# SPDX-License-Identifier: Apache-2.0

# adapted from https://github.com/deepseek-ai/DeepSeek-VL2/blob/faf18023f24b962b32d9f0a2d89e402a8d383a78/deepseek_vl2/models/modeling_deepseek_vl_v2.py
"""Inference-only Deepseek-VL2 model compatible with HuggingFace weights."""
import math
from collections.abc import Iterable, Mapping, Sequence
from typing import Literal, Optional, TypedDict, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from transformers import BatchFeature

from vllm.config import VllmConfig
from vllm.model_executor import SamplingMetadata
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.model_loader.utils import set_default_torch_dtype
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                    MultiModalKwargs, NestedTensors)
from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
                                   ImageSize, MultiModalDataItems)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                        BaseProcessingInfo, MultiModalHashes,
                                        PromptReplacement, PromptUpdate)
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs.deepseek_vl2 import (DeepseekVLV2Config,
                                                          MlpProjectorConfig,
                                                          VisionEncoderConfig)
from vllm.transformers_utils.processors.deepseek_vl2 import (
    DeepseekVLV2Processor)
from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from vllm.utils import is_list_of

from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
                    init_vllm_registered_model, maybe_prefix,
                    merge_multimodal_embeddings)

# The image token id may be various
_IMAGE_TOKEN = "<image>"


class DeepseekVL2ImagePixelInputs(TypedDict):
    type: Literal["pixel_values"]
    data: Union[torch.Tensor, list[torch.Tensor]]
    """
    Shape: `(batch_size * num_images, num_channels, height, width)`
    """
    images_spatial_crop: torch.Tensor
    """
    Shape: `(batch_size * num_images, 2)`
    """


class DeepseekVL2VImageEmbeddingInputs(TypedDict):
    type: Literal["image_embeds"]
    data: Union[torch.Tensor, list[torch.Tensor]]
    """Shape: `(batch_size * num_images, image_feature_size, hidden_size)`

    `hidden_size` must match the hidden size of language model backbone.
    """


DeepseekVL2ImageInputs = Union[DeepseekVL2ImagePixelInputs,
                               DeepseekVL2VImageEmbeddingInputs]


class MlpProjector(nn.Module):

    def __init__(self, cfg: MlpProjectorConfig):

        super().__init__()

        self.cfg = cfg
        assert not cfg.token_pooling, (
            "Token pooling is not supported currently.")

        if cfg.projector_type == "downsample_mlp_gelu":
            mlp_depth = cfg.depth
            mlp_ratio = cfg.mlp_ratio
            modules = [
                nn.Linear(
                    cfg.input_dim * cfg.downsample_ratio *
                    cfg.downsample_ratio, cfg.n_embed * mlp_ratio)
            ]
            for _ in range(1, mlp_depth - 1):
                modules.append(nn.GELU())
                modules.append(
                    nn.Linear(cfg.n_embed * mlp_ratio,
                              cfg.n_embed * mlp_ratio))
            modules.append(nn.GELU())
            modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed))
            modules = nn.Sequential(*modules)

        else:
            raise NotImplementedError(
                f"Unsupported projector type: {cfg.projector_type}")

        self.layers = modules

    def forward(self, x):
        bs, hw, input_dim = x.shape
        h = w = int((hw)**0.5)
        """compute padding"""
        if h % self.cfg.downsample_ratio:
            pad = self.cfg.downsample_ratio - h % self.cfg.downsample_ratio
        else:
            pad = 0
        x = x.reshape(bs, h, w, input_dim)
        if pad > 0:
            x = F.pad(x, (0, 0, 0, pad, 0, pad), "constant", 0)
        """4 to 1 concat"""
        x = x.permute(0, 3, 1, 2)  # B, C, H, W
        x = F.unfold(x,
                     kernel_size=self.cfg.downsample_ratio,
                     stride=self.cfg.downsample_ratio,
                     padding=0)  # B, C*4, HW // 4
        x = x.permute(0, 2, 1)

        return self.layers(x)


class DeepseekVL2ProcessingInfo(BaseProcessingInfo):

    def get_hf_config(self):
        return self.ctx.get_hf_config(DeepseekVLV2Config)

    def get_hf_processor(self, **kwargs: object):
        return self.ctx.get_hf_processor(DeepseekVLV2Processor, **kwargs)

