from types import MethodType
from typing import Callable, Dict, Optional, Union

import torch
import torch.distributed as dist
import torch.nn as nn
from torch import Tensor
from torch.nn import Parameter
from torch.utils._pytree import tree_map

from colossalai._analyzer._subclasses import MetaTensor
from colossalai.device.device_mesh import DeviceMesh
from colossalai.tensor.d_tensor import distribute_tensor
from colossalai.tensor.d_tensor.sharding_spec import ShardingSpec

# reference: https://pytorch.org/cppdocs/notes/tensor_creation.html
_NORMAL_FACTORY = [
    "arange",
    "full",
    "empty",
    "linspace",
    "logspace",
    "ones",
    "rand",
    "randn",
    "randint",
    "randperm",
    "zeros",
    "tensor",
]

# factory function that does not support meta tensor backend
_NO_META_FACTORY = [
    "eye",
]

_EARLY_MATERIALIZED_OPS = ["__getitem__", "split"]

# If your intent is to change the metadata of a Tensor (such as sizes / strides / storage / storage_offset)
# without autograd tracking the change, remove the .data / .detach() call and wrap the change in a `with torch.no_grad():` block.
# These ops cannot be unwrapped using .data
_CHANGE_META_OPS = ["_cudnn_rnn_flatten_weight", "requires_grad_", "__get__", "__set__", "numel", "size", "dim"]

_LEGACY_TENSOR_CONSTRUCTOR = {
    "FloatTensor": torch.float,
    "DoubleTensor": torch.double,
    "HalfTensor": torch.half,
    "BFloat16Tensor": torch.bfloat16,
    "ByteTensor": torch.uint8,
    "CharTensor": torch.int8,
    "ShortTensor": torch.short,
    "IntTensor": torch.int,
    "LongTensor": torch.long,
    "BoolTensor": torch.bool,
}

_EMPTY_DATA = torch.empty(0)


class _MyTensor(Tensor):
    """This class is only for correctness verification."""

    _pre_op_fn: Callable[["LazyTensor"], None] = lambda *args: None

    default_device: Optional[torch.device] = None

    def __new__(cls, func, *args, concrete_data=None, **kwargs) -> "_MyTensor":
        cls._pre_op_fn()
        if concrete_data is not None:
            # uniform api as LazyTensor
            data = concrete_data
        else:
            kwargs["device"] = cls.default_device
            data = func(*args, **kwargs)
        return Tensor._make_subclass(cls, data, require_grad=data.requires_grad)

    @classmethod
    def __torch_function__(cls, func, types, args=(), kwargs=None):
        cls._pre_op_fn()
        return super().__torch_function__(func, types, args, kwargs)


def _data_tolist(tensor: torch.Tensor) -> list:
    """tolist() method is not allowed for a subclass of tensor. Tensor.data returns a Tensor."""
    return tensor.data.tolist()


def _convert_cls(tensor: "LazyTensor", target: torch.Tensor) -> torch.Tensor:
    """Convert a lazy tensor's class to target's class, with target's data.

    The reason why we change the class of a lazy tensor in-place is that this can easily handle shared modules/parameters, which is common in huggingface models.
    If we create a new tensor and update the module by ``setattr(module, name, param)``, the shared parameters will not be updated. And we have to track all shared parameters and update them manually.

    Args:
        tensor (LazyTensor): the LazyTensor to be converted
        target (torch.Tensor): target tensor

    Returns:
        torch.Tensor: the converted tensor
    """
    cls_to_become = Parameter if isinstance(tensor, Parameter) else torch.Tensor
    tensor.__class__ = cls_to_become
    if cls_to_become is Parameter:
        # to fit UninitializedParameter
        delattr(tensor, "_is_param")
    tensor.data = target
    tensor.requires_grad = target.requires_grad
    # subclass of torch.Tensor does not have tolist() method
    # overwrite this method after materialization or distribution
    tensor.tolist = MethodType(_data_tolist, tensor)
    return tensor


class LazyTensor(torch.Tensor):
    """A naive implementation of LazyTensor (https://arxiv.org/pdf/2102.13267.pdf).

