""" PyTorch Ovis model."""
from collections.abc import Iterable, Mapping
from functools import partial
from typing import Literal, Optional, TypedDict, Union

import torch
import torch.nn as nn
from transformers import BaseImageProcessor, BatchFeature, PretrainedConfig

from aphrodite.common.sequence import IntermediateTensors
from aphrodite.config import AphroditeConfig
from aphrodite.modeling.layers.linear import ReplicatedLinear
from aphrodite.modeling.models.ovis import (OvisImagePatchInputs,
                                            VisualEmbedding)
from aphrodite.modeling.models.siglip2navit import Siglip2NavitModel
from aphrodite.modeling.models.utils import (AutoWeightsLoader, flatten_bn,
                                             init_aphrodite_registered_model,
                                             maybe_prefix)
from aphrodite.modeling.sampling_metadata import SamplingMetadata
from aphrodite.multimodal import MULTIMODAL_REGISTRY
from aphrodite.multimodal.inputs import (MultiModalDataDict,
                                         MultiModalFieldConfig,
                                         MultiModalKwargsItems)
from aphrodite.multimodal.parse import ImageSize, MultiModalDataItems
from aphrodite.multimodal.processing import (BaseMultiModalProcessor,
                                             BaseProcessingInfo,
                                             PromptReplacement)
from aphrodite.multimodal.profiling import BaseDummyInputsBuilder
from aphrodite.quantization.base_config import QuantizationConfig
from aphrodite.transformers_utils.processors.ovis2_5 import Ovis2_5Processor

from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP

IMAGE_TOKEN = "<image>"
VIDEO_TOKEN = "<video>"
INDICATOR_IDS = [-301, -302, -303, -304]

IMAGE_PAD_TOKEN_MAP = {
    "gemma2": "<unused0>",
    "llama": "<|reserved_special_token_0|>",
    "qwen2": "<|image_pad|>",
    "qwen3": "<|image_pad|>",
}
IMAGE_PAD_TOKEN_ID_MAP = {
    "gemma2": 7,
    "llama": 128002,
    "qwen2": 151655,
    "qwen3": 151655,
}


class OvisVideoPatchInputs(TypedDict):
    type: Literal["video_patches"]
    flat_data: torch.Tensor
    """
    Shape:
    `(batch_size * num_patches, patch_size_x * patch_size_y * num_channels)`
    """

    indicator_tokens: torch.Tensor
    """
    Shape:
    `(batch_size * (num_patches + 1))`
    """

    patches_per_image: list[int]
    """
    List of number of total patches for each frame in the video.
    This is used to restore the first two dimensions of `flat_data`.
    """


def _ovis2_5_field_config():
    return dict(pixel_values=MultiModalFieldConfig.batched("image"),
                grids=MultiModalFieldConfig.batched("image"),
                indicator_tokens=MultiModalFieldConfig.batched("image"),
                video_pixel_values=MultiModalFieldConfig.batched("video"),
                video_indicator_tokens=MultiModalFieldConfig.batched("video"),
                video_grids=MultiModalFieldConfig.batched("video"))


class VisualTokenizer(torch.nn.Module):
    """
    VIT
    """

    def __init__(
        self,
        config: PretrainedConfig,
        visual_vocab_size: int,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        use_data_parallel: bool = False,
    ):
        super().__init__()
        self.config = config
        self.vit = self._init_backbone(
            config=config,
            quant_config=quant_config,
            prefix=f"{prefix}.vit",
            use_data_parallel=use_data_parallel,
        )
        # reserved tokens for INDICATOR_IDS
        head_dim = visual_vocab_size - len(INDICATOR_IDS)
        self.head = torch.nn.Sequential(
            ReplicatedLinear(
                self.config.hidden_size * self.config.hidden_stride**2,
                head_dim,
                bias=False,
                return_bias=False,
            ), torch.nn.LayerNorm(head_dim))

    def _init_backbone(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        use_data_parallel: bool = False,
    ):
        model_type = config.model_type
        if model_type == "siglip2_navit":
            return Siglip2NavitModel(config=config,
                                     quant_config=quant_config,
                                     prefix=prefix,
                                     use_data_parallel=use_data_parallel)
        raise ValueError(
            f"Unsupported visual tokenizer model_type: {model_type}")

    @property
    def dtype(self) -> torch.dtype:
        return next(self.head.parameters()).dtype

