"""
Test data generators for DeepGEMM kernel tests.

Adapted from the original DeepGEMM test suite to work with the ported
kernels-community package.
"""
import enum
import random
import torch
from typing import Generator, List, Optional, Tuple

from deep_gemm.testing import get_arch_major
from deep_gemm.utils import (
    align, ceil_div,
    per_token_cast_to_fp8, per_channel_cast_to_fp8, per_block_cast_to_fp8,
    per_token_cast_to_fp4, transpose_packed_fp4,
    get_mk_alignment_for_contiguous_layout
)


class KernelType(enum.Enum):
    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


class QuantConfig:
    _legacy_quant_config = (128, 128, False, False)

    def __init__(self, value: Tuple[int, int, bool, bool] = _legacy_quant_config):
        self.gran_k_a, self.gran_k_b, self.is_fp4_a, self.is_fp4_b = value

    def is_legacy(self) -> bool:
        return (self.gran_k_a, self.gran_k_b, self.is_fp4_a, self.is_fp4_b) == self._legacy_quant_config

    def get_recipes(self, is_wgrad: bool = False) -> Tuple[Tuple, Tuple, Tuple]:
        recipe, recipe_a, recipe_b = None, None, None
        if self.is_legacy():
            recipe = (1, 1, 128) if is_wgrad else None
        else:
            recipe_a = (1, self.gran_k_a)
            recipe_b = (1, self.gran_k_b) if self.is_fp4_b or is_wgrad else (self.gran_k_b, self.gran_k_b)
        return recipe, recipe_a, recipe_b

    def max_diff(self) -> float:
        if self.is_fp4_a and self.is_fp4_b:
            return 0.02
        if self.is_fp4_a or self.is_fp4_b:
            return 0.01
        return 0.001

    @staticmethod
    def get_list_from_dtype(dtype: torch.dtype) -> List:
        if dtype == torch.bfloat16:
            return [None]
        quant_config_list = [QuantConfig()]
        if get_arch_major() == 10:
            quant_config_list.append(QuantConfig((128, 32, False, True)))
        return quant_config_list


def reset_seed(seed: int = 0):
    random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)


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


def get_kernel_types(dtype: torch.dtype) -> tuple:
    if dtype == torch.bfloat16:
        return (KernelType.KernelNoSF, )
    return (KernelType.Kernel1D2D, ) if get_arch_major() == 9 else (KernelType.Kernel1D1D, )


def get_major_ab(allow_a_mn_major: bool, allow_b_mn_major: bool) -> Generator:
    for major_a in (MajorTypeAB.KMajor, MajorTypeAB.MNMajor):
        for major_b in (MajorTypeAB.KMajor, MajorTypeAB.MNMajor):
            if major_a.is_mn_major() and not allow_a_mn_major:
                continue
            if major_b.is_mn_major() and not allow_b_mn_major:
                continue
            yield major_a, major_b


def get_psum_layout_usage() -> tuple:
    return (False, True) if get_arch_major() == 10 else (False, )


def cast_fp8_fp4_with_major(x: torch.Tensor, major: MajorTypeAB, gran_k: int, is_fp4: bool,
                            use_ue8m0: bool, use_block_cast_for_fp8: bool = False):
    if is_fp4:
        x_fp4 = per_token_cast_to_fp4(x, use_ue8m0=use_ue8m0, gran_k=gran_k)
        x = x_fp4 if major.is_k_major() else (transpose_packed_fp4(x_fp4[0]).T, x_fp4[1])
    else:
        x_fp8 = per_block_cast_to_fp8(x, use_ue8m0=use_ue8m0, gran_k=gran_k) if use_block_cast_for_fp8 \
                else per_token_cast_to_fp8(x, use_ue8m0=use_ue8m0, gran_k=gran_k)
        x = x_fp8 if major.is_k_major() else (x_fp8[0].T.contiguous().T, x_fp8[1])
    return x


def grouped_cast_fp8_fp4_with_major(x: torch.Tensor, major: MajorTypeAB, gran_k: int, is_fp4: bool,
                                    use_ue8m0: bool, use_block_cast_for_fp8: bool = False):
    num_groups, mn, k = x.size()
    if is_fp4:
        x_fp4 = (torch.empty((num_groups, mn, k // 2), device='cuda', dtype=torch.uint8) if major.is_k_major() else
                 torch.empty((num_groups, k, mn // 2), device='cuda', dtype=torch.uint8),
                 torch.empty((num_groups, mn, ceil_div(k, gran_k)), device='cuda', dtype=torch.float))
        for i in range(num_groups):
            x_i_fp4 = per_token_cast_to_fp4(x[i], use_ue8m0=use_ue8m0, gran_k=gran_k)
            x_fp4[0][i], x_fp4[1][i] = x_i_fp4 if major.is_k_major() else (transpose_packed_fp4(x_i_fp4[0]), x_i_fp4[1])
        x = x_fp4 if major.is_k_major() else (x_fp4[0].mT, x_fp4[1])
    else:
        x_fp8 = (torch.empty_like(x, dtype=torch.float8_e4m3fn),
                 torch.empty((num_groups, ceil_div(mn, gran_k), ceil_div(k, gran_k)), device='cuda', dtype=torch.float) if use_block_cast_for_fp8
                 else torch.empty((num_groups, mn, ceil_div(k, gran_k)), device='cuda', dtype=torch.float))
        for i in range(num_groups):
            x_fp8[0][i], x_fp8[1][i] = per_block_cast_to_fp8(x[i], use_ue8m0=use_ue8m0, gran_k=gran_k) if use_block_cast_for_fp8 \
                                       else per_token_cast_to_fp8(x[i], use_ue8m0=use_ue8m0, gran_k=gran_k)
        x = x_fp8 if major.is_k_major() else (x_fp8[0].mT.contiguous().mT, x_fp8[1])
    return x


