#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch


def _generate_attn_mask(max_seq_len, dtype):
    # Construct lower triangle matrix.
    mask_flag = torch.ones((max_seq_len, max_seq_len),
                           dtype=torch.bool).tril_()
    # Create upper triangle matrix used to mark mask positions.
    mask_flag = ~mask_flag
    # Currently for fp16 dtype, the mask value should be set to -inf.
    # TODO: Eliminate this part in the future.
    mask_value = float('-inf') if dtype == torch.float16 else 1
    attn_mask = torch.zeros(size=(max_seq_len, max_seq_len), dtype=dtype) \
        .masked_fill_(mask_flag, mask_value)
    return attn_mask


class AttentionMaskBuilder:

    def __init__(
        self,
        max_seq_len: int,
        dtype: torch.dtype,
        device: torch.device = None,
    ):
        # NOTE: The device argument specifies the target NPU
        # to be used for the newly added FIA operator.
        # Only pass this parameter when using the new FIA operator.

        attn_mask = _generate_attn_mask(max_seq_len, dtype)

        self._seq_len_cached = attn_mask.shape[0]
        self.attn_mask_cache = attn_mask
        self.device = device
        self.pooling_mask = None
        assigned_mask_dim = 2048
        self.chunked_prefill_attn_mask = torch.triu(
            torch.ones(assigned_mask_dim, assigned_mask_dim),
            diagonal=1).to(torch.int8).to(device)

    @staticmethod
    def get_mask_scale_factor(dtype: torch.dtype = torch.float16):
        if dtype == torch.float16:
            mask_scale_factor = 1
        elif dtype == torch.bfloat16:
            mask_scale_factor = -10000
        else:
            raise ValueError(
                "The current operation now only supports data types: torch.float16 and "
                "torch.bfloat16. Please ensure the input is of one of these types."
            )
        return mask_scale_factor

    def get_attn_mask(self, max_seq_len: int, dtype: torch.dtype,
                      device: torch.device):
        self._update_attn_cache(max_seq_len, dtype)
        return self.attn_mask_cache[:max_seq_len, :max_seq_len].contiguous(
        ).to(device, non_blocking=True)

    def get_pooling_mask(self, device):
        if self.pooling_mask is None:
            # the compressed attention mask for npu_fusion_attention sparse mode 4
            self.pooling_mask = torch.triu(torch.ones(
                2048, 2048), diagonal=1).to(torch.bool).to(device,
                                                           non_blocking=True)
        return self.pooling_mask

    def get_splitfuse_attn_mask(
        self,
        seq_lens: torch.Tensor = None,
        position: torch.Tensor = None,
        dtype: torch.dtype = None,
        device: torch.device = None,
    ) -> torch.Tensor:
        return self.chunked_prefill_attn_mask

    def _update_attn_cache(self, seqlen: int, dtype: torch.dtype):
        if seqlen > self._seq_len_cached:
            self._seq_len_cached = seqlen
            self.attn_mask_cache = _generate_attn_mask(seqlen, dtype)
        if self.attn_mask_cache.dtype != dtype:
            self.attn_mask_cache = self.attn_mask_cache.to(dtype)