vllm.model_executor.models.minimax_vl_01

class MiniMaxVL01DummyInputsBuilder(LlavaDummyInputsBuilder):
    pass

@MULTIMODAL_REGISTRY.register_processor(
    MiniMaxVL01MultiModalProcessor,
    info=MiniMaxVL01ProcessingInfo,
    dummy_inputs=MiniMaxVL01DummyInputsBuilder)
class MiniMaxVL01ForConditionalGeneration(nn.Module, SupportsMultiModal,
                                          SupportsPP):

    packed_modules_mapping = {
        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
        "gate_up_proj": ["gate_proj", "up_proj"]
    }

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("image"):
            return "<image>"

        raise ValueError("Only image modality is supported")

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
        super().__init__()

        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config

        self.config = config
        self.multimodal_config = multimodal_config

        # TODO: Optionally initializes this for supporting embeddings.
        self.vision_tower = init_vision_tower_for_llava(
            config,
            quant_config,
            require_post_norm=False,
            prefix=maybe_prefix(prefix, "vision_tower"))
        self.multi_modal_projector = MiniMaxVL01MultiModalProjector(
            vision_hidden_size=config.vision_config.hidden_size,
            text_hidden_size=config.text_config.hidden_size,
            projector_hidden_act=config.projector_hidden_act,
            multimodal_projector_bias=True,
            quant_config=quant_config,
            prefix=maybe_prefix(prefix, "multi_modal_projector"))
        self.image_newline = nn.Parameter(
            torch.empty(config.text_config.hidden_size))
        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            hf_config=config.text_config,
            prefix=maybe_prefix(prefix, "language_model"),
        )
        self.vision_feature_layer = config.vision_feature_layer
        self.vocab_size = config.text_config.vocab_size
        self.pad_token_id = -1
        if self.config.pad_token_id is not None:
            self.pad_token_id = self.config.pad_token_id

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors)

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
    ) -> torch.Tensor:
        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
        if multimodal_embeddings is not None \
            and len(multimodal_embeddings) != 0:
            inputs_embeds = merge_multimodal_embeddings(
                input_ids,
                inputs_embeds,
                multimodal_embeddings,
                self.config.image_token_index,
            )
        return inputs_embeds

    def get_language_model(self) -> torch.nn.Module:
        return self.language_model

    def _select_image_features(self, image_features: torch.Tensor, *,
                               strategy: str) -> torch.Tensor:
        if strategy == "default":
            return image_features[:, 1:]
        elif strategy == "full":
            return image_features

        raise ValueError(f"Unexpected select feature strategy: {strategy}")

    def _image_pixels_to_features(
        self,
        vision_tower: Union[CLIPVisionModel, SiglipVisionModel,
                            PixtralHFVisionModel],
        pixel_values: Union[torch.Tensor, list[torch.Tensor]],
    ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
        # NOTE: we skip the step to select the vision feature layer since
        # this is already done inside the vision tower
        image_features = tuple(vision_tower(p) for p in pixel_values)

        def select_features(leaf: torch.Tensor):
            return self._select_image_features(
                leaf,
                strategy=self.config.vision_feature_select_strategy,
            )

        return cast(
            Union[torch.Tensor, tuple[torch.Tensor, ...]],
            json_map_leaves(select_features, image_features),
        )

    # adapted from https://huggingface.co/MiniMaxAI/MiniMax-VL-01/blob/main/modeling_minimax_vl_01.py#L616-L631
    def pack_image_features(self, image_features: list[torch.Tensor],
                            image_sizes: torch.Tensor):
        new_image_features = []
        for image_idx, image_feature in enumerate(image_features):
            if image_feature.shape[0] > 1:
                base_image_feature = image_feature[0]
                image_feature = image_feature[1:]
                height = width = (self.config.vision_config.image_size //
                                  self.config.vision_config.patch_size)
                if height * width != base_image_feature.shape[0]:
                    raise ValueError(
                        "The number of patches is not consistent with "
                        "the image size.")
                num_patch_height, num_patch_width = get_anyres_image_grid_shape(
                    image_sizes[image_idx],
                    self.config.image_grid_pinpoints,
                    self.config.vision_config.image_size,
                )

                image_feature = image_feature.view(num_patch_height,
                                                   num_patch_width, height,
                                                   width, -1)
                image_feature = image_feature.permute(4, 0, 2, 1,
                                                      3).contiguous()
                image_feature = image_feature.flatten(1, 2).flatten(2, 3)
                image_feature = unpad_image(image_feature,
                                            image_sizes[image_idx])

                image_feature = torch.cat(
                    (
                        image_feature,
                        self.image_newline[:, None, None].expand(
                            *image_feature.shape[:-1], 1).to(
                                image_feature.dtype),
                    ),
                    dim=-1,
                )
                image_feature = image_feature.flatten(1, 2).transpose(0, 1)
                image_feature = torch.cat((base_image_feature, image_feature),
                                          dim=0)
            else:
                image_feature = image_feature[0]
                image_feature = torch.cat(
                    (image_feature,
                     self.image_newline[None].to(image_feature)),
                    dim=0)
            new_image_features.append(image_feature)
        return new_image_features

    def _process_image_pixels(
        self,
        inputs: MiniMaxVL01ImagePixelInputs,
    ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
        assert self.vision_tower is not None

        pixel_values = inputs["pixel_values"]
        return self._image_pixels_to_features(self.vision_tower, pixel_values)

    def _process_image_input(
        self,
        image_input: MiniMaxVL01ImageInputs,
    ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]:
        if image_input["type"] == "image_embeds":
            return image_input["data"]

        assert self.vision_tower is not None
        image_features = self._process_image_pixels(image_input)

        if isinstance(image_features, torch.Tensor):
            return self.multi_modal_projector(image_features)

        feature_sizes = [
            image_feature.shape[0] for image_feature in image_features
        ]

        image_embeds = self.multi_modal_projector(torch.cat(image_features))
        image_embeds = torch.split(image_embeds, feature_sizes)
        image_sizes = image_input.get("image_sizes")
        return self.pack_image_features(image_embeds, image_sizes)