    @property
    def device(self) -> torch.device:
        return next(self.head.parameters()).device

    def tokenize(self, logits: torch.Tensor) -> torch.Tensor:
        tokens = torch.softmax(logits, dim=-1,
                               dtype=torch.float32).to(logits.dtype)
        return tokens

    def encode(self, pixel_values: torch.Tensor,
               grid_thws: torch.Tensor) -> torch.Tensor:
        features = self.vit(pixel_values, grid_thws)
        # refer to qwen2.5-vl patchmerger
        seq_len, _ = features.shape
        features = features.reshape(seq_len // (self.config.hidden_stride**2),
                                    -1)

        return features

    def forward(self, pixel_values: torch.Tensor,
                grid_thws: torch.Tensor) -> torch.Tensor:
        features = self.encode(pixel_values, grid_thws)
        logits = self.head(features)
        tokens = self.tokenize(logits)
        # tokens' shape is [#Token, VocabSize-4],
        # so padding with [#Token, 4], after which,
        # tokens' shape should become [#Token, VocabSize];
        tokens = torch.nn.functional.pad(
            tokens,
            (0, len(INDICATOR_IDS)),
            mode="constant",
            value=0,
        )
        return tokens


class Ovis2_5ProcessingInfo(BaseProcessingInfo):

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

    def get_hf_processor(self, **kwargs):
        vit_config = self.get_hf_config().vit_config
        return self.ctx.get_hf_processor(
            Ovis2_5Processor,
            image_pad_token=self.get_image_pad_token(),
            patch_size=vit_config.patch_size,
            hidden_stride=vit_config.hidden_stride,
            temporal_patch_size=vit_config.temporal_patch_size,
        )

    def get_image_pad_token(self) -> str:
        hf_text_config = self.get_hf_config().get_text_config()
        text_model_type = hf_text_config.model_type
        return IMAGE_PAD_TOKEN_MAP.get(text_model_type)

    def get_image_processor(self) -> BaseImageProcessor:
        return self.get_hf_processor().image_processor  # type: ignore

    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {"image": None, "video": 1}

    def get_image_size_with_most_features(self) -> ImageSize:
        # NOTE(myselvess): max_pixels 1792 * 1792 hardcoded in original code
        # TODO(myselvess): Be adjusted based on the max_pixels
        return ImageSize(width=1792, height=1792)

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int = 1,
    ) -> tuple[ImageSize, int]:
        hf_config = self.get_hf_config()
        vit_config = hf_config.vit_config
        patch_size = vit_config.patch_size
        temporal_patch_size = vit_config.temporal_patch_size
        # NOTE: Frames are padded to be divisible by `temporal_patch_size`
        # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py#L294
        padded_num_frames = num_frames + (-num_frames % temporal_patch_size)
        grid_t = max(padded_num_frames // temporal_patch_size, 1)
        grid_h = image_height // patch_size
        grid_w = image_width // patch_size
        num_patches = grid_t * grid_h * grid_w
        num_vision_tokens = num_patches
        return num_vision_tokens

    def get_max_image_tokens(self) -> int:
        target_width, target_height = self.get_image_size_with_most_features()
        return self.get_num_image_tokens(image_width=target_width,
                                         image_height=target_height)

    def _get_max_video_frames(self, max_tokens: int) -> int:
        target_width, target_height = self.get_image_size_with_most_features()
        num_frames = 0
        while True:
            next_num_frames = num_frames + 1
            next_max_tokens = self.get_num_video_tokens(
                image_width=target_width,
                image_height=target_height,
                num_frames=next_num_frames,
                image_processor=None,
            )
            if next_max_tokens > max_tokens:
                break
            num_frames = next_num_frames
        return num_frames

    def get_num_frames_with_most_features(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> int:
        max_images = mm_counts.get("image", 0)
        max_videos = mm_counts.get("video", 0)
        max_image_tokens = self.get_max_image_tokens() * max_images
        max_total_frames = self._get_max_video_frames(seq_len -
                                                      max_image_tokens)
        max_frames_per_video = max_total_frames // max(max_videos, 1)
        return max(max_frames_per_video, 1)

    def get_num_video_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        num_frames: int,
        image_processor: Optional[BaseImageProcessor],
    ) -> int:
        num_video_tokens = self.get_num_image_tokens(image_width=image_width,
                                                     image_height=image_height,
                                                     num_frames=num_frames)
        return num_video_tokens

    def get_max_video_tokens(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> int:
        target_width, target_height = self.get_image_size_with_most_features()
        return self.get_num_video_tokens(
            image_width=target_width,
            image_height=target_height,
            num_frames=self.get_num_frames_with_most_features(
                seq_len, mm_counts),
            image_processor=None,
        )


class Ovis2_5DummyInputsBuilder(BaseDummyInputsBuilder[Ovis2_5ProcessingInfo]):

    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_images = mm_counts.get("image", 0)
        num_videos = mm_counts.get("video", 0)
        return IMAGE_TOKEN * num_images + VIDEO_TOKEN * num_videos

