import torch
# import flashinfer
import matplotlib.pyplot as plt
# from sparse_sageattn import sparse_sageattn
from einops import rearrange, repeat
from sageattention import sageattn
from spas_sage_attn import block_sparse_sage2_attn_cuda

def get_cuda_arch_versions():
    cuda_archs = []
    for i in range(torch.cuda.device_count()):
        major, minor = torch.cuda.get_device_capability(i)
        cuda_archs.append(f"sm{major}{minor}")
    return cuda_archs

from spas_sage_attn import block_sparse_sage2_attn_cuda

def sparge_mask_convert(mask: torch.Tensor, block_size: int = 128, arch="sm") -> torch.Tensor:
    assert block_size in [128, 64], "Radial Attention only supports block size of 128 or 64"
    assert mask.shape[0] == mask.shape[1], "Input mask must be square."

    if block_size == 128:
        if arch == "sm90":
            new_mask = torch.repeat_interleave(mask, 2, dim=0)
        else:
            new_mask = torch.repeat_interleave(mask, 2, dim=1)
        
    elif block_size == 64:
        if arch == "sm90":
            num_row, num_col = mask.shape
            reshaped_mask = mask.view(num_row, num_col // 2, 2)
            new_mask = torch.max(reshaped_mask, dim=2).values
        else:
            num_row, num_col = mask.shape
            reshaped_mask = mask.view(num_row // 2, 2, num_col)
            new_mask = torch.max(reshaped_mask, dim=1).values

    return new_mask

def get_indptr_from_mask(mask, query):
    # query shows the device of the indptr
    # indptr (torch.Tensor) - the block index pointer of the block-sparse matrix on row dimension,
    # shape `(MB + 1,)`, where `MB` is the number of blocks in the row dimension.
    # The first element is always 0, and the last element is the number of blocks in the row dimension.
    # The rest of the elements are the number of blocks in each row.
    # the mask is already a block sparse mask
    indptr = torch.zeros(mask.shape[0] + 1, device=query.device, dtype=torch.int32)
    indptr[0] = 0
    row_counts = mask.sum(dim=1).flatten()  # Ensure 1D output [num_blocks_row]
    indptr[1:] = torch.cumsum(row_counts, dim=0)
    return indptr

def get_indices_from_mask(mask, query):
    # indices (torch.Tensor) - the block indices of the block-sparse matrix on column dimension,
    # shape `(nnz,),` where `nnz` is the number of non-zero blocks.
    # The elements in `indices` array should be less than `NB`: the number of blocks in the column dimension.
    nonzero_indices = torch.nonzero(mask)
    indices = nonzero_indices[:, 1].to(dtype=torch.int32, device=query.device)
    return indices

def shrinkMaskStrict(mask, block_size=128):
    seqlen = mask.shape[0]
    block_num = seqlen // block_size
    mask = mask[:block_num * block_size, :block_num * block_size].view(block_num, block_size, block_num, block_size)
    col_densities = mask.sum(dim = 1) / block_size
    # we want the minimum non-zero column density in the block
    non_zero_densities = col_densities > 0
    high_density_cols = col_densities > 1/3
    frac_high_density_cols = high_density_cols.sum(dim=-1) / (non_zero_densities.sum(dim=-1) + 1e-9)
    block_mask = frac_high_density_cols > 0.6
    block_mask[0:0] = True
    block_mask[-1:-1] = True
    return block_mask

def pad_qkv(input_tensor, block_size=128):
    """
    Pad the input tensor to be a multiple of the block size.
    input shape: (seqlen, num_heads, hidden_dim)
    """
    seqlen, num_heads, hidden_dim = input_tensor.shape
    # Calculate the necessary padding
    padding_length = (block_size - (seqlen % block_size)) % block_size
    # Create a padded tensor with zeros
    padded_tensor = torch.zeros((seqlen + padding_length, num_heads, hidden_dim), device=input_tensor.device, dtype=input_tensor.dtype)
    # Copy the original tensor into the padded tensor
    padded_tensor[:seqlen, :, :] = input_tensor
    
    return padded_tensor

def get_diagonal_split_mask(i, j, token_per_frame, sparse_type, query):
    assert(sparse_type in ["radial"])
    dist = abs(i - j)
    group = dist.bit_length()
    threshold = 128 # hardcoded threshold for now, which is equal to block-size
    decay_length = 2 ** token_per_frame.bit_length() / 2 ** group
    if decay_length >= threshold:
        return torch.ones((token_per_frame, token_per_frame), device=query.device, dtype=torch.bool)
    