def generate_normal(m: int, n: int, k: int,
                    major_a: MajorTypeAB, major_b: MajorTypeAB,
                    accumulate: bool, out_dtype: torch.dtype,
                    kernel_type: KernelType,
                    use_ue8m0: bool = False, use_bf16: bool = False,
                    quant_config: Optional[QuantConfig] = None):
    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

    quant_config = QuantConfig() if quant_config is None else quant_config
    a = cast_fp8_fp4_with_major(a, major_a, quant_config.gran_k_a, quant_config.is_fp4_a, use_ue8m0)
    b = cast_fp8_fp4_with_major(b, major_b, quant_config.gran_k_b, quant_config.is_fp4_b, use_ue8m0,
                                use_block_cast_for_fp8=not (kernel_type.is_1d1d() and accumulate))

    return a, b, 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,
                                  use_psum_layout: bool = False,
                                  quant_config: Optional[QuantConfig] = None):
    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)
    grouped_layout = torch.empty(num_groups, device='cuda', dtype=torch.int32) if use_psum_layout \
                     else 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
        if use_psum_layout:
            grouped_layout[i] = actual_end
        else:
            grouped_layout[start: actual_end] = i
            grouped_layout[actual_end: aligned_end] = -1
        a[actual_end: aligned_end] = 0
        ref_d[start: aligned_end] = a[start: aligned_end] @ b[i].t()
        start = aligned_end

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

    assert major_a.is_k_major()
    quant_config = QuantConfig() if quant_config is None else quant_config
    a = cast_fp8_fp4_with_major(a, major_a, quant_config.gran_k_a, quant_config.is_fp4_a, use_ue8m0)
    b = grouped_cast_fp8_fp4_with_major(b, major_b, quant_config.gran_k_b, quant_config.is_fp4_b, use_ue8m0,
                                        use_block_cast_for_fp8=True)

    return m, a, b, grouped_layout, d, ref_d


def layout_masked_to_psum(x: torch.Tensor, psum_m: torch.Tensor):
    num_groups, max_m, _ = x.size()
    x_psum = torch.empty_like(x).view(num_groups * max_m, -1)
    last_psum_m = 0
    for i in range(num_groups):
        x_psum[last_psum_m: psum_m[i]] = x[i, :psum_m[i] - last_psum_m]
        last_psum_m = align(psum_m[i], 128)
    return x_psum


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,
                              use_psum_layout: bool = False,
                              quant_config: Optional[QuantConfig] = None):
    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)
    psum_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))
        psum_m[j] = (0 if j == 0 else align(psum_m[j - 1], 128)) + masked_m[j]
    assert masked_m.amax().item() <= max_m

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

    quant_config = QuantConfig() if quant_config is None else quant_config
    a = grouped_cast_fp8_fp4_with_major(a, MajorTypeAB.KMajor, quant_config.gran_k_a, quant_config.is_fp4_a, use_ue8m0)
    b = grouped_cast_fp8_fp4_with_major(b, MajorTypeAB.KMajor, quant_config.gran_k_b, quant_config.is_fp4_b, use_ue8m0,
                                        use_block_cast_for_fp8=True)

    return a, b, masked_m, psum_m, d, ref_d


def generate_k_grouped_contiguous(num_groups: int, m: int, n: int,
                                  major_a: MajorTypeAB, major_b: MajorTypeAB,
                                  ks: List[int],
                                  use_ue8m0: bool = False, use_bf16: bool = False):
    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

    if use_bf16:
        assert (major_a, major_b) == (MajorTypeAB.MNMajor, MajorTypeAB.MNMajor)
        return k, a, b, c, d, ref_d

    a_fp8 = per_channel_cast_to_fp8(a, use_ue8m0=use_ue8m0)
    b_fp8 = per_channel_cast_to_fp8(b, use_ue8m0=use_ue8m0)

    if (major_a, major_b) == (MajorTypeAB.KMajor, MajorTypeAB.KMajor):
        a, sfa = a_fp8
        b, sfb = b_fp8
        new_a = torch.empty((sum(ks) * m, ), dtype=a.dtype, device=a.device)
        new_b = torch.empty((sum(ks) * n, ), dtype=b.dtype, device=b.device)
        prefix = 0
        for K in ks:
            new_a[prefix * m : (prefix + K) * m] = a[prefix : prefix + K, ].T.flatten()
            new_b[prefix * n : (prefix + K) * n] = b[prefix : prefix + K, ].T.flatten()
            prefix += K
        a_fp8, b_fp8 = (new_a, sfa.T), (new_b, sfb.T)
    else:
        assert (major_a, major_b) == (MajorTypeAB.MNMajor, MajorTypeAB.MNMajor)

    return k, a_fp8, b_fp8, c, d, ref_d