#
# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
#
# 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
import torch.nn as nn
import torch.nn.functional as F
from vllm.model_executor.models.kimi_k25_vit import (
    Learnable2DInterpPosEmbDivided_fixed,
    MoonViT3dPretrainedModel,
    get_rope_shape_decorate,
)

from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type


@get_rope_shape_decorate
def get_rope_shape(org, interpolation_mode, shape):
    return (
        F.interpolate(
            org.permute((2, 0, 1)).unsqueeze(0),
            size=shape,
            mode=interpolation_mode,
        )
        .squeeze(0)
        .permute((1, 2, 0))
        .flatten(end_dim=1)
    )


class AscendLearnable2DInterpPosEmbDivided_fixed(nn.Module):
    def forward(self, x: torch.Tensor, grid_thws: torch.Tensor | list) -> torch.Tensor:
        pos_embs = []
        if isinstance(grid_thws, torch.Tensor):
            grid_list = grid_thws.tolist()
        else:
            grid_list = grid_thws

        for t, h, w in grid_list:
            assert t <= self.num_frames, (
                f"[vllm-ascend/patch_kimi_k25] Invalid frame count. t={t}, num_frames={self.num_frames}"
            )
            if (h, w) == self.weight.shape[:-1]:
                pos_emb_2d = self.weight.flatten(end_dim=1)
            else:
                pos_emb_2d = get_rope_shape(
                    self.weight,
                    interpolation_mode=self.interpolation_mode,
                    shape=(h, w),
                )

            if t == 1:
                pos_emb_3d = pos_emb_2d
            else:
                pos_emb_3d = pos_emb_2d.unsqueeze(0).repeat(t, 1, 1) + self.time_weight[0:t]

            pos_embs.append(pos_emb_3d.reshape(-1, pos_emb_3d.shape[-1]))

        out = x + torch.cat(pos_embs)
        return out


Learnable2DInterpPosEmbDivided_fixed.forward = AscendLearnable2DInterpPosEmbDivided_fixed.forward


# Patch MoonViT3dPretrainedModel.to() to ignore the `dtype` argument.
# When KimiK25ForConditionalGeneration.__init__ calls:
#     self.vision_tower = self.vision_tower.to(device=..., dtype=model_config.dtype)
# the `dtype=model_config.dtype` (e.g. bf16) would overwrite the fp8 parameters
# created by the Ascend quantization scheme, causing a dtype mismatch later
# in weight_loader when the checkpoint's fp8 weights are loaded.
if get_ascend_device_type() == AscendDeviceType.A5:
    _original_moonvit_to = MoonViT3dPretrainedModel.to

    def _patched_moonvit_to(self, *args, **kwargs):
        # Filter out dtype from positional arguments and remove from kwargs
        # to prevent overriding quantized weight dtypes on A5.
        new_args = tuple(a for a in args if not isinstance(a, torch.dtype))
        kwargs.pop("dtype", None)
        return _original_moonvit_to(self, *new_args, **kwargs)

    MoonViT3dPretrainedModel.to = _patched_moonvit_to