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# SPDX-License-Identifier: Apache-2.0
#
# 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.

from typing import List, Optional, Tuple

import torch

from cosmos_rl.utils.wfm.utils import DataType
from cosmos_rl.policy.model.wfm.networks.minimal_v4_dit import MiniTrainDIT


class MinimalV1LVGDiT(MiniTrainDIT):
    def __init__(self, *args, **kwargs):
        assert "in_channels" in kwargs, "in_channels must be provided"
        kwargs["in_channels"] += 1  # Add 1 for the condition mask
        super().__init__(*args, **kwargs)

    def forward(
        self,
        x_B_C_T_H_W: torch.Tensor,
        timesteps_B_T: torch.Tensor,
        crossattn_emb: torch.Tensor,
        condition_video_input_mask_B_C_T_H_W: Optional[torch.Tensor] = None,
        fps: Optional[torch.Tensor] = None,
        padding_mask: Optional[torch.Tensor] = None,
        data_type: Optional[DataType] = DataType.VIDEO,
        img_context_emb: Optional[torch.Tensor] = None,
        **kwargs,
    ) -> torch.Tensor | List[torch.Tensor] | Tuple[torch.Tensor, List[torch.Tensor]]:
        del kwargs

        if data_type == DataType.VIDEO:
            x_B_C_T_H_W = torch.cat(
                [
                    x_B_C_T_H_W,
                    condition_video_input_mask_B_C_T_H_W.type_as(x_B_C_T_H_W),
                ],
                dim=1,
            )
        else:
            B, _, T, H, W = x_B_C_T_H_W.shape
            x_B_C_T_H_W = torch.cat(
                [
                    x_B_C_T_H_W,
                    torch.zeros(
                        (B, 1, T, H, W),
                        dtype=x_B_C_T_H_W.dtype,
                        device=x_B_C_T_H_W.device,
                    ),
                ],
                dim=1,
            )
        return super().forward(
            x_B_C_T_H_W=x_B_C_T_H_W,
            timesteps_B_T=timesteps_B_T,
            crossattn_emb=crossattn_emb,
            fps=fps,
            padding_mask=padding_mask,
            data_type=data_type,
            img_context_emb=img_context_emb,
        )
