# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

from typing import Dict, Iterable, List, Tuple

import torch


_MISSING: torch.Tensor = object()  # type: ignore[assignment]


def set_tensor(module: "torch.nn.Module", name: str, tensor: torch.Tensor) -> None:
    if not isinstance(module, torch.nn.Module):
        raise TypeError(f"{module} is not an instance of torch.nn.Module")
    if not isinstance(tensor, torch.Tensor) and tensor is not None:
        raise TypeError(f"{tensor} is not an instance of torch.Tensor")
    if "." in name:
        raise KeyError('tensor name can\'t contain "."')
    if name == "":
        raise KeyError('tensor name can\'t be empty string ""')
    if name in module._parameters:
        module._parameters[name] = tensor  # type: ignore[assignment]
    elif name in module._buffers:
        module._buffers[name] = tensor
    else:
        setattr(module, name, tensor)


def swap_tensor(
    module: "torch.nn.Module",
    name: str,
    tensor: torch.Tensor,
    allow_missing: bool = False,
) -> torch.Tensor:
    if not isinstance(module, torch.nn.Module):
        raise TypeError(f"{module} is not an instance of torch.nn.Module")
    if (
        tensor is not _MISSING
        and not isinstance(tensor, torch.Tensor)
        and tensor is not None
    ):
        raise TypeError(f"{tensor} is not an instance of torch.Tensor")
    if "." in name:
        raise KeyError('tensor name can\'t contain "."')
    if name == "":
        raise KeyError('tensor name can\'t be empty string ""')

    orig_tensor: torch.Tensor
    if name in module._parameters:
        orig_tensor = module._parameters[name]  # type: ignore[assignment]
        if tensor is not _MISSING:
            module._parameters[name] = tensor  # type: ignore[assignment]
        else:
            del module._parameters[name]
    elif name in module._buffers:
        orig_tensor = module._buffers[name]  # type: ignore[assignment]
        if tensor is not _MISSING:
            module._buffers[name] = tensor
        else:
            del module._buffers[name]
    else:
        try:
            orig_tensor = getattr(module, name)
        except AttributeError as ex:
            if not allow_missing:
                raise AttributeError(
                    f"{module._get_name()} has no attribute `{name}`"
                ) from ex
            orig_tensor = _MISSING
        if (
            orig_tensor is not _MISSING
            and not isinstance(orig_tensor, torch.Tensor)
            and orig_tensor is not None
        ):
            raise TypeError(
                f"attribute `{name}`: {orig_tensor} is not an instance of torch.Tensor"
            )
        if tensor is not _MISSING:
            setattr(module, name, tensor)
        elif hasattr(module, name):
            delattr(module, name)
    return orig_tensor


class NamedMemberAccessor:
    """
    A class that provides a way to access the submodules and parameters/buffers
    of a module. It provides caching mechanism to speed up submodule lookups.
    This is useful for functional programming to manipulate the module state.
    """

    def __init__(self, module: "torch.nn.Module") -> None:
        self.module = module
        self.memo: Dict[str, torch.nn.Module] = {}

    # Nested attribute access

    def get_submodule(self, name: str) -> "torch.nn.Module":
        """
        Return the submodule specified by the given path.
        For example, to get the submodule mod.layer1.conv1,
        use accessor.get_submodule("layer1.conv1")

        Compare to mod.get_submodule("layer1.conv1"), this method will cache the
        intermediate submodule access to speed up future lookups.
        """
        if not name:
            return self.module

        try:
            return self.memo[name]
        except KeyError:
            prefix, dot, attr = name.rpartition(".")
            if dot:
                module = self.get_submodule(prefix)
            else:
                module = self.module
            try:
                submodule = getattr(module, attr)
            except AttributeError as ex:
                raise AttributeError(
                    f"{module._get_name()} has no attribute `{attr}`"
                ) from ex
            if not isinstance(submodule, torch.nn.Module):
                raise TypeError(
                    f"submodule `{name}`: {submodule} is not an instance of torch.nn.Module"
                )
            self.memo[name] = submodule
            return submodule

    def get_tensor(self, name: str) -> torch.Tensor:
        """
        Get the tensor specified by the given path to value.
        For example, to get the attribute mod.layer1.conv1.weight,
        use accessor.get_tensor('layer1.conv1.weight')