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

        target_width, target_height = \
            self.info.get_image_size_with_most_features()
        target_num_frames = \
            self.info.get_num_frames_with_most_features(seq_len, mm_counts)
        mm_data = {
            "image":
            self._get_dummy_images(width=target_width,
                                   height=target_height,
                                   num_images=num_images),
            "video":
            self._get_dummy_videos(
                width=target_width,
                height=target_height,
                num_frames=target_num_frames,
                num_videos=num_videos,
            )
        }
        return mm_data


class Ovis2_5MultiModalProcessor(BaseMultiModalProcessor[Ovis2_5ProcessingInfo]
                                 ):

    def visual_indicators_to_visual_tokens(
        self,
        visual_indicators: list[int],
    ) -> list[int]:
        """
        Filter image indicators placeholders and convert them to corresponding 
        tokens in visual tokenizer.
        """
        hf_config = self.info.get_hf_config()
        vte_vocab_size = hf_config.visual_vocab_size
        return [
            vte_vocab_size - len(INDICATOR_IDS) + abs(x + 300) - 1
            for x in visual_indicators if x < -300
        ]

    def _call_hf_processor(
        self,
        prompt: str,
        mm_data: Mapping[str, object],
        mm_kwargs: Mapping[str, object],
        tok_kwargs: Mapping[str, object],
    ) -> BatchFeature:
        if not mm_data:
            # Avoid warning from HF logger for text-only input
            tokenizer = self.info.get_tokenizer()
            prompt_ids = tokenizer.encode(prompt, add_special_tokens=False)
            return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")

        processed_outputs = super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )
        hf_processor = self.info.get_hf_processor()

        if "videos" in mm_data:
            visual_indicators = [
                hf_processor.construct_visual_indicators((1, 1, 1), True)
                for grid in processed_outputs["video_grids"]
            ]
            indicator_tokens = [
                self.visual_indicators_to_visual_tokens(indicator)
                for indicator in visual_indicators
            ]
            processed_outputs["video_indicator_tokens"] = indicator_tokens
        if "images" in mm_data:
            visual_indicators = [
                hf_processor.construct_visual_indicators((1, 1, 1), False)
                for grid in processed_outputs["grids"]
            ]
            indicator_tokens = [
                self.visual_indicators_to_visual_tokens(indicator)
                for indicator in visual_indicators
            ]

            processed_outputs["indicator_tokens"] = indicator_tokens
        return processed_outputs

    def _apply_hf_processor_tokens_only(
        self,
        prompt_tokens: list[int],
    ) -> list[int]:

        return prompt_tokens

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return _ovis2_5_field_config()

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> list[PromptReplacement]:

        def get_replacement_ovis(item_idx, modality: str):
            if modality == "image":
                out_item = out_mm_kwargs["image"][item_idx]
                grid = out_item["grids"].data
            elif modality == "video":
                out_item = out_mm_kwargs["video"][item_idx]
                grid = out_item["video_grids"].data
            hf_processor = self.info.get_hf_processor()
            return hf_processor.construct_visual_placeholders(grid[0], )

        return [
            PromptReplacement(
                modality=modality,
                target=IMAGE_TOKEN if modality == "image" else VIDEO_TOKEN,
                replacement=partial(get_replacement_ovis, modality=modality),
            ) for modality in ("image", "video")
        ]


@MULTIMODAL_REGISTRY.register_processor(Ovis2_5MultiModalProcessor,
                                        info=Ovis2_5ProcessingInfo,
                                        dummy_inputs=Ovis2_5DummyInputsBuilder)
class Ovis2_5(nn.Module, SupportsMultiModal, SupportsPP):

    def __init__(self, *, aphrodite_config: AphroditeConfig, prefix: str = ""):
        super().__init__()
        config = aphrodite_config.model_config.hf_config
        quant_config = aphrodite_config.quant_config

        self.config: PretrainedConfig = config
        self.llm = init_aphrodite_registered_model(
            aphrodite_config=aphrodite_config.with_hf_config(config.text_config),
            prefix=maybe_prefix(prefix, "llm"),
        )

        self.visual_tokenizer = VisualTokenizer(
            config=config.vit_config,
            visual_vocab_size=config.visual_vocab_size,
            quant_config=quant_config,
            prefix=f"{prefix}.visual_tokenizer",
        )

        self.vte = VisualEmbedding(config.visual_vocab_size,
                                   config.hidden_size)

        text_model_type = self.config.get_text_config().model_type
        self.image_pad_token_id = IMAGE_PAD_TOKEN_ID_MAP[text_model_type]

        self.make_empty_intermediate_tensors = (
            self.get_language_model().make_empty_intermediate_tensors)

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[OvisImagePatchInputs]:
        pixel_values = kwargs.pop("pixel_values", None)
        indicator_tokens = kwargs.pop("indicator_tokens", None)
        grids = kwargs.pop("grids", None)
        if pixel_values is None and indicator_tokens is None:
            return None