    Usage:
        1. Use ``LazyTensor`` instead of ``torch.Tensor``.
        >>> x = LazyTensor(torch.zeros, 2, 3)
        >>> x += 1
        >>> y = x * x
        >>> y = y.cuda().half()
        >>> y[0, 0] = 0
        >>> y = y.materialize()     # materialize the tensor
        >>> print(y)
        tensor([[0., 1., 1.],
                [1., 1., 1.]], device='cuda:0', dtype=torch.float16)

    Warnings:
        1. Cases that ``LazyTensor`` can't deal with.
        >>> x = LazyTensor(torch.ones, 2, 3)
        >>> x[0, 0] = -x[0, 0]    # this will cause infinite recursion
        >>> y = x.clone()
        >>> x.add_(1) # modifying origin tensor after cloning leads to wrong materialization
        >>> z = x.tolist()
        >>> x.zeros_() # modifying origin tensor after cloning tolist is not allowed
        >>> nn.utils.weight_norm(self.conv, name="weight", dim=2) # applying weight norm on a lazy tensor is not allowed


        2. Cases that ``LazyTensor`` becomes eager (early materialization).
        >>> b = a[:, 2:]  # get a slice of a lazy tensor triggers early materialization
        >>> chunks = a.split(3)  # this also triggers early materialization
        >>> x.data = torch.rand(2, 3) # directly setting data of a lazy tensor triggers early materialization

    """

    _repr = True
    _meta_data: Optional[MetaTensor] = None  # shape, dtype, device
    _pre_op_fn: Callable[["LazyTensor"], None] = lambda *args: None

    default_device: Optional[torch.device] = None

    @staticmethod
    def __new__(cls, func, *args, meta_data=None, concrete_data=None, **kwargs):
        if concrete_data is not None:
            # some ops don't support meta backend and should have concrete data
            elem = concrete_data
        else:
            if meta_data is None:
                device = kwargs.get("device", "cpu")
                elem = func(*args, **{**kwargs, "device": "meta"})
                meta_data = MetaTensor(elem, device=device)
            elem = meta_data._tensor
        # As a meta tensor cannot be modified __class__ to torch.Tensor, we should use an empty real tensor here
        r = torch.Tensor._make_subclass(cls, _EMPTY_DATA, require_grad=elem.requires_grad)
        r._meta_data = meta_data
        return r

    def __init__(self, func, *args, meta_data=None, concrete_data=None, **kwargs):
        if func.__name__ in _NORMAL_FACTORY:
            kwargs = {**kwargs, "device": LazyTensor.default_device}
        self._factory_method = (func, args, kwargs)  # (func, args, kwargs)
        self._op_buffer = []  # (func, args, kwargs, replace)
        self._materialized_data: Optional[torch.Tensor] = concrete_data  # materialized data

    def materialize(self) -> torch.Tensor:
        """Materialize the ``LazyTensor`` to ``torch.Tensor`` by modifying __class__ (inplace).

        Returns:
            torch.Tensor: The materialized tensor (self).
        """
        target = self._materialize_data()
        self.clean()
        return _convert_cls(self, target)

    def distribute(self, device_mesh: DeviceMesh, sharding_spec: ShardingSpec) -> torch.Tensor:
        """Distribute the ``LazyTensor`` to ``torch.Tensor`` by modifying __class__ (inplace), according to the layout.

        Args:
            layout (Layout): Distribution layout.

        Returns:
            torch.Tensor: The distributed tensor (self).
        """
        target = self._materialize_data()
        self.clean()
        local_tensor = distribute_tensor(target, device_mesh, sharding_spec)
        return _convert_cls(self, local_tensor)

    def clean(self) -> None:
        """Clean all stored operations, meta data and materialized data, which prevents memory leaking. This should be called after all tensors are materialized."""
        delattr(self, "_factory_method")
        delattr(self, "_op_buffer")
        delattr(self, "_materialized_data")
        delattr(self, "_meta_data")

    @staticmethod
    def _replace_with_materialized(x):
        if isinstance(x, LazyTensor):
            return x._materialize_data()
        return x

    def _materialize_data(self) -> torch.Tensor:
        # self._materialized_data should be generated after the first call of this function
        if self._materialized_data is None:
            # apply factory method
            func, args, kwargs = self._factory_method
            # apply cached sequence
            self._pre_op_fn()

            init_val = func(
                *tree_map(self._replace_with_materialized, args), **tree_map(self._replace_with_materialized, kwargs)
            )

            self._materialized_data = self._rerun_ops(init_val)
        return self._materialized_data

    def _rerun_ops(self, target=None) -> torch.Tensor:
        """Do lazy execution by rerunning all (stored) related operations.