        Compare to mod.get_parameter("layer1.conv1.weight"), this method will
        cache the intermediate submodule access to speed up future lookups.
        """
        prefix, _, attr = name.rpartition(".")
        submodule = self.get_submodule(prefix)
        try:
            tensor = getattr(submodule, attr)
        except AttributeError as ex:
            raise AttributeError(
                f"{submodule._get_name()} has no attribute `{name}`"
            ) from ex
        if not isinstance(tensor, torch.Tensor) and tensor is not None:
            raise TypeError(f"{tensor} is not an instance of torch.Tensor")
        return tensor  # type: ignore[return-value]

    def set_tensor(self, name: str, value: torch.Tensor) -> None:
        """
        Set the attribute specified by the given path to value.
        For example, to set the attribute mod.layer1.conv1.weight,
        use accessor.set_tensor("layer1.conv1.weight", value)
        """
        prefix, _, attr = name.rpartition(".")
        set_tensor(self.get_submodule(prefix), attr, value)

    def del_tensor(self, name: str) -> None:
        """
        Delete the attribute specified by the given path.
        For example, to delete the attribute mod.layer1.conv1.weight,
        use accessor.del_tensor("layer1.conv1.weight")
        """
        prefix, _, attr = name.rpartition(".")
        submodule = self.get_submodule(prefix)
        try:
            delattr(submodule, attr)
        except AttributeError as ex:
            raise AttributeError(
                f"{submodule._get_name()} has no attribute `{name}`"
            ) from ex

    def swap_tensor(
        self, name: str, value: torch.Tensor, allow_missing: bool = False
    ) -> torch.Tensor:
        """
        Swap the attribute specified by the given path to value.
        For example, to swap the attribute mod.layer1.conv1.weight,
        use accessor.swap_tensor("layer1.conv1.weight", value)
        """
        prefix, _, attr = name.rpartition(".")
        return swap_tensor(
            self.get_submodule(prefix), attr, value, allow_missing=allow_missing
        )

    # Batched operations

    def get_tensors(self, names: Iterable[str]) -> List[torch.Tensor]:
        """
        Get the tensors specified by the given paths.
        For example, to get the attributes mod.layer1.conv1.weight and
        mod.layer1.conv1.bias, use accessor.get_tensors(["layer1.conv1.weight",
        "layer1.conv1.bias"])
        """
        return [self.get_tensor(name) for name in names]

    def set_tensors(self, names: Iterable[str], values: Iterable[torch.Tensor]) -> None:
        """
        Set the attributes specified by the given paths to values.
        For example, to set the attributes mod.layer1.conv1.weight and
        mod.layer1.conv1.bias, use accessor.set_tensors(["layer1.conv1.weight",
        "layer1.conv1.bias"], [weight, bias])
        """
        if not isinstance(names, (list, tuple)):
            names = list(names)
        if not isinstance(values, (list, tuple)):
            values = list(values)
        assert len(names) == len(values), "names and values must have the same length"

        for name, value in zip(names, values):
            self.set_tensor(name, value)

    def set_tensors_dict(self, named_tensors: Dict[str, torch.Tensor]) -> None:
        """
        Set the attributes specified by the given paths to values.
        For example, to set the attributes mod.layer1.conv1.weight and
        mod.layer1.conv1.bias, use accessor.set_tensors_dict({
            "layer1.conv1.weight": weight,
            "layer1.conv1.bias": bias,
        })
        """
        for name, value in named_tensors.items():
            self.set_tensor(name, value)

