"""This file is used for /tests and /benchmarks"""
import random

import numpy
import torch

from ktransformers.ktransformers_ext.operators.custom_marlin.quantize.utils.format_24 import (
    mask_creator, sparse_semi_structured_from_dense_cutlass)
from ktransformers.ktransformers_ext.operators.custom_marlin.quantize.utils.marlin_24_perms import (
    marlin_24_perm, marlin_24_scale_perm, marlin_24_scale_perm_single)
from ktransformers.ktransformers_ext.operators.custom_marlin.quantize.utils.marlin_perms import (
    marlin_perm, marlin_scale_perm, marlin_scale_perm_single)
from ktransformers.ktransformers_ext.operators.custom_marlin.quantize.utils.quant_utils import (
    get_pack_factor, quantize_weights, sort_weights)

__cuda_arch = torch.cuda.get_device_capability()

MARLIN_TILE = 16

GPTQ_MARLIN_TILE = 16
GPTQ_MARLIN_MIN_THREAD_N = 64
GPTQ_MARLIN_MIN_THREAD_K = 128
GPTQ_MARLIN_MAX_PARALLEL = 16

GPTQ_MARLIN_SUPPORTED_NUM_BITS = [4, 8]
GPTQ_MARLIN_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128]
GPTQ_MARLIN_SUPPORTED_SYM = [True]

def is_marlin_supported():
    return __cuda_arch[0] >= 8


def marlin_permute_weights(q_w, size_k, size_n, perm, tile=MARLIN_TILE):
    assert q_w.shape == (size_k, size_n)
    assert size_k % tile == 0, f"size_k = {size_k}, tile = {tile}"
    assert size_n % tile == 0, f"size_k = {size_n}, tile = {tile}"

    # Permute weights to 16x64 marlin tiles
    q_w = q_w.reshape((size_k // tile, tile, size_n // tile, tile))
    q_w = q_w.permute((0, 2, 1, 3))
    q_w = q_w.reshape((size_k // tile, size_n * tile))

    q_w = q_w.reshape((-1, perm.numel()))[:, perm].reshape(q_w.shape)

    return q_w


def marlin_weights(q_w, size_k, size_n, num_bits, perm):
    # Permute
    q_w = marlin_permute_weights(q_w, size_k, size_n, perm)

    # Pack
    pack_factor = get_pack_factor(num_bits)
    orig_device = q_w.device

    q_w = q_w.cpu().numpy().astype(numpy.uint32)

    q_packed = numpy.zeros((q_w.shape[0], q_w.shape[1] // pack_factor),
                           dtype=numpy.uint32)
    for i in range(pack_factor):
        q_packed |= q_w[:, i::pack_factor] << num_bits * i

    q_packed = torch.from_numpy(q_packed.astype(numpy.int32)).to(orig_device)

    return q_packed


def marlin_permute_scales(s, size_k, size_n, group_size, scale_perm,
                          scale_perm_single):
    if group_size < size_k and group_size != -1:
        s = s.reshape((-1, len(scale_perm)))[:, scale_perm]
    else:
        s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single]
    s = s.reshape((-1, size_n)).contiguous()

    return s


def marlin_quantize(
    w: torch.Tensor,
    num_bits: int,
    group_size: int,
    act_order: bool,
):
    size_k, size_n = w.shape

    # Normalize group_size
    if group_size == -1:
        group_size = size_k
    assert group_size <= size_k

    # Quantize (and apply act_order if provided)
    q_w, s, g_idx, rand_perm = quantize_weights(w, num_bits, group_size,
                                                       act_order)

    # For act_order, sort the "weights" and "g_idx" so that group ids are
    # increasing
    sort_indices = torch.empty(0, dtype=torch.int, device=w.device)
    if act_order:
        q_w, g_idx, sort_indices = sort_weights(q_w, g_idx)

    # Reformat to marlin
    marlin_q_w = marlin_weights(q_w, size_k, size_n, num_bits,
                                marlin_perm[num_bits])
    marlin_s = marlin_permute_scales(s, size_k, size_n, group_size,
                                     marlin_scale_perm[num_bits],
                                     marlin_scale_perm_single[num_bits])

