# mypy: allow-untyped-defs
from typing import Optional, Union

import torch
import torch.nn.functional as F
from torch import SymInt, Tensor
from torch._C import _add_docstr, _nested  # type: ignore[attr-defined]
from torch.types import _device as Device, _dtype as DType


__all__ = [
    "to_padded_tensor",
    "as_nested_tensor",
    "nested_tensor",
    "nested_tensor_from_jagged",
    "narrow",
    "masked_select",
]

# Allowlist these for weights_only load of NJT
from ._internal.nested_tensor import _rebuild_njt, NestedTensor as _NestedTensor


torch.serialization.add_safe_globals([_NestedTensor, _rebuild_njt])


def as_nested_tensor(
    ts: Union[Tensor, list[Tensor], tuple[Tensor, ...]],
    dtype: Optional[DType] = None,
    device: Optional[Device] = None,
    layout=None,
) -> Tensor:
    r"""
    Constructs a nested tensor preserving autograd history from a tensor or a list / tuple of
    tensors.

    If a nested tensor is passed, it will be returned directly unless the device / dtype / layout
    differ. Note that converting device / dtype will result in a copy, while converting layout
    is not currently supported by this function.

    If a non-nested tensor is passed, it is treated as a batch of constituents of consistent size.
    A copy will be incurred if the passed device / dtype differ from those of the input OR if
    the input is non-contiguous. Otherwise, the input's storage will be used directly.

    If a tensor list is provided, tensors in the list are always copied during construction of
    the nested tensor.

    Args:
        ts (Tensor or List[Tensor] or Tuple[Tensor]): a tensor to treat as a nested tensor OR a
            list / tuple of tensors with the same ndim

    Keyword arguments:
        dtype (:class:`torch.dtype`, optional): the desired type of returned nested tensor.
            Default: if None, same :class:`torch.dtype` as leftmost tensor in the list.
        device (:class:`torch.device`, optional): the desired device of returned nested tensor.
            Default: if None, same :class:`torch.device` as leftmost tensor in the list
        layout (:class:`torch.layout`, optional): the desired layout of returned nested tensor.
            Only strided and jagged layouts are supported. Default: if None, the strided layout.

    Example::

        >>> a = torch.arange(3, dtype=torch.float, requires_grad=True)
        >>> b = torch.arange(5, dtype=torch.float, requires_grad=True)
        >>> nt = torch.nested.as_nested_tensor([a, b])
        >>> nt.is_leaf
        False
        >>> fake_grad = torch.nested.nested_tensor([torch.ones_like(a), torch.zeros_like(b)])
        >>> nt.backward(fake_grad)
        >>> a.grad
        tensor([1., 1., 1.])
        >>> b.grad
        tensor([0., 0., 0., 0., 0.])
        >>> c = torch.randn(3, 5, requires_grad=True)
        >>> nt2 = torch.nested.as_nested_tensor(c)
    """
    is_tensor_list = isinstance(ts, (list, tuple)) and all(
        isinstance(t, Tensor) for t in ts
    )
    if not isinstance(ts, Tensor) and not is_tensor_list:
        raise TypeError(
            "as_nested_tensor(): Expected first argument to be a tensor or a list / tuple of tensors "
        )
    # convert tuple -> list if needed
    if is_tensor_list and not isinstance(ts, list):
        ts = list(ts)

    if isinstance(ts, Tensor) and ts.dim() < 2:
        raise RuntimeError(
            "as_nested_tensor(): Expected tensor argument to have dim() > 1"
        )

    if isinstance(ts, Tensor) and ts.is_nested:
        if layout == ts.layout:
            # return input directly or input copied to device / dtype
            return ts.to(device=device, dtype=dtype)
        else:
            # TODO: Just use nt.to(layout=layout) when it exists.
            raise RuntimeError(
                "as_nested_tensor(): Converting between nested tensor layouts is not supported"
            )

    if layout is None:
        layout = torch.strided
    if layout == torch.strided:
        if isinstance(ts, Tensor):
            # contiguous() might be necessary to get flattened view.
            # we could probably be more precise about when to do this as an optimization
            buffer = ts.contiguous().view(-1).to(device=device, dtype=dtype)
            nested_sizes = torch.tensor([t.shape for t in ts])
            return torch._nested_view_from_buffer(
                buffer,
                nested_sizes,
                *torch._nested_compute_contiguous_strides_offsets(nested_sizes),
            )
        else:
            assert isinstance(ts, list)
            return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)
    elif layout == torch.jagged:
        if isinstance(ts, Tensor):
            if device is None:
                device = ts.device