    def del_tensors(self, names: Iterable[str]) -> None:
        """
        Delete the attributes specified by the given paths.
        For example, to delete the attributes mod.layer1.conv1.weight and
        mod.layer1.conv1.bias, use accessor.del_tensors(["layer1.conv1.weight",
        "layer1.conv1.bias"])
        """
        for name in names:
            self.del_tensor(name)

    def swap_tensors(
        self,
        names: Iterable[str],
        values: Iterable[torch.Tensor],
        allow_missing: bool = False,
    ) -> List[torch.Tensor]:
        """
        Swap the attributes specified by the given paths to values.
        For example, to swap the attributes mod.layer1.conv1.weight and
        mod.layer1.conv1.bias, use accessor.swap_tensors(["layer1.conv1.weight",
        "layer1.conv1.bias"], [weight, bias])
        """
        if not isinstance(names, (list, tuple)):
            names = list(names)
        if not isinstance(values, (list, tuple)):
            values = list(values)
        assert len(names) == len(values), "names and values must have the same length"

        return [
            self.swap_tensor(name, value, allow_missing=allow_missing)
            for name, value in zip(names, values)
        ]

    def swap_tensors_dict(
        self, named_tensors: Dict[str, torch.Tensor], allow_missing: bool = False
    ) -> Tuple[Dict[str, torch.Tensor], List[str]]:
        """
        Swap the attributes specified by the given paths to values.
        For example, to swap the attributes mod.layer1.conv1.weight and
        mod.layer1.conv1.bias, use accessor.swap_tensors_dict({
            "layer1.conv1.weight": weight,
            "layer1.conv1.bias": bias,
        })
        """
        orig_named_tensors = {}
        missing_keys = []
        try:
            for name, tensor in named_tensors.items():
                orig_tensor = self.swap_tensor(name, tensor, allow_missing=True)
                if orig_tensor is _MISSING:
                    missing_keys.append(name)
                orig_named_tensors[name] = orig_tensor
        except Exception:
            # Swap back if any exception occurs
            for name, orig_tensor in orig_named_tensors.items():
                self.swap_tensor(name, orig_tensor, allow_missing=True)
            raise
        if missing_keys and not allow_missing:
            # Swap back if any key is missing when allow_missing is False
            for name, orig_tensor in orig_named_tensors.items():
                self.swap_tensor(name, orig_tensor, allow_missing=True)
            raise RuntimeError(
                "Missing key(s): {}.".format(", ".join(map(repr, missing_keys)))
            )
        return orig_named_tensors, missing_keys

    def check_keys(self, keys: Iterable[str]) -> Tuple[List[str], List[str]]:
        """
        Check that the given keys are valid.
        """
        keys = set(keys)
        valid_keys = {name for name, _ in self.named_tensors(remove_duplicate=False)}
        missing_keys = valid_keys - keys
        unexpected_keys = keys - valid_keys
        return sorted(missing_keys), sorted(unexpected_keys)

    # Shortcut methods

    def named_parameters(
        self,
        remove_duplicate: bool = True,
    ) -> Iterable[Tuple[str, torch.Tensor]]:
        """
        Iterate over all the parameters in the module.
        """
        yield from self.module.named_parameters(remove_duplicate=remove_duplicate)

    def named_buffers(
        self,
        remove_duplicate: bool = True,
    ) -> Iterable[Tuple[str, torch.Tensor]]:
        """
        Iterate over all the buffers in the module.
        """
        yield from self.module.named_buffers(remove_duplicate=remove_duplicate)

    def named_tensors(
        self,
        remove_duplicate: bool = True,
    ) -> Iterable[Tuple[str, torch.Tensor]]:
        """
        Iterate over all the tensors in the module.
        """
        yield from self.module.named_parameters(remove_duplicate=remove_duplicate)
        yield from self.module.named_buffers(remove_duplicate=remove_duplicate)

    def named_modules(
        self,
        remove_duplicate: bool = True,
    ) -> Iterable[Tuple[str, "torch.nn.Module"]]:
        """
        Iterate over all the modules in the module.
        """
        yield from self.module.named_modules(remove_duplicate=remove_duplicate)