    split_factor = int(threshold / decay_length)
    modular = dist % split_factor
    if modular == 0:
        return torch.ones((token_per_frame, token_per_frame), device=query.device, dtype=torch.bool)
    else:
        return torch.zeros((token_per_frame, token_per_frame), device=query.device, dtype=torch.bool)

def get_window_width(i, j, token_per_frame, sparse_type, num_frame, decay_factor=1, block_size=128, model_type=None):
    assert(sparse_type in ["radial"])
    dist = abs(i - j)
    if model_type == "wan":
        if dist < 1:
            return token_per_frame
        if dist == 1:
            return token_per_frame // 2
    elif model_type == "hunyuan":
        if dist <= 1:
            return token_per_frame
    else:
        raise ValueError(f"Unknown model type: {model_type}")
    group = dist.bit_length()
    decay_length = 2 ** token_per_frame.bit_length() / 2 ** group * decay_factor
    threshold = block_size
    if decay_length >= threshold:
        return decay_length
    else:
        return threshold

def gen_log_mask_shrinked(query, s, video_token_num, num_frame, block_size=128, sparse_type="log", decay_factor=0.5, model_type=None):
    """
    A more memory friendly version, we generate the attention mask of each frame pair at a time,
    shrinks it, and stores it into the final result
    """
    final_log_mask = torch.zeros((s // block_size, s // block_size), device=query.device, dtype=torch.bool)
    token_per_frame = video_token_num // num_frame
    video_text_border = video_token_num // block_size

    col_indices = torch.arange(0, token_per_frame, device=query.device).view(1, -1)
    row_indices = torch.arange(0, token_per_frame, device=query.device).view(-1, 1)
    final_log_mask[video_text_border:] = True
    final_log_mask[:, video_text_border:] = True
    for i in range(num_frame):
        for j in range(num_frame):
            local_mask = torch.zeros((token_per_frame, token_per_frame), device=query.device, dtype=torch.bool)
            if j == 0 and model_type == "wan": # this is attention sink
                local_mask = torch.ones((token_per_frame, token_per_frame), device=query.device, dtype=torch.bool)
            else:
                window_width = get_window_width(i, j, token_per_frame, sparse_type, num_frame, decay_factor=decay_factor, block_size=block_size, model_type=model_type)
                local_mask = torch.abs(col_indices - row_indices) <= window_width
                split_mask = get_diagonal_split_mask(i, j, token_per_frame, sparse_type, query)
                local_mask = torch.logical_and(local_mask, split_mask)

            remainder_row = (i * token_per_frame) % block_size
            remainder_col = (j * token_per_frame) % block_size
            # get the padded size
            all_length_row = remainder_row + ((token_per_frame - 1) // block_size + 1) * block_size
            all_length_col = remainder_col + ((token_per_frame - 1) // block_size + 1) * block_size
            padded_local_mask = torch.zeros((all_length_row, all_length_col), device=query.device, dtype=torch.bool)
            padded_local_mask[remainder_row:remainder_row + token_per_frame, remainder_col:remainder_col + token_per_frame] = local_mask
            # shrink the mask
            block_mask = shrinkMaskStrict(padded_local_mask, block_size=block_size)
            # set the block mask to the final log mask
            block_row_start = (i * token_per_frame) // block_size
            block_col_start = (j * token_per_frame) // block_size
            block_row_end = block_row_start + block_mask.shape[0]
            block_col_end = block_col_start + block_mask.shape[1]
            final_log_mask[block_row_start:block_row_end, block_col_start:block_col_end] = torch.logical_or(
                final_log_mask[block_row_start:block_row_end, block_col_start:block_col_end], block_mask)
    print(f"mask sparsity: {1 - final_log_mask.sum() / final_log_mask.numel()}")
    return final_log_mask

class MaskMap:
    _log_mask = None

    def __init__(self, video_token_num=25440, num_frame=16):
        self.video_token_num = video_token_num
        self.num_frame = num_frame