            # contiguous() might be necessary to get flattened view.
            # we could probably be more precise about when to do this as an optimization
            values = ts.contiguous().flatten(0, 1).to(device=device, dtype=dtype)
            batch_size = ts.shape[0]
            seq_len = ts.shape[1]
            offsets = torch.arange(
                0, batch_size * seq_len + 1, seq_len, device=device, dtype=torch.int64
            )

            from torch.nested._internal.nested_tensor import (
                nested_view_from_values_offsets,
            )

            return nested_view_from_values_offsets(
                values, offsets, min_seqlen=seq_len, max_seqlen=seq_len
            )
        else:
            from torch.nested._internal.nested_tensor import jagged_from_list

            assert isinstance(ts, list)
            nt, _ = jagged_from_list(ts, offsets=None, device=device, dtype=dtype)
            return nt
    else:
        raise RuntimeError(
            f"Specified layout is unsupported for nested tensors: {layout}"
        )


# Note: This not only adds doc strings for the nested ops, but
# also connects the torch.nested Python namespace to the torch._C._nested builtins.

to_padded_tensor = _add_docstr(
    _nested.nested_to_padded_tensor,
    r"""
to_padded_tensor(input, padding, output_size=None, out=None) -> Tensor

Returns a new (non-nested) Tensor by padding the :attr:`input` nested tensor.
The leading entries will be filled with the nested data,
while the trailing entries will be padded.

.. warning::

    :func:`to_padded_tensor` always copies the underlying data,
    since the nested and the non-nested tensors differ in memory layout.

Args:
    padding (float): The padding value for the trailing entries.

Keyword args:
    output_size (Tuple[int]): The size of the output tensor.
                              If given, it must be large enough to contain all nested data;
                              else, will infer by taking the max size of each nested sub-tensor along each dimension.
    out (Tensor, optional): the output tensor.

Example::

    >>> nt = torch.nested.nested_tensor([torch.randn((2, 5)), torch.randn((3, 4))])
    nested_tensor([
      tensor([[ 1.6862, -1.1282,  1.1031,  0.0464, -1.3276],
              [-1.9967, -1.0054,  1.8972,  0.9174, -1.4995]]),
      tensor([[-1.8546, -0.7194, -0.2918, -0.1846],
              [ 0.2773,  0.8793, -0.5183, -0.6447],
              [ 1.8009,  1.8468, -0.9832, -1.5272]])
    ])
    >>> pt_infer = torch.nested.to_padded_tensor(nt, 0.0)
    tensor([[[ 1.6862, -1.1282,  1.1031,  0.0464, -1.3276],
             [-1.9967, -1.0054,  1.8972,  0.9174, -1.4995],
             [ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000]],
            [[-1.8546, -0.7194, -0.2918, -0.1846,  0.0000],
             [ 0.2773,  0.8793, -0.5183, -0.6447,  0.0000],
             [ 1.8009,  1.8468, -0.9832, -1.5272,  0.0000]]])
    >>> pt_large = torch.nested.to_padded_tensor(nt, 1.0, (2, 4, 6))
    tensor([[[ 1.6862, -1.1282,  1.1031,  0.0464, -1.3276,  1.0000],
             [-1.9967, -1.0054,  1.8972,  0.9174, -1.4995,  1.0000],
             [ 1.0000,  1.0000,  1.0000,  1.0000,  1.0000,  1.0000],
             [ 1.0000,  1.0000,  1.0000,  1.0000,  1.0000,  1.0000]],
            [[-1.8546, -0.7194, -0.2918, -0.1846,  1.0000,  1.0000],
             [ 0.2773,  0.8793, -0.5183, -0.6447,  1.0000,  1.0000],
             [ 1.8009,  1.8468, -0.9832, -1.5272,  1.0000,  1.0000],
             [ 1.0000,  1.0000,  1.0000,  1.0000,  1.0000,  1.0000]]])
    >>> pt_small = torch.nested.to_padded_tensor(nt, 2.0, (2, 2, 2))
    RuntimeError: Value in output_size is less than NestedTensor padded size. Truncation is not supported.