        Args:
            target (torc.Tensor, optional): Intial value of the target tensor (self). Defaults to None.
        """

        def replace(x):
            if x is self:
                return target
            elif isinstance(x, LazyTensor):
                return x._materialize_data()
            return x

        packed = None

        for func, args, kwargs in self._op_buffer:
            if func == torch.Tensor.requires_grad_:
                packed = func, args, kwargs  # requires grad should be set at last
            else:
                self._pre_op_fn()
                o = func(*tree_map(replace, args), **tree_map(replace, kwargs))
                target = o if isinstance(o, torch.Tensor) else target  # if func returns non-Tensor, discard the value

        # super-dainiu: set requires_grad after all inplace-ops are done
        if packed is not None:
            func, args, kwargs = packed
            func(*tree_map(replace, args), **tree_map(replace, kwargs))

        return target

    # cache everything with __torch_function__

    @classmethod
    def __torch_function__(cls, func, types, args=(), kwargs=None):
        if kwargs is None:
            kwargs = {}
        if func.__name__ in _EARLY_MATERIALIZED_OPS:
            # These OPs cannot be lazy and related tensors should be early materialized
            tree_map(cls._replace_with_materialized, args)
            tree_map(cls._replace_with_materialized, kwargs)
        is_inplace: bool = (
            func.__name__.endswith("_")
            and not (func.__name__.endswith("__"))
            or func.__name__ in ("__setitem__", "__set__")
        )

        is_change_meta_op: bool = func.__name__ in _CHANGE_META_OPS

        if isinstance(func, torch._C.ScriptMethod):
            # FIXME(ver217): torch script functions are not verified

            target = None

            def unwrap(x):
                if isinstance(x, LazyTensor):
                    return x._meta_data
                return x

            target: LazyTensor = args[0].clone()
            target._op_buffer.append((func, args, kwargs))
            target._meta_data = getattr(target._meta_data, func.name)(
                *tree_map(unwrap, args[1:]), **tree_map(unwrap, kwargs)
            )
            return target
        else:
            meta_to_lazy = {}

            def unwrap(x):
                if isinstance(x, LazyTensor):
                    if x._materialized_data is not None:
                        # for early materialized tensor, use its materialized data directly
                        return x._materialized_data if is_change_meta_op else x._materialized_data.data
                    t = x if is_inplace else x.clone()
                    t._op_buffer.append((func, args, kwargs))
                    meta = x._meta_data if is_change_meta_op else x._meta_data.data
                    meta_to_lazy[meta] = t
                    return meta
                return x

            def wrap(y, i=None):
                if isinstance(y, MetaTensor):
                    if y in meta_to_lazy:
                        # inplace op, just return origin lazy tensor
                        return meta_to_lazy[y]
                    else:
                        # out of place op, create new lazy tensor
                        fn = lambda *a, **kw: func(*a, **kw) if i is None else func(*a, **kw)[i]
                        fn.__name__ = func.__name__
                        lazy_y = LazyTensor(fn, *args, meta_data=y, **kwargs)
                        return lazy_y
                elif type(y) is Tensor:
                    # for early materialized tensor
                    return LazyTensor(lambda: None, concrete_data=y)
                return y

            cls._pre_op_fn()
            o = func(*tree_map(unwrap, args), **tree_map(unwrap, kwargs))
            if isinstance(o, (tuple, list)):
                return type(o)(wrap(y, i=i) for i, y in enumerate(o))
            return wrap(o)

    @classmethod
    def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
        pass  # skip

    def clone(self) -> "LazyTensor":
        def factory_fn():
            # if self is materialized, return self
            new_tensor = self.materialize() if type(self) is LazyTensor else self
            return new_tensor.clone()

        target = LazyTensor(factory_fn, meta_data=self._meta_data)

        return target

    def detach(self) -> Tensor:
        return self

    def __deepcopy__(self, memo):
        if not self.is_leaf:
            raise RuntimeError(
                "Only Tensors created explicitly by the user "
                "(graph leaves) support the deepcopy protocol at the moment"
            )
        if id(self) in memo:
            return memo[id(self)]

        def factory_fn():
            # if self is materialized, return self
            new_tensor = self.materialize() if type(self) is LazyTensor else self
            return _copy_tensor(new_tensor, new_tensor.requires_grad)

        if self._materialized_data is not None:
            # self is early materialized
            copied = _copy_tensor(self._materialized_data, self.requires_grad)
            target = LazyTensor(lambda: None, concrete_data=copied)
        else:
            target = LazyTensor(factory_fn, meta_data=self._meta_data)

        if isinstance(self, Parameter):
            # hack isinstance check of parameter
            target._is_param = True

        memo[id(self)] = target
        return target

    @property
    def data(self):
        return self

    @data.setter
    def data(self, other: "LazyTensor"):
        """This is sightly different from oringinal `data` setter.

