# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2025 Bytedance Ltd. and/or its affiliates.
"""Inference-only BAGEL model compatible with HuggingFace weights.

BAGEL is a unified multimodal model for image understanding and generation.
For vLLM, we focus on the image understanding (vision-to-text) capabilities.
"""

from collections.abc import Iterable, Mapping, Sequence
from typing import Any, Literal, TypeAlias

import torch
import torch.nn as nn

from vllm.config import VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.logger import init_logger
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 (
    MultiModalDataDict,
    MultiModalFieldConfig,
    MultiModalKwargsItems,
)
from vllm.multimodal.parse import MultiModalDataItems
from vllm.multimodal.processing import (
    BaseMultiModalProcessor,
    BaseProcessingInfo,
    PromptReplacement,
)
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.processors.bagel import BagelProcessor
from vllm.utils.tensor_schema import TensorSchema

from .interfaces import (
    MultiModalEmbeddings,
    SupportsLoRA,
    SupportsMultiModal,
    SupportsPP,
)
from .siglip import SiglipVisionModel
from .utils import (
    AutoWeightsLoader,
    WeightsMapper,
    init_vllm_registered_model,
    maybe_prefix,
)

logger = init_logger(__name__)


class BagelImagePixelInputs(TensorSchema):
    """
    Dimensions:
        - bn: Batch size * number of images
        - c: Number of channels (3)
        - h: Height of each image
        - w: Width of each image
    """

    type: Literal["pixel_values"]
    pixel_values: torch.Tensor  # Shape: (bn, 3, h, w)


BagelImageInputs: TypeAlias = BagelImagePixelInputs


class BagelVisionMLP(nn.Module):
    """MLP connector for vision features."""

    def __init__(
        self,
        in_features: int,
        hidden_features: int,
        out_features: int,
        act_layer: str = "gelu_pytorch_tanh",
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()
        self.fc1 = ColumnParallelLinear(
            in_features,
            hidden_features,
            bias=True,
            quant_config=quant_config,
            prefix=f"{prefix}.fc1",
        )
        self.act = get_act_fn(act_layer)
        self.fc2 = RowParallelLinear(
            hidden_features,
            out_features,
            bias=True,
            quant_config=quant_config,
            prefix=f"{prefix}.fc2",
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x, _ = self.fc1(x)
        x = self.act(x)
        x, _ = self.fc2(x)
        return x


class PositionEmbedding(nn.Module):
    """2D position embedding for vision tokens using sin-cos embeddings."""

    def __init__(self, max_num_patch_per_side: int, hidden_size: int):
        super().__init__()
        self.max_num_patch_per_side = max_num_patch_per_side
        self.hidden_size = hidden_size

        # Create learnable 2D position embeddings (frozen sin-cos)
        pos_embed = self._get_2d_sincos_pos_embed(hidden_size, max_num_patch_per_side)
        self.register_buffer(
            "pos_embed",
            torch.from_numpy(pos_embed).float(),
            persistent=False,
        )

    @staticmethod
    def _get_2d_sincos_pos_embed(embed_dim: int, grid_size: int):
        """Generate 2D sin-cos position embeddings."""
        import numpy as np

        grid_h = np.arange(grid_size, dtype=np.float32)
        grid_w = np.arange(grid_size, dtype=np.float32)
        grid = np.meshgrid(grid_w, grid_h)  # w goes first
        grid = np.stack(grid, axis=0)
        grid = grid.reshape([2, 1, grid_size, grid_size])
        pos_embed = PositionEmbedding._get_2d_sincos_pos_embed_from_grid(
            embed_dim, grid
        )
        return pos_embed

    @staticmethod
    def _get_2d_sincos_pos_embed_from_grid(embed_dim: int, grid):
        """Generate 2D sin-cos position embeddings from grid."""
        import numpy as np

        assert embed_dim % 2 == 0
        # use half of dimensions to encode grid_h
        emb_h = PositionEmbedding._get_1d_sincos_pos_embed_from_grid(
            embed_dim // 2, grid[0]
        )
        emb_w = PositionEmbedding._get_1d_sincos_pos_embed_from_grid(
            embed_dim // 2, grid[1]
        )
        emb = np.concatenate([emb_h, emb_w], axis=1)
        return emb

    @staticmethod
    def _get_1d_sincos_pos_embed_from_grid(embed_dim: int, pos):
        """Generate 1D sin-cos position embeddings."""
        import numpy as np

        assert embed_dim % 2 == 0
        omega = np.arange(embed_dim // 2, dtype=np.float64)
        omega /= embed_dim / 2.0
        omega = 1.0 / 10000**omega

        pos = pos.reshape(-1)
        out = np.einsum("m,d->md", pos, omega)

        emb_sin = np.sin(out)
        emb_cos = np.cos(out)
        emb = np.concatenate([emb_sin, emb_cos], axis=1)
        return emb

    def forward(self, position_ids: torch.Tensor) -> torch.Tensor:
        """
        Args:
            position_ids: Flattened position IDs, shape (N,) where each ID
                         corresponds to a position in the flattened grid
        Returns:
            Position embeddings of shape (N, hidden_size)
        """
        # Ensure position_ids are on the same device as pos_embed
        position_ids = position_ids.to(self.pos_embed.device)
        return self.pos_embed[position_ids]


class BagelProcessingInfo(BaseProcessingInfo):
    """Processing information for BAGEL model."""

