import enum
import random
import torch
from typing import Generator, Tuple, List

from deep_gemm.utils import (
    align, ceil_div,
    per_token_cast_to_fp8, per_channel_cast_to_fp8, per_block_cast_to_fp8,
    get_mk_alignment_for_contiguous_layout
)


class KernelType(enum.Enum):
    # For SM100 GEMMs
    Kernel1D1D = 0
    Kernel1D2D = 1
    KernelNoSF = 2

    def is_1d1d(self):
        return self.value == 0

    def is_1d2d(self):
        return self.value == 1

    def is_nosf(self):
        return self.value == 2


class MajorTypeAB(enum.Enum):
    KMajor = 0
    MNMajor = 1

    def is_k_major(self):
        return self.value == 0

    def is_mn_major(self):
        return self.value == 1


def get_arch_major() -> int:
    major, minor = torch.cuda.get_device_capability()
    return major


def get_ue8m0_usage(kernel_type: KernelType) -> bool:
    if get_arch_major() == 9:
        return False
    return kernel_type.is_1d1d()


def get_kernel_types(use_bf16: bool = False) -> tuple:
    if use_bf16:
        return (KernelType.KernelNoSF, )
    return (KernelType.Kernel1D2D, ) if get_arch_major() == 9 else (KernelType.Kernel1D1D, KernelType.Kernel1D2D)


def get_out_dtype() -> tuple:
    return (torch.bfloat16, ) if get_arch_major() == 9 else (torch.bfloat16, torch.float)


def get_major_ab(freeze_a: bool) -> tuple:
    # TODO: test other major-ness for SM90 BF16 GEMMs
    if get_arch_major() == 9:
        return ((MajorTypeAB.KMajor,  MajorTypeAB.KMajor), )
    if freeze_a:
        return (MajorTypeAB.KMajor, MajorTypeAB.KMajor), (MajorTypeAB.KMajor, MajorTypeAB.MNMajor)
    return (MajorTypeAB.KMajor,  MajorTypeAB.KMajor), (MajorTypeAB.KMajor,  MajorTypeAB.MNMajor), \
           (MajorTypeAB.MNMajor, MajorTypeAB.KMajor), (MajorTypeAB.MNMajor, MajorTypeAB.MNMajor)


def enumerate_normal(use_bf16: bool = False) -> Generator:
    for kernel_type in get_kernel_types(use_bf16):
        for m in (128, 4096):
            for n, k in [(2112, 7168), (24576, 1536), (32768, 512), (7168, 16384), (4096, 7168), (7168, 2048)]:
                for major_a, major_b in get_major_ab(False):
                    for out_dtype in get_out_dtype():
                        for accumulate in (False, ) if out_dtype == torch.bfloat16 or kernel_type.is_1d2d() else (False, True):
                            yield kernel_type, m, n, k, major_a, major_b, accumulate, out_dtype


def enumerate_m_grouped_contiguous(use_bf16: bool = False) -> Generator:
    for kernel_type in get_kernel_types(use_bf16):
        for num_groups, expected_m_per_group, n, k in ((4, 8192, 4096, 7168), (4, 8192, 7168, 2048), (8, 4096, 4096, 7168), (8, 4096, 7168, 2048)):
            for major_a, major_b in get_major_ab(True):
                yield kernel_type, num_groups, expected_m_per_group, n, k, major_a, major_b


def enumerate_m_grouped_masked() -> Generator:
    max_m = 4096
    for kernel_type in get_kernel_types():
        for num_groups, m in ((1, 1024), (2, 512), (4, 256)):
            for n, k in ((4096, 7168), (7168, 2048), ):
                yield kernel_type, num_groups, max_m, m, n, k


def enumerate_k_grouped_contiguous():
    # TODO: support SM90 kernels
    if get_arch_major() == 9:
        return []