    def queryLogMask(self, query, sparse_type, block_size=128, decay_factor=0.5, model_type=None):
        if MaskMap._log_mask is None:
            MaskMap._log_mask = torch.ones((query.shape[0] // block_size, query.shape[0] // block_size), device=query.device, dtype=torch.bool)
            MaskMap._log_mask = gen_log_mask_shrinked(query, query.shape[0], self.video_token_num, self.num_frame, sparse_type=sparse_type, decay_factor=decay_factor, model_type=model_type, block_size=block_size)
        return MaskMap._log_mask

def SpargeSageAttnBackend(query, key, value, mask_map=None, video_mask=None, pre_defined_mask=None, block_size=128):
    if video_mask.all():
        # dense case
        kv_border = pre_defined_mask[0].sum() if pre_defined_mask is not None else key.shape[0]
        output_video = sageattn(
            query[:mask_map.video_token_num, :, :].unsqueeze(0),
            key[:kv_border, :, :].unsqueeze(0),
            value[:kv_border, :, :].unsqueeze(0),
            tensor_layout="NHD",
        )[0]
        
        if pre_defined_mask is not None:
            output_text = flashinfer.single_prefill_with_kv_cache(
                q=query[mask_map.video_token_num:, :, :],
                k=key[:pre_defined_mask[0].sum(), :, :],
                v=value[:pre_defined_mask[0].sum(), :, :],
                causal=False,
                return_lse=False,
            )
            return torch.cat([output_video, output_text], dim=0)
        else:
            return output_video
    
    # sparse-sageattention only supports (b, h, s, d) layout, need rearrange first
    query_hnd = rearrange(query.unsqueeze(0), "b s h d -> b h s d")
    key_hnd = rearrange(key.unsqueeze(0), "b s h d -> b h s d")
    value_hnd = rearrange(value.unsqueeze(0), "b s h d -> b h s d")
    arch = get_cuda_arch_versions()[query.device.index]
    converted_mask = repeat(sparge_mask_convert(mask=video_mask, block_size=block_size, arch=arch), "s t -> b h s t", b=query_hnd.shape[0], h=query_hnd.shape[1])
    
    converted_mask = converted_mask.to(torch.int8)
    if pre_defined_mask is None:
        # wan case
        output = block_sparse_sage2_attn_cuda(
            query_hnd[:, :, :mask_map.video_token_num, :],
            key_hnd[:, :, :mask_map.video_token_num, :],
            value_hnd[:, :, :mask_map.video_token_num, :],
            mask_id=converted_mask,
            tensor_layout="HND",
        )

        # rearrange back to (s, h, d), we know that b = 1
        output = rearrange(output, "b h s d -> s (b h) d", b=1)
        return output
    
    query_video = query_hnd[:, :, :mask_map.video_token_num, :]
    key_video = key_hnd
    value_video = value_hnd
    kv_border = (pre_defined_mask[0].sum() + 63) // 64
    converted_mask[:, :, :, kv_border:] = False
    output_video = block_sparse_sage2_attn_cuda(
        query_video,
        key_video,
        value_video,
        mask_id=converted_mask[:, :, :mask_map.video_token_num // block_size, :].contiguous(),
        tensor_layout="HND",
    )
    
    # rearrange back to (s, h, d), we know that b = 1
    output_video = rearrange(output_video, "b h s d -> s (b h) d", b=1)
    
    # gt = sparse_sageattn(
    #     query_video,
    #     key_video,
    #     value_video,
    #     mask_id=None,
    #     is_causal=False,
    #     tensor_layout="HND",
    # )[0]
    
    
    
    # import pdb; pdb.set_trace()
    
    output_text = flashinfer.single_prefill_with_kv_cache(
        q=query[mask_map.video_token_num:, :, :],
        k=key[:pre_defined_mask[0].sum(), :, :],
        v=value[:pre_defined_mask[0].sum(), :, :],
        causal=False,
        return_lse=False,
    )
    
    return torch.cat([output_video, output_text], dim=0)
    

def FlashInferBackend(query, key, value, mask_map=None, pre_defined_mask=None, bsr_wrapper=None):
    if pre_defined_mask is not None:
        video_video_o, video_video_o_lse = bsr_wrapper.run(
            query[:mask_map.video_token_num, :, :], 
            key[:mask_map.video_token_num, :, :],
            value[:mask_map.video_token_num, :, :],
            return_lse=True
        ) 
        # perform non-causal flashinfer on the text tokens
        video_text_o, video_text_o_lse = flashinfer.single_prefill_with_kv_cache(
            q=query[:mask_map.video_token_num, :, :],
            k=key[mask_map.video_token_num:, :, :],
            v=value[mask_map.video_token_num:, :, :],
            causal=False,
            return_lse=True,
            custom_mask=pre_defined_mask[:mask_map.video_token_num, mask_map.video_token_num:]
        )
        