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

            return OvisImagePatchInputs(
                type="image_patches",
                flat_data=flatten_bn(flatten_bn(pixel_values), concat=True),
                patches_per_image=[
                    x.shape[0] // (self.config.vit_config.hidden_stride**2)
                    for x in flatten_bn(pixel_values)
                ],
                indicator_tokens=flatten_bn(flatten_bn(indicator_tokens),
                                            concat=True),
                grids=flatten_bn(flatten_bn(grids), concat=True),
            )

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

    def _parse_and_validate_video_input(
            self, **kwargs: object) -> Optional[OvisImagePatchInputs]:
        pixel_values = kwargs.pop("video_pixel_values", None)
        indicator_tokens = kwargs.pop("video_indicator_tokens", None)
        grids = kwargs.pop("video_grids", None)
        if pixel_values is None and indicator_tokens is None:
            return None

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

            return OvisVideoPatchInputs(
                type="video_patches",
                flat_data=flatten_bn(flatten_bn(pixel_values), concat=True),
                patches_per_image=[
                    x.shape[0] // (self.config.vit_config.hidden_stride**2)
                    for x in flatten_bn(pixel_values)
                ],
                indicator_tokens=flatten_bn(flatten_bn(indicator_tokens),
                                            concat=True),
                grids=flatten_bn(flatten_bn(grids), concat=True),
            )

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

    def _process_image_input(
        self, image_input: Union[OvisImagePatchInputs, OvisVideoPatchInputs]
    ) -> MultiModalEmbeddings:
        image_patches_flat = image_input["flat_data"]
        patches_per_image = image_input["patches_per_image"]
        indicator_tokens = image_input["indicator_tokens"]
        grid_thws = image_input["grids"]

        indicator_per_image = list(
            map(lambda x: 2 if x > 1 else x + 2, patches_per_image))

        target_dtype = self.visual_tokenizer.dtype
        visual_tokens = self.visual_tokenizer(
            image_patches_flat.to(target_dtype), grid_thws)

        visual_embeds = self.vte(visual_tokens)  # 1:1 numeric eq.
        indicator_embeds = self.vte(indicator_tokens)

        visual_embeds_per_image = visual_embeds.split(patches_per_image, dim=0)
        indicator_embeds_per_image = indicator_embeds.split(
            indicator_per_image)

        vision_embeddings = []
        for indicator, visual in zip(indicator_embeds_per_image,
                                     visual_embeds_per_image):
            vision_embeddings_per_image = []
            visual = visual.unsqueeze(0)
            for i in range(visual.shape[0]):
                vision_embeddings_per_image.append(
                    torch.cat([indicator[i:i + 1], visual[i]], dim=0))
            vision_embeddings_per_image.append(indicator[i + 1:])
            vision_embeddings.append(
                torch.cat(vision_embeddings_per_image, dim=0))
        return tuple(vision_embeddings)

    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
        modalities = {}

        # Preserve the order of modalities if there are multiple of them
        # from the order of kwargs.
        for input_key in kwargs:
            if input_key in ("pixel_values", "indicator_tokens",
                             "grids") and "images" not in modalities:
                modalities["images"] = self._parse_and_validate_image_input(
                    **kwargs)
            if input_key in ("video_pixel_values", "video_indicator_tokens",
                             "video_grids") and "videos" not in modalities:
                modalities["videos"] = self._parse_and_validate_video_input(
                    **kwargs)

        return modalities

    def get_multimodal_embeddings(self,
                                  **kwargs: object) -> MultiModalEmbeddings:

        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
        if not modalities:
            return []

        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
        # NOTE: It is important to iterate over the keys in this dictionary
        # to preserve the order of the modalities.
        for modality in modalities:
            if modality == "images":
                image_input = modalities["images"]
                vision_embeddings = self._process_image_input(image_input)
                multimodal_embeddings += vision_embeddings
            if modality == "videos":
                video_input = modalities["videos"]
                video_embeddings = self._process_image_input(video_input)
                multimodal_embeddings += video_embeddings

        return multimodal_embeddings

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
    ) -> torch.Tensor:
        inputs_embeds = self.llm.get_input_embeddings(input_ids)
        if multimodal_embeddings is not None:
            tmp = torch.concat(multimodal_embeddings, dim=0)
            inputs_embeds[input_ids == self.image_pad_token_id] = tmp
        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,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        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

        # up until here we have a inputs_embeds 100% numerical identity
        # between the OG HF Transformers implementation and ours
        hidden_states = self.llm(
            input_ids=input_ids,
            positions=positions,
            intermediate_tensors=intermediate_tensors,
            inputs_embeds=inputs_embeds,
        )
        return hidden_states

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

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

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