from dataclasses import dataclass
from typing import List, Any
from triton._utils import validate_block_shape


@dataclass
class TensorDescriptor:
    base: Any
    shape: List[int]
    strides: List[int]
    block_shape: List[int]

    def __post_init__(self):
        rank = len(self.shape)
        assert len(self.strides) == rank, f"rank mismatch: {self}"
        assert len(self.block_shape) == rank, f"rank mismatch: {self}"
        assert rank > 0, "rank must not be zero"
        assert rank <= 5, "rank cannot be more than 5"
        ty = type(self.base)
        type_name = f"{ty.__module__}.{ty.__name__}"
        if type_name not in ("torch.FakeTensor", "torch.FunctionalTensor"):
            assert self.base.data_ptr() % 16 == 0, "base must be 16-byte aligned"
        validate_block_shape(self.block_shape)
        elem_bytes = self.base.dtype.itemsize
        for stride in self.strides[:-1]:
            assert (stride * elem_bytes) % 16 == 0, "strides must be 16-byte aligned"
        assert self.strides[-1] == 1, "Last dimension must be contiguous"

    @staticmethod
    def from_tensor(tensor: Any, block_shape: List[int]):
        return TensorDescriptor(
            tensor,
            tensor.shape,
            tensor.stride(),
            block_shape,
        )