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

    def get_num_image_tokens(self,
                             *,
                             image_width: int,
                             image_height: int,
                             cropping: bool = True) -> int:
        hf_processor = self.get_hf_processor()
        image_size = hf_processor.image_size
        patch_size = hf_processor.patch_size
        downsample_ratio = hf_processor.downsample_ratio

        if cropping:
            best_width, best_height = hf_processor.select_best_resolution(
                (image_width, image_height))
            num_width_tiles, num_height_tiles = (best_width // image_size,
                                                 best_height // image_size)
        else:
            num_width_tiles = num_height_tiles = 1

        h = w = math.ceil((image_size // patch_size) / downsample_ratio)

        global_views_tokens = h * (w + 1)
        local_views_tokens = (num_height_tiles * h) * (num_width_tiles * w + 1)
        return global_views_tokens + local_views_tokens + 1

    def get_image_size_with_most_features(self) -> ImageSize:
        hf_config = self.get_hf_config()
        candidate_resolutions = hf_config.candidate_resolutions
        height, width = max(candidate_resolutions,
                            key=lambda x: self.get_num_image_tokens(
                                image_width=x[1], image_height=x[0]))
        return ImageSize(width=width, height=height)


class DeepseekVL2DummyInputsBuilder(
        BaseDummyInputsBuilder[DeepseekVL2ProcessingInfo]):

    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_images = mm_counts.get("image", 0)

        processor = self.info.get_hf_processor()
        image_token = processor.image_token

        return image_token * num_images

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> MultiModalDataDict:
        num_images = mm_counts.get("image", 0)

        max_image_size = self.info.get_image_size_with_most_features()

        return {
            "image":
            self._get_dummy_images(width=max_image_size.width,
                                   height=max_image_size.height,
                                   num_images=num_images)
        }


class DeepseekVL2MultiModalProcessor(
        BaseMultiModalProcessor[DeepseekVL2ProcessingInfo]):

    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        if mm_data:
            processed_outputs = self.info.ctx.call_hf_processor(
                self.info.get_hf_processor(**mm_kwargs),
                dict(prompt=prompt, **mm_data),
                mm_kwargs,
            )
            target_dtype = self.info.ctx.model_config.dtype
            pixel_values = processed_outputs.pop("pixel_values").to(
                target_dtype)
            # split pixel values into patches corresponding to each image
            images_spatial_crop = processed_outputs["images_spatial_crop"]
            patches_per_image = [
                x.prod().item() + 1 for x in images_spatial_crop
            ]
            pixel_values = pixel_values.split(patches_per_image)
            processed_outputs["pixel_values"] = pixel_values
        else:
            tokenizer = self.info.get_tokenizer()
            processed_outputs = tokenizer(prompt,
                                          add_special_tokens=True,
                                          return_tensors="pt")

        return processed_outputs

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

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargs,
    ) -> Sequence[PromptUpdate]:
        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)

        image_token_id = hf_processor.image_token_id
        assert isinstance(image_token_id, int)

        def get_replacement_deepseek_vl2(item_idx: int):
            images = mm_items.get_items(
                "image", (ImageEmbeddingItems, ImageProcessorItems))

            if isinstance(images, ImageEmbeddingItems):
                num_image_tokens = images.get_feature_size(item_idx)
            else:
                image_size = images.get_image_size(item_idx)

                num_image_tokens = self.info.get_num_image_tokens(
                    image_width=image_size.width,
                    image_height=image_size.height,
                    cropping=len(images) <= 2,
                )
            return [image_token_id] * num_image_tokens

        return [
            PromptReplacement(
                modality="image",
                target=[image_token_id],
                replacement=get_replacement_deepseek_vl2,
            )
        ]

    def _cached_apply_hf_processor(
        self,
        prompt: Union[str, list[int]],
        mm_data_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        *,
        return_mm_hashes: bool,
    ) -> tuple[list[int], MultiModalKwargs, Optional[MultiModalHashes], bool]:
        # The processor logic is different for len(images) <= 2 vs > 2
        # Since the processing cache assumes that the processor output is
        # invariant of how many images are passed per prompt, we only
        # perform caching for the most common case
        if mm_data_items.get_count("image", strict=False) > 2:
            return self._apply_hf_processor(
                prompt=prompt,
                mm_data_items=mm_data_items,
                hf_processor_mm_kwargs=hf_processor_mm_kwargs,
                return_mm_hashes=return_mm_hashes,
            )

        return super()._cached_apply_hf_processor(
            prompt=prompt,
            mm_data_items=mm_data_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
            return_mm_hashes=return_mm_hashes,
        )