    # Must with FP32 accumulation and 1D1D kernels
    for num_groups, m, n, expected_k_per_group in (( 4, 4096, 7168, 8192), ( 4, 7168, 2048, 8192),   # EP64
                                                   ( 8, 4096, 7168, 4096), ( 8, 7168, 2048, 4096),   # EP32
                                                   (16, 4096, 7168, 2048), (16, 7168, 2048, 2048)):  # EP16
        ks = [align(int(expected_k_per_group * random.uniform(0.7, 1.3)), get_mk_alignment_for_contiguous_layout()) for _ in range(num_groups)]
        yield num_groups, m, n, ks, expected_k_per_group


def enumerate_sf_layout():
    for use_ue8m0 in (False, True):
        for with_transpose in (True, False):
            for mn in (4096, 4097, 8192):
                for k in (128, 7168, 7296):
                    for num_groups in (1, 2, 4):
                        yield mn, k, with_transpose, use_ue8m0, num_groups


def enumerate_k_grouped_sf_layout():
    alignment = get_mk_alignment_for_contiguous_layout()
    assert alignment % 128 == 0
    for mn in (4096, 7168):
        for num_groups, avg_k in ((16, 2048), (8, 4096), (72, 384), (128, 256)):
            ks = [align(int(random.uniform(0.7, 1.3) * avg_k), alignment) for _ in range(num_groups)]
            yield mn, ks, num_groups


def enumerate_transpose():
    for mn in (64, 4096, 16384):
        for delta in (0, 101, 202, 303):
            for k in (128, 1024, 4096, 9984, 16384):
                yield mn + delta, k


def generate_normal(m: int, n: int, k: int,
                    major_a: MajorTypeAB, major_b: MajorTypeAB,
                    accumulate: bool, out_dtype: torch.dtype,
                    use_ue8m0: bool = False, use_bf16: bool = False):
    a = torch.randn((m, k), device='cuda', dtype=torch.bfloat16)
    b = torch.randn((n, k), device='cuda', dtype=torch.bfloat16)
    d = torch.randn((m, n), device='cuda', dtype=out_dtype) * 32 if accumulate else \
        torch.empty((m, n), device='cuda', dtype=out_dtype)
    c = d if accumulate else None
    ref_d = (a.float() @ b.float().t() + (c if accumulate else 0)).to(out_dtype)

    if use_bf16:
        a = a if major_a.is_k_major() else a.T.contiguous().T
        b = b if major_b.is_k_major() else b.T.contiguous().T
        return a, b, c, d, ref_d

    a_fp8, b_fp8 = per_token_cast_to_fp8(a, use_ue8m0=use_ue8m0), per_block_cast_to_fp8(b, use_ue8m0=use_ue8m0)
    a_fp8 = a_fp8 if major_a.is_k_major() else (a_fp8[0].T.contiguous().T, a_fp8[1])
    b_fp8 = b_fp8 if major_b.is_k_major() else (b_fp8[0].T.contiguous().T, b_fp8[1])
    return a_fp8, b_fp8, c, d, ref_d


def generate_m_grouped_contiguous(num_groups: int, expected_m_per_group: int, n: int, k: int,
                                  major_a: MajorTypeAB, major_b: MajorTypeAB,
                                  use_ue8m0: bool = False, use_bf16: bool = False):
    actual_ms = [int(expected_m_per_group * random.uniform(0.7, 1.3)) for _ in range(num_groups)]
    aligned_ms = [align(actual_m, get_mk_alignment_for_contiguous_layout()) for actual_m in actual_ms]
    m = sum(aligned_ms)

    a = torch.randn((m, k), device='cuda', dtype=torch.bfloat16)
    b = torch.randn((num_groups, n, k), device='cuda', dtype=torch.bfloat16)
    m_indices = torch.empty(m, device='cuda', dtype=torch.int32)
    d = torch.empty((m, n), device='cuda', dtype=torch.bfloat16)
    ref_d = torch.randn((m, n), device='cuda', dtype=torch.bfloat16)