        E.g.:
            >>> a = torch.randn(3, 3) # a is a Tensor
            >>> b = torch.rand(2, 2)
            >>> a.data = b
            >>> b.add_(1)   # this will affect a
            >>> x = torch.randn(3, 3) # x is a LazyTensor
            >>> y = torch.rand(2, 2) # y is a LazyTensor
            >>> x.data = y
            >>> y.add_(1)   # this will not affect x

        """
        if other is self:
            return

        self._op_buffer.append(other._factory_method)

        def replace(x):
            if x is other:
                return self
            return x

        for func, args, kwargs in other._op_buffer:
            self._op_buffer.append((func, tree_map(replace, args), tree_map(replace, kwargs)))

    def tolist(self) -> list:
        # Though self.__class__ is modified to torch.Tensor, in C++ side, it is still a subclass of torch.Tensor
        # And subclass of torch.Tensor does not have tolist() method
        t = self._materialize_data()
        return t.tolist()

    def __hash__(self):
        return id(self)

    def __rpow__(self, other):
        dtype = torch.result_type(self, other)
        return torch.tensor(other, dtype=dtype, device=self.device) ** self


class LazyInitContext:
    """Context manager for lazy initialization. Enables initializing the model without allocating real memory.

    Usage:
        1. The model is initialized, but no real memory is allocated.
        >>> ctx = LazyInitContext()
        >>> with ctx:
        >>>     model = MyModel().cuda()

        2. The model is initialized with ``MetaTensor`` as weights, but still no real memory is allocated.
        >>> with ctx.traceable(model):
        >>>     gm = symbolic_trace(model, meta_args=meta_args)
        >>> # Solve the execution strategy and apply the strategy to the model
        >>> strategy = StrategyAndSpec()

        3. The model is initialized with ``torch.Tensor`` as weights, and real memory is allocated. (single device)
        >>> model = ctx.materialize(model)

        3. The model is initialized with sharded ``torch.Tensor`` as weights, and real memory is allocated. (distributed scenario)
        >>> model = apply_strategy_to_all_params(model, strategy)
        >>> model = ctx.distribute(model)

    Warnings:
        This API is still experimental and further modifications can be made to it.
        For example:
            1. Quantization strategies can be applied before allocating real memory.
            2. Lazy initialization seems slower than normal initialization.
    """

    _replaced: bool = False

    def __init__(
        self,
        tensor_cls: Union[_MyTensor, LazyTensor] = LazyTensor,
        default_device: Optional[Union[torch.device, str, int]] = None,
    ):
        assert tensor_cls is LazyTensor or tensor_cls is _MyTensor
        self.overrides = {}
        self.tensor_cls = tensor_cls
        self.old_default_device = LazyTensor.default_device
        self.default_device = default_device

    def __enter__(self):
        if LazyInitContext._replaced:
            raise RuntimeError(f"LazyInitContext is not reentrant")
        LazyInitContext._replaced = True
        self.old_default_device = self.tensor_cls.default_device
        self.tensor_cls.default_device = self.default_device

        def wrap_factory_method(target):
            # factory functions (eg. torch.empty())
            def wrapper(*args, **kwargs):
                return self.tensor_cls(target, *args, **kwargs)

            return wrapper, target

        def wrap_factory_like_method(orig_target, target):
            # factory_like functions (eg. torch.empty_like())
            def wrapper(*args, **kwargs):
                orig_t = args[0]
                return self.tensor_cls(orig_target, *args[1:], device=orig_t.device, dtype=orig_t.dtype, **kwargs)

            return wrapper, target

        def wrap_legacy_constructor(target, dtype):
            # legacy constructor (e.g. torch.LongTensor())
            def wrapper(*args, **kwargs):
                if len(args) == 1 and isinstance(args[0], torch.Tensor):
                    # (Tensor other)
                    return args[0]
                elif len(args) == 1:
                    # (object data, *, torch.device device)
                    kwargs = {**kwargs, "dtype": dtype}
                    replaced, orig = self.overrides["tensor"]
                    return replaced(*args, **kwargs)
                elif _is_int_tuple(args):
                    # (tuple of ints size, *, torch.device device)
                    kwargs = {**kwargs, "dtype": dtype}
                    replaced, orig = self.overrides["empty"]
                    return replaced(*args, **kwargs)
                else:
                    raise TypeError(
                        f"new() received an invalid combination of arguments - got {tuple(type(x) for x in args)}, but expected one of:\n * (Tensor other)\n * (tuple of ints size, *, torch.device device)\n * (object data, *, torch.device device)"
                    )

            return wrapper, target

        def wrap_no_meta_factory(target):
            # factory functions which don't support meta tensor backend
            def wrapper(*args, **kwargs):
                tensor = target(*args, **kwargs)
                return self.tensor_cls(lambda: None, concrete_data=tensor)