@MULTIMODAL_REGISTRY.register_processor(
    DeepseekVL2MultiModalProcessor,
    info=DeepseekVL2ProcessingInfo,
    dummy_inputs=DeepseekVL2DummyInputsBuilder)
class DeepseekVLV2ForCausalLM(nn.Module, SupportsMultiModal, SupportsPP):

    hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
        "language.": "language_model.",
    })

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config: DeepseekVLV2Config = 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

        self.vision_config = config.vision_config
        self.projector_config = config.projector_config
        self.text_config = config.text_config

        model_config = vllm_config.model_config
        tokenizer = cached_tokenizer_from_config(model_config)
        self.image_token_id = tokenizer.vocab[_IMAGE_TOKEN]

        self.vision = self._init_vision_module(self.vision_config,
                                               quant_config,
                                               maybe_prefix(prefix, "vision"))

        self.projector = MlpProjector(self.projector_config)
        self.tile_tag = config.tile_tag
        self.global_view_pos = config.global_view_pos

        # special token for image token sequence format
        embed_std = 1 / torch.sqrt(
            torch.tensor(self.projector_config.n_embed, dtype=torch.float32))
        if self.tile_tag == "2D":
            # <|view_separator|>, <|\n|>
            self.image_newline = nn.Parameter(
                torch.randn(self.projector_config.n_embed) * embed_std)
            # This is a typo in original implementation
            self.view_seperator = nn.Parameter(
                torch.randn(self.projector_config.n_embed) * embed_std)
        else:
            raise ValueError(
                f"Only 2D tile_tag is supported currently, got: {self.tile_tag}"
            )

        if self.text_config.topk_method == "noaux_tc":
            architectures = ["DeepseekV3ForCausalLM"]
        elif not self.text_config.use_mla:
            architectures = ["DeepseekForCausalLM"]
        else:
            architectures = ["DeepseekV2ForCausalLM"]

        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            hf_config=self.text_config,
            prefix=maybe_prefix(prefix, "language"),
            architectures=architectures,
        )

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors)

    def _init_vision_module(
        self,
        vision_config: VisionEncoderConfig,
        quant_config: Optional[QuantizationConfig],
        prefix: str = "",
    ) -> nn.Module:
        # TODO: refactor vision model through timm wrapper from transformers
        try:
            import timm
        except ImportError:
            raise ImportError("Please install timm") from ImportError

        with set_default_torch_dtype(torch.float16):
            model = timm.create_model(
                "vit_so400m_patch14_siglip_384.webli",
                pretrained=False,
                num_classes=0,
                dynamic_img_size=True,
                dynamic_img_pad=True,
            )

        model = model.to(dtype=torch.get_default_dtype())
        return model

    def _validate_pixel_values(
        self, data: Union[torch.Tensor, list[torch.Tensor]]
    ) -> Union[torch.Tensor, list[torch.Tensor]]:

        h = w = self.vision_config.image_size
        expected_dims = (3, h, w)

        def _validate_shape(d: torch.Tensor):
            actual_dims = tuple(d.shape[1:])

            if actual_dims != expected_dims:
                expected_expr = ("num_patches", *map(str, expected_dims))
                raise ValueError(
                    "The expected shape of pixel values per image per batch "
                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")

        for d in data:
            _validate_shape(d)

        return data

    def _validate_images_spatial_crop(
        self, data: Union[torch.Tensor, list[torch.Tensor]]
    ) -> Union[torch.Tensor, list[torch.Tensor]]:
        expected_dims = 2

        def _validate_shape(d: torch.Tensor):
            actual_dims = d.size(-1)

            if actual_dims != expected_dims:
                expected_expr = str(expected_dims)
                raise ValueError(
                    f"The expected shape of image sizes per image per batch "
                    f"is {expected_expr}. You supplied {tuple(d.shape)}.")

        for d in data:
            _validate_shape(d)

        return data

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[DeepseekVL2ImageInputs]:
        pixel_values = kwargs.pop("pixel_values", None)
        images_spatial_crop = kwargs.pop("images_spatial_crop", 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:
            if not isinstance(pixel_values, (torch.Tensor, list)):
                raise ValueError("Incorrect type of pixel values. "
                                 f"Got type: {type(pixel_values)}")

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

            return DeepseekVL2ImagePixelInputs(
                type="pixel_values",
                data=self._validate_pixel_values(flatten_bn(pixel_values)),
                images_spatial_crop=self._validate_images_spatial_crop(
                    flatten_bn(images_spatial_crop, 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 DeepseekVL2VImageEmbeddingInputs(
                type="image_embeds",
                data=flatten_bn(image_embeds),
            )