        # merge the two results
        o_video, _ = flashinfer.merge_state(v_a=video_video_o, s_a=video_video_o_lse, v_b=video_text_o, s_b=video_text_o_lse)
        
        o_text = flashinfer.single_prefill_with_kv_cache(
            q=query[mask_map.video_token_num:, :, :],
            k=key,
            v=value,
            causal=False,
            return_lse=False,
            custom_mask=pre_defined_mask[mask_map.video_token_num:, :]
        )
        
        return torch.cat([o_video, o_text], dim=0)
    else:
        o = bsr_wrapper.run(
            query[:mask_map.video_token_num, :, :],
            key[:mask_map.video_token_num, :, :],
            value[:mask_map.video_token_num, :, :]
        )
        return o

def RadialAttention(query, key, value, mask_map=None, sparsity_type="radial", block_size=128, decay_factor=1, model_type=None, pre_defined_mask=None, use_sage_attention=False):
    orig_seqlen, num_head, hidden_dim = query.shape

    if sparsity_type == "dense":
        video_mask = torch.ones((mask_map.video_token_num // block_size, mask_map.video_token_num // block_size), device=query.device, dtype=torch.bool)
    else:
        video_mask = mask_map.queryLogMask(query, sparsity_type, block_size=block_size, decay_factor=decay_factor, model_type=model_type) if mask_map else None
    
    backend = "sparse_sageattn" if use_sage_attention else "flashinfer"
    
    if backend == "flashinfer":
        video_mask = video_mask[:mask_map.video_token_num // block_size, :mask_map.video_token_num // block_size]
        # perform block-sparse attention on the video tokens
        workspace_buffer = torch.empty(128 * 1024 * 1024, device=query.device, dtype=torch.uint8)
        bsr_wrapper = flashinfer.BlockSparseAttentionWrapper(
            workspace_buffer,
            backend="fa2",
        )
        
        indptr = get_indptr_from_mask(video_mask, query)
        indices = get_indices_from_mask(video_mask, query)
        
        bsr_wrapper.plan(
            indptr=indptr,
            indices=indices,
            M=mask_map.video_token_num,
            N=mask_map.video_token_num,
            R=block_size,
            C=block_size,
            num_qo_heads=num_head,
            num_kv_heads=num_head,
            head_dim=hidden_dim,
            q_data_type=query.dtype,
            kv_data_type=key.dtype,
            o_data_type=query.dtype,
        )
        
        return FlashInferBackend(query, key, value, mask_map, pre_defined_mask, bsr_wrapper)
    elif backend == "sparse_sageattn":
        return SpargeSageAttnBackend(query, key, value, mask_map, video_mask, pre_defined_mask, block_size=block_size)
        
if __name__ == "__main__":
    query = torch.randn(1, 2, 4, 64).cuda()
    # mask = torch.tensor([
    #     [True, False, True, False],
    #     [False, True, False, True],
    #     [True, False, False, True],
    #     [False, True, True, False]
    # ], dtype=torch.bool)
    # indices = get_indices_from_mask(mask, query)
    # indptr = get_indptr_from_mask(mask, query)
    # print("Indices: ", indices)
    # print("Indptr: ", indptr)
    video_token_num = 3840 * 30
    num_frame = 30
    token_per_frame = video_token_num / num_frame
    padded_video_token_num = ((video_token_num + 1) // 128 + 1) * 128
    print("padded: ", padded_video_token_num)
    temporal_mask = gen_log_mask_shrinked(query, padded_video_token_num, video_token_num, num_frame, sparse_type="radial", decay_factor=1, model_type="hunyuan")
    plt.figure(figsize=(10, 8), dpi=500)

    plt.imshow(temporal_mask.cpu().numpy()[:, :], cmap='hot')
    plt.colorbar()
    plt.title("Temporal Mask")

    plt.savefig("temporal_mask.png",
                dpi=300,
                bbox_inches='tight',
                pad_inches=0.1)

    plt.close()
    # save the mask tensor
    torch.save(temporal_mask, "temporal_mask.pt")