    # Create result
    res_list = [marlin_q_w, marlin_s, g_idx, sort_indices, rand_perm]
    for i in range(len(res_list)):
        res_list[i] = res_list[i].to(w.device)

    return res_list


def inject_24(w, size_k, size_n):
    assert w.shape == (size_k, size_n)

    mask = mask_creator(w.t()).t().cuda().bool()

    return (mask * w).contiguous(), mask.contiguous()


def check_24(w, num_rows_to_sample=50, _verbose=False):
    BLOCK_SIZE = 4
    MAX_NON_ZEROS = 2

    w = w.t().contiguous()

    print("check_24: w.shape = {}".format(w.shape))

    num_rows, num_cols = w.shape
    sampled_row_idxs = random.choices(range(num_rows), k=num_rows_to_sample)
    if _verbose:
        print(f"Sampled row idxs = {sampled_row_idxs}")

    total_segments = 0
    non_24_segments = 0
    for i in sampled_row_idxs:
        for j in range(0, num_cols - BLOCK_SIZE, BLOCK_SIZE):
            total_segments += 1
            block = w[i, j:j + BLOCK_SIZE]
            num_nonzero = torch.count_nonzero(block)
            if num_nonzero > MAX_NON_ZEROS:
                print("i = {} j = {} block = {}".format(i, j, block))
                non_24_segments += 1

    print(f"{non_24_segments} / {total_segments} do not have 2:4 structure.")


def compress_quantized_24_weight(q_24, size_k, size_n, num_bits):
    assert q_24.shape == (size_k, size_n)

    # Remove zp to normalize over 0
    max_q_val = (1 << num_bits) - 1
    zp = (max_q_val + 1) // 2
    q_24_no_zp = q_24 - zp

    # Compress
    q_24_no_zp = q_24_no_zp.t().contiguous()
    q_24_no_zp_comp, meta = sparse_semi_structured_from_dense_cutlass(
        q_24_no_zp)
    q_24_no_zp_comp = q_24_no_zp_comp.t().contiguous()

    # Restore zp
    q_24_comp = q_24_no_zp_comp + zp

    # Resize meta to its actual shape (without moving any data)
    meta = meta.resize_(meta.shape[1] // 2, meta.shape[0] * 2)

    return q_24_comp, meta


def marlin_24_quantize(
    w: torch.Tensor,
    num_bits: int,
    group_size: int,
):
    size_k, size_n = w.shape

    # Normalize group_size
    if group_size == -1:
        group_size = size_k
    assert group_size <= size_k

    # Inject 2:4 sparsity
    w_24, mask_24 = inject_24(w, size_k, size_n)

    # Quantize
    w_24_ref, q_w_24, s, g_idx, rand_perm = quantize_weights(w_24,
                                                             num_bits,
                                                             group_size,
                                                             act_order=False)

    # Compress quantized weight
    q_w_24_comp, meta = compress_quantized_24_weight(q_w_24, size_k, size_n,
                                                     num_bits)
    size_k_comp = size_k // 2

    # Reformat to marlin
    marlin_24_q_w_comp = marlin_weights(q_w_24_comp, size_k_comp, size_n,
                                        num_bits, marlin_24_perm[num_bits])
    marlin_24_s = marlin_permute_scales(s, size_k, size_n, group_size,
                                        marlin_24_scale_perm[num_bits],
                                        marlin_24_scale_perm_single[num_bits])

    # Create result
    res_list = [w_24_ref, marlin_24_q_w_comp, meta, marlin_24_s]
    for i in range(len(res_list)):
        res_list[i] = res_list[i].to(w.device)

    return res_list


def compute_max_diff(output, output_ref):
    return torch.mean(torch.abs(output - output_ref)) / torch.mean(
        torch.abs(output_ref))


class MarlinWorkspace:

    def __init__(self, out_features, min_thread_n, max_parallel, device):
        assert (out_features % min_thread_n == 0), (
            "out_features = {} is undivisible by min_thread_n = {}".format(
                out_features, min_thread_n))

        max_workspace_size = ((out_features // min_thread_n) * max_parallel)

        self.scratch = torch.zeros(max_workspace_size,
                                   dtype=torch.int,
                                   device=device)