            return wrapper, target

        self.overrides = {
            target: wrap_factory_method(getattr(torch, target))
            for target in _NORMAL_FACTORY
            if callable(getattr(torch, target, None))
        }

        self.overrides.update(
            {
                target + "_like": wrap_factory_like_method(getattr(torch, target), getattr(torch, target + "_like"))
                for target in _NORMAL_FACTORY
                if callable(getattr(torch, target + "_like", None))
            }
        )

        self.overrides.update(
            {
                target: wrap_legacy_constructor(getattr(torch, target), dtype)
                for target, dtype in _LEGACY_TENSOR_CONSTRUCTOR.items()
                if callable(getattr(torch, target, None))
            }
        )

        self.overrides.update(
            {
                target: wrap_no_meta_factory(getattr(torch, target))
                for target in _NO_META_FACTORY
                if callable(getattr(torch, target, None))
            }
        )

        for name, (wrapper, orig) in self.overrides.items():
            setattr(torch, name, wrapper)

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.tensor_cls.default_device = self.old_default_device
        LazyInitContext._replaced = False
        for name, (wrapper, orig) in self.overrides.items():
            setattr(torch, name, orig)

    @staticmethod
    def materialize(module: nn.Module, verbose: bool = False) -> nn.Module:
        """Initialize all ``Parameter`` from ``LazyTensor``. This function will modify the module in-place.

        Args:
            module (nn.Module): Target ``nn.Module``
            verbose (bool): Whether to print lazy initialization rate. Defaults to False.
        """

        def apply_fn(name: str, p: LazyTensor):
            p.materialize()

        return _apply_to_lazy_module(module, apply_fn, verbose)

    @staticmethod
    def distribute(
        module: nn.Module, device_mesh: DeviceMesh, sharding_spec_dict: Dict[str, ShardingSpec], verbose: bool = False
    ) -> nn.Module:
        """Distribute all ``Parameter`` from ``LazyTensor``. This function will modify the module in-place.

        Args:
            module (nn.Module): Target ``nn.Module``
            layout_dict (dict): Dict of layout for each parameter/buffer. The key is the parameter/buffer name, and the value is the layout.
            verbose (bool, optional): Whether to print lazy initialization rate. Defaults to False.
        """

        def apply_fn(name: str, p: LazyTensor):
            p.distribute(device_mesh, sharding_spec_dict[name])

        return _apply_to_lazy_module(module, apply_fn, verbose)


def _apply_to_lazy_module(
    module: nn.Module, apply_fn: Callable[[str, torch.Tensor], None], verbose: bool = False
) -> nn.Module:
    if verbose:
        # verbose info
        param_cnt = 0
        param_lazy_cnt = 0
        buf_cnt = 0
        buf_lazy_cnt = 0
        total_numel = 0
        non_lazy_numel = 0

    for name, p in module.named_parameters():
        if verbose:
            param_cnt += 1
            total_numel += p.numel()
            if getattr(p, "_materialized_data", False) is None:
                # if no _materialized_data attr, the tensor is not lazy
                param_lazy_cnt += 1
            else:
                non_lazy_numel += p.numel()
        if isinstance(p, LazyTensor):
            apply_fn(name, p)

    for name, buf in module.named_buffers():
        if verbose:
            buf_cnt += 1
            total_numel += buf.numel()
            if getattr(buf, "_materialized_data", False) is None:
                # if no _materialized_data attr, the tensor is not lazy
                buf_lazy_cnt += 1
            else:
                non_lazy_numel += buf.numel()
        if isinstance(buf, LazyTensor):
            apply_fn(name, buf)

    if verbose:
        non_lazy_numel_ratio = non_lazy_numel / total_numel * 100 if non_lazy_numel != 0 else 0
        _print_rank_0(f"Param lazy rate: {param_lazy_cnt}/{param_cnt}")
        _print_rank_0(f"Buffer lazy rate: {buf_lazy_cnt}/{buf_cnt}")
        _print_rank_0(
            f"Non lazy numel: {non_lazy_numel} ({non_lazy_numel/1024**2:.3f} M), ratio: {non_lazy_numel_ratio}%"
        )

    return module


def _print_rank_0(*args, **kwargs):
    if not dist.is_initialized() or dist.get_rank() == 0:
        print(*args, **kwargs)


def _is_int_tuple(args) -> bool:
    if not isinstance(args, tuple):
        return False
    for x in args:
        if not isinstance(x, int):
            return False
    return True


def _copy_tensor(tensor: Tensor, requires_grad: bool) -> Tensor:
    copied = tensor.data.clone()
    copied.requires_grad = requires_grad
    return copied