""",
)


def nested_tensor(
    tensor_list,
    *,
    dtype=None,
    layout=None,
    device=None,
    requires_grad=False,
    pin_memory=False,
) -> Tensor:
    r"""
    Constructs a nested tensor with no autograd history (also known as a "leaf tensor", see
    :ref:`Autograd mechanics <autograd-mechanics>`) from :attr:`tensor_list` a list of tensors.

    Args:
        tensor_list (List[array_like]): a list of tensors, or anything that can be passed to torch.tensor,
        where each element of the list has the same dimensionality.

    Keyword arguments:
        dtype (:class:`torch.dtype`, optional): the desired type of returned nested tensor.
            Default: if None, same :class:`torch.dtype` as leftmost tensor in the list.
        layout (:class:`torch.layout`, optional): the desired layout of returned nested tensor.
            Only strided and jagged layouts are supported. Default: if None, the strided layout.
        device (:class:`torch.device`, optional): the desired device of returned nested tensor.
            Default: if None, same :class:`torch.device` as leftmost tensor in the list
        requires_grad (bool, optional): If autograd should record operations on the
            returned nested tensor. Default: ``False``.
        pin_memory (bool, optional): If set, returned nested tensor would be allocated in
            the pinned memory. Works only for CPU tensors. Default: ``False``.

    Example::

        >>> a = torch.arange(3, dtype=torch.float, requires_grad=True)
        >>> b = torch.arange(5, dtype=torch.float, requires_grad=True)
        >>> nt = torch.nested.nested_tensor([a, b], requires_grad=True)
        >>> nt.is_leaf
        True
    """
    if layout is None:
        layout = torch.strided
    if layout == torch.strided:
        return _nested.nested_tensor(
            tensor_list,
            dtype=dtype,
            device=device,
            requires_grad=requires_grad,
            pin_memory=pin_memory,
        )
    elif layout == torch.jagged:
        # Need to wrap lists of scalars as tensors
        list_of_tensors = [
            t if isinstance(t, Tensor) else torch.as_tensor(t) for t in tensor_list
        ]

        from torch.nested._internal.nested_tensor import jagged_from_list

        with torch.no_grad():
            nt, _ = jagged_from_list(
                list_of_tensors, offsets=None, device=device, dtype=dtype
            )

        nt.requires_grad_(requires_grad)
        if pin_memory:
            nt = nt.pin_memory()  # type: ignore[assignment]

        return nt
    else:
        raise RuntimeError(
            f"Specified layout is unsupported for nested tensors: {layout}"
        )


def narrow(
    tensor: Tensor,
    dim: int,
    start: Union[int, Tensor],
    length: Union[int, Tensor],
    layout=torch.strided,
) -> Tensor:
    r"""
    Constructs a nested tensor (which might be a view) from :attr:`tensor`, a strided tensor. This follows
    similar semantics to torch.Tensor.narrow, where in the :attr:`dim`-th dimension the new nested tensor
    shows only the elements in the interval `[start, start+length)`. As nested representations
    allow for a different `start` and `length` at each 'row' of that dimension, :attr:`start` and :attr:`length`
    can also be tensors of shape `tensor.shape[0]`.

    There's some differences depending on the layout you use for the nested tensor. If using strided layout,
    torch.narrow will do a copy of the narrowed data into a contiguous NT with strided layout, while
    jagged layout narrow() will create a non-contiguous view of your original strided tensor. This particular
    representation is really useful for representing kv-caches in Transformer models, as specialized
    SDPA kernels can deal with format easily, resulting in performance improvements.


    Args:
        tensor (:class:`torch.Tensor`): a strided tensor, which will be used as the underlying data
            for the nested tensor if using the jagged layout or will be copied for the strided layout.
        dim (int): the dimension where narrow will be applied. Only `dim=1` is supported for the
            jagged layout, while strided supports all dim
        start (Union[int, :class:`torch.Tensor`]): starting element for the narrow operation
        length (Union[int, :class:`torch.Tensor`]): number of elements taken during the narrow op

    Keyword arguments:
        layout (:class:`torch.layout`, optional): the desired layout of returned nested tensor.
            Only strided and jagged layouts are supported. Default: if None, the strided layout.