    def get_hf_processor(self, **kwargs: object) -> BagelProcessor:
        from vllm.transformers_utils.processor import cached_get_image_processor

        image_processor = cached_get_image_processor(
            self.ctx.model_config.model,
            revision=self.ctx.model_config.revision,
            trust_remote_code=self.ctx.model_config.trust_remote_code,
        )

        tokenizer = self.get_tokenizer()

        return BagelProcessor(
            image_processor=image_processor,
            tokenizer=tokenizer,
            **kwargs,
        )

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

    def get_mm_max_tokens_per_item(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> Mapping[str, int]:
        hf_config = self.get_hf_config()
        # Calculate max tokens per image
        # For BAGEL: (vit_max_num_patch_per_side) ** 2
        max_num_patches = hf_config.vit_max_num_patch_per_side**2
        return {"image": max_num_patches}

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
        hf_config = self.get_hf_config()
        vit_config = hf_config.vit_config
        patch_size = vit_config.patch_size

        # Calculate number of patches
        num_patches_h = image_height // patch_size
        num_patches_w = image_width // patch_size
        return num_patches_h * num_patches_w


class BagelDummyInputsBuilder(BaseDummyInputsBuilder[BagelProcessingInfo]):
    """Build dummy inputs for BAGEL model profiling."""

    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_images = mm_counts.get("image", 0)
        # Use a simple placeholder for each image
        return "<|image_pad|>" * num_images

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
        mm_options: Mapping[str, BaseDummyOptions] | None = None,
    ) -> MultiModalDataDict:
        num_images = mm_counts.get("image", 0)
        hf_config = self.info.get_hf_config()
        vit_config = hf_config.vit_config

        # Use the configured image size
        image_size = vit_config.image_size
        image_overrides = mm_options.get("image") if mm_options else None

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


class BagelMultiModalProcessor(BaseMultiModalProcessor[BagelProcessingInfo]):
    """Multimodal processor for BAGEL model."""

    def _hf_processor_applies_updates(
        self,
        prompt_text: str,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        tokenization_kwargs: Mapping[str, object],
    ) -> bool:
        return False

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, Any],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> Sequence[PromptReplacement]:
        """Replace image placeholders with the correct number of tokens."""
        hf_config = self.info.get_hf_config()

        # Get the tokenizer to look up the image token ID
        tokenizer = self.info.get_tokenizer()
        image_token_id = tokenizer.get_vocab().get("<|image_pad|>")
        if image_token_id is None:
            raise ValueError(
                "Image token '<|image_pad|>' not found in tokenizer vocabulary"
            )

        def get_replacement_bagel(item_idx: int):
            # For BAGEL, calculate number of tokens based on max patch size
            num_tokens = hf_config.vit_max_num_patch_per_side**2
            # Use the image token ID from tokenizer
            return [image_token_id] * num_tokens

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

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


@MULTIMODAL_REGISTRY.register_processor(
    BagelMultiModalProcessor,
    info=BagelProcessingInfo,
    dummy_inputs=BagelDummyInputsBuilder,
)
class BagelForConditionalGeneration(
    nn.Module, SupportsMultiModal, SupportsLoRA, SupportsPP
):
    """
    BAGEL: A unified multimodal model for image understanding and generation.

    For vLLM, we focus on the image understanding (vision-to-text) capabilities.
    The image generation part is not supported in vLLM.
    """

    # Weight mapping from HF to vLLM
    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_prefix={
            "language_model.": "language_model.",
            "vit_model.": "vit_model.",
            "connector.": "connector.",
            "vit_pos_embed.": "vit_pos_embed.",
        }
    )

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

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

        # Ensure we have a BagelConfig (check by name to handle trust_remote_code)
        # When trust_remote_code=True, the config comes from transformers_modules
        if type(config).__name__ != "BagelConfig":
            raise ValueError(
                f"Expected BagelConfig, got {type(config).__name__}. "
                "Make sure the model config is properly loaded."
            )

        self.config = config
        self.multimodal_config = multimodal_config

        # Initialize language model (Qwen2)
        # Pass the llm_config from BagelConfig to initialize Qwen2 properly
        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            hf_config=config.llm_config,
            prefix=maybe_prefix(prefix, "language_model"),
            architectures=["Qwen2ForCausalLM"],
        )