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

    def _pixel_values_to_embedding(
        self,
        pixel_values: NestedTensors,
        images_spatial_crop: torch.Tensor,
    ) -> NestedTensors:
        # Pixel_values: n_image * batch_size * [patch_per_img, 3, height, width]
        total_tiles = [x for x in pixel_values]

        # [batch_all_tiles, 3, height, width]
        total_tiles = torch.cat(total_tiles, dim=0)

        # [batch_all_tiles, vit_seq_len, c]
        images_feature = self.vision.forward_features(total_tiles)

        # [batch_all_tiles, hw, D]
        images_embeds = self.projector(images_feature)

        _, hw, n_dim = images_embeds.shape
        h = w = int(hw**0.5)

        # fill image token based on self.tile_tag & self.global_view_pos
        tile_index = 0
        vision_embeddings = []
        for jdx in range(images_spatial_crop.size(0)):
            # extra global & local features
            num_width_tiles, num_height_tiles = images_spatial_crop[jdx]
            if num_width_tiles == 0 or num_height_tiles == 0:
                break
            num_tiles_in_image = num_width_tiles * num_height_tiles

            # [hw, D]
            global_features = images_embeds[tile_index]

            # [num_height_tiles * num_width_tiles, hw, D]
            local_features = images_embeds[tile_index + 1:tile_index + 1 +
                                           num_tiles_in_image]
            tile_index += num_tiles_in_image + 1

            # format global and local features
            # ----------------- global view add newline -----------------
            # [hw, D] -> [h, w, D]
            global_features = global_features.view(h, w, n_dim)

            # [D]     -> [h, 1, D]
            new_lines_in_global = repeat(self.image_newline, "d -> h 1 d", h=h)

            # cat([h, w, D], [h, 1, D], dim=1) -> [h, w + 1, D]
            global_features = torch.cat([global_features, new_lines_in_global],
                                        dim=1)

            # [h, w + 1, D] -> [h * (w + 1), D]
            global_features = global_features.view(-1, n_dim)

            # ----------------- local view add newline -----------------
            # [num_height_tiles * num_width_tiles, h * w, D] ->
            # [num_height_tiles * h, num_width_tiles * w, D]
            local_features = rearrange(local_features,
                                       "(th tw) (h w) d -> (th h) (tw w) d",
                                       th=num_height_tiles,
                                       tw=num_width_tiles,
                                       h=h,
                                       w=w)

            # [D] -> [num_height_tiles * h, 1, D]
            new_lines_in_local = repeat(self.image_newline,
                                        "d -> (th h) 1 d",
                                        th=num_height_tiles,
                                        h=h)

            # [num_height_tiles * h, num_width_tiles * w + 1, D]
            local_features = torch.cat([local_features, new_lines_in_local],
                                       dim=1)

            # [num_height_tiles * h, num_width_tiles * w + 1, D]
            #   --> [(num_height_tiles * h) * (num_width_tiles * w + 1), D]
            local_features = local_features.view(-1, n_dim)

            # merge global and local tiles
            if self.global_view_pos == "head":
                global_local_features = torch.cat([
                    global_features,
                    self.view_seperator[None, :],
                    local_features,
                ])
            else:
                global_local_features = torch.cat([
                    local_features,
                    self.view_seperator[None, :],
                    global_features,
                ])

            vision_embeddings.append(global_local_features)
        return vision_embeddings

    def _process_image_input(
            self, image_input: DeepseekVL2ImageInputs) -> torch.Tensor:
        if image_input["type"] == "image_embeds":
            image_data = image_input["data"]
            if is_list_of(image_data, torch.Tensor):
                # it's already a list of tensors
                return image_data
            if len(image_data.shape) == 3:
                # 3D tensor
                return list(torch.unbind(image_data, dim=0))
            raise ValueError(
                "We expect batched 2D tensors; "
                "this can be either a list of 2D tensors or a single 3D tensor."
            )

        pixel_values = image_input["data"]
        images_spatial_crop = image_input["images_spatial_crop"]

        return self._pixel_values_to_embedding(
            pixel_values=pixel_values, images_spatial_crop=images_spatial_crop)

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

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

    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:
            inputs_embeds = merge_multimodal_embeddings(
                input_ids, inputs_embeds, multimodal_embeddings,
                self.image_token_id)
        return inputs_embeds

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

        if intermediate_tensors is not None:
            inputs_embeds = None

        # NOTE: In v1, inputs_embeds is always generated at model runner, this
        # condition is for v0 compatibility
        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(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)
        autoloaded_weights = loader.load_weights(weights,
                                                 mapper=self.hf_to_vllm_mapper)
        return autoloaded_weights