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[MiniMaxVL01ImageInputs]:
        pixel_values = kwargs.pop("pixel_values", None)
        image_sizes = kwargs.pop("image_sizes", None)
        image_embeds = kwargs.pop("image_embeds", None)

        if pixel_values is None and image_embeds is None:
            return None

        if pixel_values is not None and image_sizes is not None:
            if not isinstance(pixel_values, (torch.Tensor, list)):
                raise ValueError("Incorrect type of pixel values. "
                                 f"Got type: {type(pixel_values)}")

            if not isinstance(image_sizes, (torch.Tensor, list)):
                raise ValueError("Incorrect type of image sizes. "
                                 f"Got type: {type(image_sizes)}")

            return MiniMaxVL01ImagePixelInputs(
                type="pixel_values",
                pixel_values=flatten_bn(pixel_values),
                image_sizes=flatten_bn(image_sizes, concat=True),
            )

        if image_embeds is not None:
            if not isinstance(image_embeds, (torch.Tensor, list)):
                raise ValueError("Incorrect type of image embeddings. "
                                 f"Got type: {type(image_embeds)}")

            return MiniMaxVL01ImageEmbeddingInputs(
                type="image_embeds",
                data=flatten_bn(image_embeds, concat=True),
            )

        raise AssertionError("This line should be unreachable.")

    def get_multimodal_embeddings(self,
                                  **kwargs: object) -> MultiModalEmbeddings:
        image_input = self._parse_and_validate_image_input(**kwargs)
        if image_input is None:
            return []

        return self._process_image_input(image_input)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs: object,
    ) -> Union[torch.Tensor, IntermediateTensors]:

        if intermediate_tensors is not None:
            inputs_embeds = None
        elif inputs_embeds is None:
            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
            inputs_embeds = self.get_input_embeddings(input_ids,
                                                      vision_embeddings)
            input_ids = None

        hidden_states = self.language_model.model(input_ids,
                                                  positions,
                                                  intermediate_tensors,
                                                  inputs_embeds=inputs_embeds)

        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
        return self.language_model.compute_logits(hidden_states,
                                                  sampling_metadata)

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights)

class MiniMaxVL01ImageEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - bn: Batch size * number of images
        - ifs: Image feature size
        - hs: Hidden size (must match language model backbone)
    """
    type: Literal["image_embeds"] = "image_embeds"
    data: Annotated[torch.Tensor, TensorShape("bn", "ifs", "hs")]

class MiniMaxVL01ImagePixelInputs(TensorSchema):
    """
    Dimensions:
        - bn: Batch size * number of images
        - np: Number of patches + 1
        - c: Number of channels (3)
        - h: Height
        - w: Width

    Note that `num_patches` may be different per batch and image,
    in which case the data is passed as a list instead of a batched tensor.
    """
    type: Literal["pixel_values"] = "pixel_values"
    pixel_values: Annotated[
        Union[torch.Tensor, list[torch.Tensor]],
        TensorShape("bn", "np", 3, "h", "w", dynamic_dims={"np", "h", "w"})]

    image_sizes: Annotated[Optional[torch.Tensor], TensorShape("bn", 2)]

class MiniMaxVL01MultiModalProcessor(
        BaseLlavaMultiModalProcessor[MiniMaxVL01ProcessingInfo]):

    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        processed_outputs = super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )

        pixel_values = processed_outputs.get("pixel_values")
        if pixel_values is not None:
            # Avoid padding since we need the output for each image to be
            # independent of other images for the cache to work correctly
            image_sizes = processed_outputs["image_sizes"]
            assert len(pixel_values) == len(image_sizes)

            processed_outputs["pixel_values"] = [
                p[:, :h, :w] for p, (h, w) in zip(pixel_values, image_sizes)
            ]

        return processed_outputs

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return {
            "pixel_values": MultiModalFieldConfig.batched("image"),
            "image_sizes": MultiModalFieldConfig.batched("image"),
            "image_embeds": MultiModalFieldConfig.batched("image"),
        }

class MiniMaxVL01MultiModalProjector(nn.Module):

    def __init__(self,
                 vision_hidden_size: int,
                 text_hidden_size: int,
                 projector_hidden_act: str,
                 multimodal_projector_bias: bool,
                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = ""):
        super().__init__()

        self.linear_1 = ColumnParallelLinear(vision_hidden_size,
                                             text_hidden_size,
                                             bias=multimodal_projector_bias,
                                             quant_config=quant_config,
                                             prefix=f"{prefix}.linear_1")
        self.act = get_act_fn(projector_hidden_act)
        self.linear_2 = RowParallelLinear(text_hidden_size,
                                          text_hidden_size,
                                          bias=multimodal_projector_bias,
                                          quant_config=quant_config,
                                          prefix=f"{prefix}.linear_2")

    def forward(self, image_features: torch.Tensor) -> torch.Tensor:
        hidden_states, _ = self.linear_1(image_features)
        hidden_states = self.act(hidden_states)
        hidden_states, _ = self.linear_2(hidden_states)
        return hidden_states