    Example::

        >>> starts = torch.tensor([0, 1, 2, 3, 4], dtype=torch.int64)
        >>> lengths = torch.tensor([3, 2, 2, 1, 5], dtype=torch.int64)
        >>> narrow_base = torch.randn(5, 10, 20)
        >>> nt_narrowed = torch.nested.narrow(narrow_base, 1, starts, lengths, layout=torch.jagged)
        >>> nt_narrowed.is_contiguous()
        False
    """
    if not isinstance(start, (int, SymInt, Tensor)):
        raise RuntimeError("start must be an integer or a tensor")

    if not isinstance(length, (int, SymInt, Tensor)):
        raise RuntimeError("length must be an integer or a tensor")

    if layout == torch.strided:
        if isinstance(start, Tensor) or isinstance(length, Tensor):
            raise RuntimeError(
                "start and length must be integers for the strided layout NT impl"
            )
        # TODO: switch to as_nested_tensor(tensor) when it is available
        nt = as_nested_tensor(torch.unbind(tensor), layout=torch.strided).narrow(
            dim, start, length
        )
    elif layout == torch.jagged:
        if dim != 1:
            raise RuntimeError("jagged layout only supports dim=1")

        from torch.nested._internal.nested_tensor import jagged_from_tensor_and_lengths

        if isinstance(start, (int, SymInt)):
            start = torch.tensor([start], device=tensor.device, dtype=torch.int64)

        if isinstance(length, (int, SymInt)):
            length = torch.tensor([length], device=tensor.device, dtype=torch.int64)

        nt, _, _ = jagged_from_tensor_and_lengths(tensor, start, length)
    else:
        raise RuntimeError(
            f"Specified layout is unsupported for nested narrow: {layout}"
        )

    return nt


def nested_tensor_from_jagged(
    values: Tensor,
    offsets: Optional[Tensor] = None,
    lengths: Optional[Tensor] = None,
    jagged_dim: Optional[int] = None,
    min_seqlen: Optional[int] = None,
    max_seqlen: Optional[int] = None,
) -> Tensor:
    r"""
    Constructs a jagged layout nested tensor from the given jagged components. The jagged layout
    consists of a required values buffer with the jagged dimension packed into a single dimension.
    The offsets / lengths metadata determines how this dimension is split into batch elements
    and are expected to be allocated on the same device as the values buffer.

    Expected metadata formats:
        * offsets: Indices within the packed dimension splitting it into heterogeneously-sized
          batch elements. Example: [0, 2, 3, 6] indicates that a packed jagged dim of size 6
          should be conceptually split into batch elements of length [2, 1, 3]. Note that both the
          beginning and ending offsets are required for kernel convenience (i.e. shape batch_size + 1).
        * lengths: Lengths of the individual batch elements; shape == batch_size. Example: [2, 1, 3]
          indicates that a packed jagged dim of size 6 should be conceptually split into batch
          elements of length [2, 1, 3].

    Note that it can be useful to provide both offsets and lengths. This describes a nested tensor
    with "holes", where the offsets indicate the start position of each batch item and the length
    specifies the total number of elements (see example below).

    The returned jagged layout nested tensor will be a view of the input values tensor.

    Args:
        values (:class:`torch.Tensor`): The underlying buffer in the shape of
            (sum_B(*), D_1, ..., D_N). The jagged dimension is packed into a single dimension,
            with the offsets / lengths metadata used to distinguish batch elements.
        offsets (optional :class:`torch.Tensor`): Offsets into the jagged dimension of shape B + 1.
        lengths (optional :class:`torch.Tensor`): Lengths of the batch elements of shape B.
        jagged_dim (optional int): Indicates which dimension in values is the packed jagged
            dimension. If None, this is set to dim=1 (i.e. the dimension immediately following
            the batch dimension). Default: None
        min_seqlen (optional int): If set, uses the specified value as the cached minimum sequence
            length for the returned nested tensor. This can be a useful alternative to computing
            this value on-demand, possibly avoiding a GPU -> CPU sync. Default: None
        max_seqlen (optional int): If set, uses the specified value as the cached maximum sequence
            length for the returned nested tensor. This can be a useful alternative to computing
            this value on-demand, possibly avoiding a GPU -> CPU sync. Default: None

    Example::

