# mypy: allow-untyped-defs
import functools
import operator
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Callable, Dict, List, TYPE_CHECKING

import mindtorch
import mindtorch.distributed._shard.sharded_tensor.metadata as sharded_tensor_meta
from mindtorch.distributed._shard.metadata import ShardMetadata
from mindtorch.distributed._shard.op_registry_utils import _decorator_func

from ._internals import (
    check_tensor,
    get_chunked_dim_size,
    get_split_size,
    validate_non_overlapping_shards_metadata,
)


if TYPE_CHECKING:
    # Only include ShardedTensor when do type checking, exclude it
    # from run-time to resolve circular dependency.
    from mindtorch.distributed._shard.sharded_tensor import ShardedTensor


class PlacementSpec(ABC):  # noqa: B024
    """
    Base class representing the placement of an entity. Subclasses of this
    class can be used to specify customized placements which might not be
    covered by existing APIs.
    """


@dataclass
class DevicePlacementSpec(PlacementSpec):
    """
    Associates placement of an entity with a single device.

    Args:
        device(:class:`mindtorch.distributed._remote_device`): The device to place the entity on.
    """

    device: mindtorch.distributed._remote_device

    def __post_init__(self):
        if not isinstance(self.device, mindtorch.distributed._remote_device):
            self.device = mindtorch.distributed._remote_device(self.device)


class ShardingSpec(ABC):
    """
    Base class representing sharding specifications.
    """

    @abstractmethod
    def build_metadata(
        self,
        tensor_sizes: mindtorch.Size,
        tensor_properties: sharded_tensor_meta.TensorProperties,
    ) -> sharded_tensor_meta.ShardedTensorMetadata:
        """
        Given a global tensor size, define how to shard a tensor like this shape
        across ranks, return ShardedTensorMetadata
        Args:
            tensor_sizes (:class:`mindtorch.Size`):
                The tensor shape to shard on, a `mindtorch.Size` object that represents the
                tensor shape to be sharded according to the ShardingSpec.
            tensor_properties(:class:`mindtorch.distributed._shard.sharded_tensor.TensorProperties):
                Tensor properties used to create a ShardedTensor.
        Returns:
            A :class:`ShardedTensorMetadata` object that encodes the information about
            the layout of the ShardedTensor and its properties.
        """

    @abstractmethod
    def shard(
        self, tensor: mindtorch.Tensor, src_rank: int = 0, process_group=None
    ) -> "ShardedTensor":
        """
        Given a global tensor on src_rank, shard this tensor
        across ranks within the process group, return a ShardedTensor.
        Args:
            tensor (:class:`mindtorch.Tensor`): Tensor needs to be sharded.
        Keyword args:
            src_rank (int, optional): The source rank which is used as the ground truth of
                the data for the parameter that would be sharded and scattered
                across the rest of the ranks.
                Default: 0.
            process_group (ProcessGroup, optional): The process group to work on. If None,
                the default process group will be used.
        Returns:
            A :class:`ShardedTensor` sharded from the given tensor.
        """


# Ops customized for a particular ShardingSpec.
_CUSTOM_SHARDING_SPEC_OPS: Dict[str, Dict[Callable, Callable]] = {}


def _has_custom_op(sharding_spec, op):
    """
    Returns whether or not the ShardingSpec has a custom op implementation.
    """
    class_name = type(sharding_spec).__qualname__
    return (
        class_name in _CUSTOM_SHARDING_SPEC_OPS
        and op in _CUSTOM_SHARDING_SPEC_OPS[class_name]
    )


def _dispatch_custom_op(
    sharding_spec, op: Callable, types, args, kwargs, process_group
):
    """
    Calls the custom op for this ShardingSpec if it exists.
    """
    class_name = type(sharding_spec).__qualname__
    if not _has_custom_op(sharding_spec, op):
        raise RuntimeError(f"Custom op: {op} not registered for {class_name}")
    func = _CUSTOM_SHARDING_SPEC_OPS[class_name][op]
    return func(types, args, kwargs, process_group)


def custom_sharding_spec_op(sharding_spec_class, func):
    """
    Decorator to allow custom registration of ops.
    Args:
        sharding_spec_class(type): The ShardingSpec for which we need to add this custom op.
        func(Callable): The op to override (ex: mindtorch.bmm)
    """
    class_name = sharding_spec_class.__qualname__
    if class_name not in _CUSTOM_SHARDING_SPEC_OPS:
        _CUSTOM_SHARDING_SPEC_OPS[class_name] = {}
    return functools.partial(
        _decorator_func, op=func, op_table=_CUSTOM_SHARDING_SPEC_OPS[class_name]
    )


@dataclass
class EnumerableShardingSpec(ShardingSpec):
    """
    This is a type of PlacementSpec that allows users to specify a generic
    sharding scheme by enumerating exactly how each shard is laid out.

