output


def sample_pdf_python(
    bins: torch.Tensor,
    weights: torch.Tensor,
    N_samples: int,
    det: bool = False,
    eps: float = 1e-5,
) -> torch.Tensor:
    """
    This is a pure python implementation of the `sample_pdf` function.
    It may be faster than sample_pdf when the number of bins is very large,
    because it behaves as O(batchsize * [n_bins + log(n_bins) * n_samples] )
    whereas sample_pdf behaves as O(batchsize * n_bins * n_samples).
    For 64 bins sample_pdf is much faster.

    Samples probability density functions defined by bin edges `bins` and
    the non-negative per-bin probabilities `weights`.

    Note: This is a direct conversion of the TensorFlow function from the original
    release [1] to PyTorch. It requires PyTorch 1.6 or greater due to the use of
    torch.searchsorted.

    Args:
        bins: Tensor of shape `(..., n_bins+1)` denoting the edges of the sampling bins.
        weights: Tensor of shape `(..., n_bins)` containing non-negative numbers
            representing the probability of sampling the corresponding bin.
        N_samples: The number of samples to draw from each set of bins.
        det: If `False`, the sampling is random. `True` yields deterministic
            uniformly-spaced sampling from the inverse cumulative density function.
        eps: A constant preventing division by zero in case empty bins are present.

    Returns:
        samples: Tensor of shape `(..., N_samples)` containing `N_samples` samples
            drawn from each probability distribution.

    Refs:
        [1] https://github.com/bmild/nerf/blob/55d8b00244d7b5178f4d003526ab6667683c9da9/run_nerf_helpers.py#L183  # noqa E501
    """

    # Get pdf
    weights = weights + eps  # prevent nans
    if weights.min() <= 0:
        raise ValueError("Negative weights provided.")
    pdf = weights / weights.sum(dim=-1, keepdim=True)
    cdf = torch.cumsum(pdf, -1)
    cdf = torch.cat([torch.zeros_like(cdf[..., :1]), cdf], -1)

    # Take uniform samples u of shape (..., N_samples)
    if det:
        u = torch.linspace(0.0, 1.0, N_samples, device=cdf.device, dtype=cdf.dtype)
        u = u.expand(list(cdf.shape[:-1]) + [N_samples]).contiguous()
    else:
        u = torch.rand(
            list(cdf.shape[:-1]) + [N_samples], device=cdf.device, dtype=cdf.dtype
        )

    # Invert CDF
    inds = torch.searchsorted(cdf, u, right=True)
    # inds has shape (..., N_samples) identifying the bin of each sample.
    below = (inds - 1).clamp(0)
    above = inds.clamp(max=cdf.shape[-1] - 1)
    # Below and above are of shape (..., N_samples), identifying the bin
    # edges surrounding each sample.

    inds_g = torch.stack([below, above], -1).view(
        *below.shape[:-1], below.shape[-1] * 2
    )
    cdf_g = torch.gather(cdf, -1, inds_g).view(*below.shape, 2)
    bins_g = torch.gather(bins, -1, inds_g).view(*below.shape, 2)
    # cdf_g and bins_g are of shape (..., N_samples, 2) and identify
    # the cdf and the index of the two bin edges surrounding each sample.

    denom = cdf_g[..., 1] - cdf_g[..., 0]
    denom = torch.where(denom < eps, torch.ones_like(denom), denom)
    t = (u - cdf_g[..., 0]) / denom
    # t is of shape  (..., N_samples) and identifies how far through
    # each sample is in its bin.

    samples = bins_g[..., 0] + t * (bins_g[..., 1] - bins_g[..., 0])

    return samples