    start = 0
    for i, (actual_m, aligned_m) in enumerate(zip(actual_ms, aligned_ms)):
        actual_end = start + actual_m
        aligned_end = start + aligned_m
        m_indices[start:actual_end] = i
        m_indices[actual_end:aligned_end] = -1
        ref_d[start:aligned_end] = a[start:aligned_end] @ b[i].t()
        start = aligned_end
    ref_d = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(ref_d), ref_d)

    if use_bf16:
        b = b if major_b.is_k_major() else b.mT.contiguous().mT
        return m, a, b, m_indices, d, ref_d

    assert major_a.is_k_major()
    a_fp8 = per_token_cast_to_fp8(a, use_ue8m0=use_ue8m0)
    b_fp8 = (torch.empty_like(b, dtype=torch.float8_e4m3fn),
             torch.empty((num_groups, ceil_div(n, 128), ceil_div(k, 128)), device='cuda', dtype=torch.float))
    for i in range(num_groups):
        b_fp8[0][i], b_fp8[1][i] = per_block_cast_to_fp8(b[i], use_ue8m0=use_ue8m0)
    b_fp8 = b_fp8 if major_b.is_k_major() else (b_fp8[0].mT.contiguous().mT, b_fp8[1])
    return m, a_fp8, b_fp8, m_indices, d, ref_d


def generate_m_grouped_masked(num_groups: int, max_m: int, expected_m_per_group: int, n: int, k: int,
                              use_ue8m0: bool = False, use_bf16: bool = False):
    a = torch.randn((num_groups, max_m, k), device='cuda', dtype=torch.bfloat16)
    b = torch.randn((num_groups, n, k), device='cuda', dtype=torch.bfloat16)
    d = torch.empty((num_groups, max_m, n), device='cuda', dtype=torch.bfloat16)
    ref_d = torch.einsum('gmk,gnk->gmn', a, b)

    masked_m = torch.empty((num_groups, ), device='cuda', dtype=torch.int)
    for j in range(num_groups):
        masked_m[j] = int(expected_m_per_group * random.uniform(0.7, 1.3))
    assert masked_m.amax().item() <= max_m

    if use_bf16:
        return a, b, masked_m, d, ref_d

    a_fp8 = (torch.empty_like(a, dtype=torch.float8_e4m3fn), torch.empty((num_groups, max_m, ceil_div(k, 128)), device='cuda', dtype=torch.float))
    b_fp8 = (torch.empty_like(b, dtype=torch.float8_e4m3fn), torch.empty((num_groups, ceil_div(n, 128), ceil_div(k, 128)), device='cuda', dtype=torch.float))
    for i in range(num_groups):
        a_fp8[0][i], a_fp8[1][i] = per_token_cast_to_fp8(a[i], use_ue8m0=use_ue8m0)
        b_fp8[0][i], b_fp8[1][i] = per_block_cast_to_fp8(b[i], use_ue8m0=use_ue8m0)

    return a_fp8, b_fp8, masked_m, d, ref_d


def generate_k_grouped_contiguous(num_groups: int, m: int, n: int, ks: List[int], use_ue8m0: bool):
    assert get_mk_alignment_for_contiguous_layout() % 128 == 0
    k = sum(ks)

    a = torch.randn((k, m), device='cuda', dtype=torch.bfloat16)
    b = torch.randn((k, n), device='cuda', dtype=torch.bfloat16)
    c = torch.randn((num_groups, m, n), device='cuda', dtype=torch.float) * 32
    d = c
    ref_d = torch.empty_like(c)

    start = 0
    for i, group_k in enumerate(ks):
        end = start + group_k
        ref_d[i] = c[i] + (a[start:end].T @ b[start:end])
        start = end

    a_fp8 = per_channel_cast_to_fp8(a, use_ue8m0=use_ue8m0)
    b_fp8 = per_channel_cast_to_fp8(b, use_ue8m0=use_ue8m0)
    return k, a_fp8, b_fp8, c, d, ref_d