    Args:
        shards(List[ShardMetadata]): List of :class:`ShardMetadata` objects representing
            each shard. Note that none of the shards should overlap.
    """

    shards: List[ShardMetadata]

    def __post_init__(self):
        if len(self.shards) == 0:
            raise ValueError(f"Empty shard list provided: {self.shards}")

        # Validate each shard has same rank.
        rank = -1
        for shard in self.shards:
            if rank != -1 and rank != len(shard.shard_offsets):
                raise ValueError(
                    f"Found inconsistent ranks for shards: {rank} and {len(shard.shard_offsets)}"
                )
            rank = len(shard.shard_offsets)

        validate_non_overlapping_shards_metadata(self.shards)

    def build_metadata(
        self,
        tensor_sizes: mindtorch.Size,
        tensor_properties: sharded_tensor_meta.TensorProperties,
    ) -> sharded_tensor_meta.ShardedTensorMetadata:
        # check if shards form a valid tensor
        check_tensor(self.shards, tensor_sizes)
        return sharded_tensor_meta.ShardedTensorMetadata(
            self.shards, tensor_sizes, tensor_properties
        )

    def shard(
        self, tensor: mindtorch.Tensor, src_rank: int = 0, process_group=None
    ) -> "ShardedTensor":
        # TODO: figure out a generic and efficient way to scatter the shards for EnumerableShardingSpec
        raise NotImplementedError("EnumerableShardingSpec.shard not implemented yet!")


def _infer_sharding_spec_from_shards_metadata(shards_metadata):
    """
    Infer the sharding spec from the metadata of each shard of a ShardedTensor.
    If the tensor is sharded only on one dimension, we can then verify whether it's
    a ChunkShardingSpec or not. The way to verify it is to first get the total length
    and perform a chunk sharding with the given placements to see if we can have the
    same chunk size as the given shards_metadata. If not, we assume it's enum sharded.

    Args:
        shards_metadata (List[ShardMetadata]): List of Metadata of local shards.

    Returns:
        A :class:`mindtorch.distributed._shard.sharding_spec.ShardingSpec` object of sharding
            spec for one sharded tensor.
    """
    placements = []
    chunk_sharding_dim = None
    chunk_offset_list = []
    shard_size_list = []
    shard_offset_list = []
    # collect local shard metadatas from the global sharded_tensor_metadata
    for shard_metadata in shards_metadata:  # type: ignore[attr-defined]
        placements.append(shard_metadata.placement)
        local_offsets = shard_metadata.shard_offsets
        chunk_offset_list.append(sum(local_offsets))
        shard_size_list.append(shard_metadata.shard_sizes)
        shard_offset_list.append(shard_metadata.shard_offsets)
        shard_dims = [idx for idx, e in enumerate(local_offsets) if e != 0]
        # If the offset is [0, 0, ..., 0] (all zeros),
        # we cannot decide whether how the tensor is sharded.
        if len(shard_dims) == 0:
            continue
        # If the offset is [0, N, .,0, M, 0, .., 0],
        # we are sure it's sharded by more than one dimension.
        if len(shard_dims) != 1:
            chunk_sharding_dim = None
            break
        # If the offset is [0, 0, .,0, M, 0, .., 0], aka, it's sharded by just
        # one dimension, we need to make sure all ranks share the same dimension.
        if not chunk_sharding_dim:
            chunk_sharding_dim = shard_dims[0]
        elif chunk_sharding_dim != shard_dims[0]:
            chunk_sharding_dim = None
            break

    if chunk_sharding_dim is not None:
        # Ensure we infer the correct placement order from offsets
        placements = [
            x
            for _, x in sorted(
                zip(chunk_offset_list, placements), key=operator.itemgetter(0)
            )
        ]

        from .chunk_sharding_spec import ChunkShardingSpec

        chunk_spec = ChunkShardingSpec(
            dim=chunk_sharding_dim,
            placements=placements,
        )

        shard_sizes = sorted([x[chunk_sharding_dim] for x in shard_size_list])
        shard_total_length = sum(shard_sizes)
        shard_offsets = sorted([x[chunk_sharding_dim] for x in shard_offset_list])

        chunks = len(placements)
        split_size = get_split_size(shard_total_length, chunks)
        chunk_shard_sizes = sorted(
            [
                get_chunked_dim_size(shard_total_length, split_size, idx)
                for idx in range(chunks)
            ]
        )
        # Should match ChunkShardingSpec offsets calculation
        chunk_shard_offsets = [split_size * idx for idx in range(chunks)]
        if shard_sizes == chunk_shard_sizes and shard_offsets == chunk_shard_offsets:
            return chunk_spec
    return EnumerableShardingSpec(shards_metadata)
