# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Iterable, Mapping
from typing import Annotated, Literal, TypeAlias

import torch
import torch.nn as nn
from transformers import BatchFeature, PretrainedConfig
from transformers.models.llava_next.modeling_llava_next import (
    get_anyres_image_grid_shape,
    unpad_image,
)

from vllm.config import VllmConfig
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import MultiModalFieldConfig
from vllm.sequence import IntermediateTensors
from vllm.utils.tensor_schema import TensorSchema, TensorShape

from .clip import CLIPVisionModel
from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .llava import (
    BaseLlavaMultiModalProcessor,
    LlavaDummyInputsBuilder,
    init_vision_tower_for_llava,
)
from .llava_next import LlavaNextProcessingInfo
from .pixtral import PixtralHFVisionModel
from .siglip import SiglipVisionModel
from .utils import (
    AutoWeightsLoader,
    init_vllm_registered_model,
    maybe_prefix,
)


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[
        torch.Tensor | list[torch.Tensor],
        TensorShape("bn", "np", 3, "h", "w", dynamic_dims={"np", "h", "w"}),
    ]

    image_sizes: Annotated[torch.Tensor | None, TensorShape("bn", 2)]
    # This should be in `(height, width)` format.


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")]


MiniMaxVL01ImageInputs: TypeAlias = (
    MiniMaxVL01ImagePixelInputs | MiniMaxVL01ImageEmbeddingInputs
)


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: QuantizationConfig | None = 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


class MiniMaxVL01DummyInputsBuilder(LlavaDummyInputsBuilder):
    pass


class MiniMaxVL01ProcessingInfo(LlavaNextProcessingInfo):
    def get_hf_config(self):  # Need to override the config type
        return self.ctx.get_hf_config(PretrainedConfig)

    def get_hf_processor(self, **kwargs: object):
        hf_processor = self.ctx.get_hf_processor(**kwargs)
        image_processor = hf_processor.image_processor
        image_processor.anyres_preprocess = image_processor.anyres_for_vllm_preprocess

        return hf_processor

    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
        return {"image": None}


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"),
        }


@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) -> str | None:
        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=quant_config,
            multimodal_config=multimodal_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_language_model(self) -> torch.nn.Module:
        return self.language_model

    def _image_pixels_to_features(
        self,
        vision_tower: CLIPVisionModel | SiglipVisionModel | PixtralHFVisionModel,
        pixel_values: torch.Tensor | list[torch.Tensor],
    ) -> 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
        feature_select_strategy = self.config.vision_feature_select_strategy
        return tuple(
            vision_tower(p, feature_select_strategy=feature_select_strategy)
            for p in pixel_values
        )

    # 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,
    ) -> 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,
    ) -> 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
    ) -> MiniMaxVL01ImageInputs | None:
        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:
            return MiniMaxVL01ImagePixelInputs(
                type="pixel_values",
                pixel_values=pixel_values,
                image_sizes=image_sizes,
            )

        if image_embeds is not None:
            return MiniMaxVL01ImageEmbeddingInputs(
                type="image_embeds",
                data=image_embeds,
            )

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

    def embed_multimodal(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: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs: object,
    ) -> torch.Tensor | IntermediateTensors:
        if intermediate_tensors is not None:
            inputs_embeds = None
        elif inputs_embeds is None:
            vision_embeddings = self.embed_multimodal(**kwargs)
            inputs_embeds = self.embed_input_ids(
                input_ids,
                vision_embeddings,
                is_multimodal=input_ids == self.config.image_token_index,
            )
            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,
    ) -> torch.Tensor | None:
        return self.language_model.compute_logits(hidden_states)

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