        >>> values = torch.randn(12, 5)
        >>> offsets = torch.tensor([0, 3, 5, 6, 10, 12])
        >>> nt = nested_tensor_from_jagged(values, offsets)
        >>> # 3D shape with the middle dimension jagged
        >>> nt.shape
        torch.Size([5, j2, 5])
        >>> # Length of each item in the batch:
        >>> offsets.diff()
        tensor([3, 2, 1, 4, 2])

        >>> values = torch.randn(6, 5)
        >>> offsets = torch.tensor([0, 2, 3, 6])
        >>> lengths = torch.tensor([1, 1, 2])
        >>> # NT with holes
        >>> nt = nested_tensor_from_jagged(values, offsets, lengths)
        >>> a, b, c = nt.unbind()
        >>> # Batch item 1 consists of indices [0, 1)
        >>> torch.equal(a, values[0:1, :])
        True
        >>> # Batch item 2 consists of indices [2, 3)
        >>> torch.equal(b, values[2:3, :])
        True
        >>> # Batch item 3 consists of indices [3, 5)
        >>> torch.equal(c, values[3:5, :])
        True
    """
    from torch.fx._symbolic_trace import is_fx_tracing

    if is_fx_tracing():
        raise RuntimeError(
            "torch.nested.nested_tensor_from_jagged does not support tracing with fx.symbolic_trace. "
            "Use fx.wrap to wrap the function that calls nested_tensor_from_jagged."
        )

    if offsets is None:
        if lengths is None:
            raise RuntimeError(
                "nested_tensor_from_jagged(): At least one of offsets or lengths is required."
            )
        else:
            # TODO: Truly support offsets=None at some point?
            # For now, just convert lengths -> offsets for kernel convenience
            offsets = F.pad(lengths.cumsum(0), (1, 0))
            lengths = None

    if jagged_dim is None:
        jagged_dim = 1

    from torch.nested._internal.nested_tensor import (
        nested_view_from_values_offsets_lengths,
    )

    return nested_view_from_values_offsets_lengths(
        values,
        offsets,
        lengths,
        ragged_idx=jagged_dim,
        min_seqlen=min_seqlen,
        max_seqlen=max_seqlen,
    )


def masked_select(tensor: Tensor, mask: Tensor) -> Tensor:
    r"""
    Constructs a nested tensor given a strided tensor input and a strided mask, the resulting jagged layout nested tensor
    will have values retain values where the mask is equal to True. The dimensionality of the mask is preserved and is
    represented with the offsets, this is unlike :func:`masked_select` where the output is collapsed to a 1D tensor.

    Args:
    tensor (:class:`torch.Tensor`): a strided tensor from which the jagged layout nested tensor is constructed from.
    mask (:class:`torch.Tensor`): a strided mask tensor which is applied to the tensor input

    Example::

        >>> tensor = torch.randn(3, 3)
        >>> mask = torch.tensor([[False, False, True], [True, False, True], [False, False, True]])
        >>> nt = torch.nested.masked_select(tensor, mask)
        >>> nt.shape
        torch.Size([3, j4])
        >>> # Length of each item in the batch:
        >>> nt.offsets().diff()
        tensor([1, 2, 1])

        >>> tensor = torch.randn(6, 5)
        >>> mask = torch.tensor([False])
        >>> nt = torch.nested.masked_select(tensor, mask)
        >>> nt.shape
        torch.Size([6, j5])
        >>> # Length of each item in the batch:
        >>> nt.offsets().diff()
        tensor([0, 0, 0, 0, 0, 0])
    """
    if tensor.layout != torch.strided:
        raise RuntimeError(
            f"torch.nested.masked_select requires a strided tensor, given {tensor.layout}"
        )

    if mask.layout != torch.strided:
        raise RuntimeError(
            f"torch.nested.masked_select requires a strided mask, given: {mask.layout}"
        )
    res_values = tensor.masked_select(mask)
    expanded_mask = mask.expand(tensor.shape)
    res_lengths = expanded_mask.sum(dim=tensor.ndim - 1).view(-1)

    from torch.nested._internal.nested_tensor import nested_view_from_values_offsets

    return nested_view_from_values_offsets(
        values=res_values,
        offsets=F.pad(res_lengths.cumsum(dim=0), (1, 0)),
    )