        # Initialize vision model (SigLIP) if visual understanding is enabled
        if config.visual_und:
            # Fix vit_config: checkpoint has 26 layers (0-25) but config says 27
            # Also disable head as it's not in checkpoint
            vit_config = config.vit_config
            if vit_config.num_hidden_layers == 27:
                logger.warning(
                    "Overriding vit_config.num_hidden_layers from 27 to 26 "
                    "to match the Bagel model checkpoint."
                )
                vit_config.num_hidden_layers = 26
            if not hasattr(vit_config, "vision_use_head"):
                logger.warning(
                    "Setting vit_config.vision_use_head to False as it is not "
                    "present in the Bagel model checkpoint."
                )
                vit_config.vision_use_head = False

            self.vit_model = SiglipVisionModel(
                config=vit_config,
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "vit_model"),
            )

            # Initialize connector (MLP)
            vit_hidden_size = config.vit_config.hidden_size
            llm_hidden_size = config.llm_config.hidden_size

            self.connector = BagelVisionMLP(
                in_features=vit_hidden_size,
                hidden_features=llm_hidden_size,
                out_features=llm_hidden_size,
                act_layer=config.connector_act,
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "connector"),
            )

            # Position embedding for vision tokens
            self.vit_pos_embed = PositionEmbedding(
                max_num_patch_per_side=config.vit_max_num_patch_per_side,
                hidden_size=llm_hidden_size,
            )
        else:
            self.vit_model = None
            self.connector = None
            self.vit_pos_embed = None

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors
        )

    def _parse_and_validate_image_input(
        self, **kwargs: object
    ) -> BagelImageInputs | None:
        pixel_values = kwargs.pop("pixel_values", None)

        if pixel_values is None:
            return None

        return BagelImagePixelInputs(
            type="pixel_values",
            pixel_values=pixel_values,
        )

    def _process_image_input(
        self, image_input: BagelImageInputs
    ) -> tuple[torch.Tensor, ...]:
        """Process image inputs through vision encoder and connector."""
        pixel_values = image_input["pixel_values"]

        # Handle potential extra batch dimension
        # Expected shape: (batch_size * num_images, 3, H, W)
        # But might receive: (batch_size, num_images, 3, H, W)
        if pixel_values.ndim == 5:
            # Flatten batch and num_images dimensions
            batch_size, num_images, channels, height, width = pixel_values.shape
            pixel_values = pixel_values.reshape(
                batch_size * num_images, channels, height, width
            )

        # Get vision features from SigLIP
        # pixel_values shape: (batch_size * num_images, 3, H, W)
        vision_features = self.vit_model(pixel_values)

        # Pass through connector
        vision_embeds = self.connector(vision_features)

        # Add position embeddings
        batch_size, num_patches, hidden_size = vision_embeds.shape
        patch_size = self.config.vit_config.patch_size
        image_size = self.config.vit_config.image_size

        # Calculate grid dimensions
        num_patches_per_side = image_size // patch_size

        # Create flattened position IDs (0 to num_patches-1)
        # For BAGEL, we use extrapolate mode by default
        h_coords = torch.arange(num_patches_per_side, device=vision_embeds.device)
        w_coords = torch.arange(num_patches_per_side, device=vision_embeds.device)
        position_ids = (
            h_coords[:, None] * self.config.vit_max_num_patch_per_side + w_coords
        ).flatten()
        position_ids = position_ids.unsqueeze(0).expand(batch_size, -1).flatten()

        # Add position embeddings
        pos_embeds = self.vit_pos_embed(position_ids)
        pos_embeds = pos_embeds.reshape(batch_size, num_patches, hidden_size)
        # Ensure pos_embeds are on the same device as vision_embeds
        pos_embeds = pos_embeds.to(vision_embeds.device)
        vision_embeds = vision_embeds + pos_embeds

        # Split by image
        return tuple(vision_embeds)

    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
        """Get multimodal embeddings from input."""
        image_input = self._parse_and_validate_image_input(**kwargs)
        if image_input is None:
            return []

        return self._process_image_input(image_input)

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

    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:
        """Run forward pass for BAGEL.

        Args:
            input_ids: Flattened (concatenated) input_ids corresponding to a batch.
            positions: Flattened (concatenated) position ids corresponding to a batch.
            intermediate_tensors: Intermediate tensors from prior forward pass.
            inputs_embeds: Optional tensor of input embeddings.
        """
        if intermediate_tensors is not None:
            inputs_embeds = None

        hidden_states = self.language_model.model(
            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,
    ) -> torch.Tensor | None:
        return self.language_model.compute_logits(hidden_states)

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        """Load weights from checkpoint."""
        skip_prefixes = []
        # Skip vit_pos_embed.pos_embed as it's handled by PositionEmbedding module
        skip_prefixes.append("vit_pos_embed.pos_embed")

        # If visual understanding is disabled, skip vision-related weights
        if self.vit_model is None:
            skip_prefixes.extend(["vit_model.", "connector.", "vit_pos_embed"])

        # Skip generation-related weights since we only support text2text and image2text
        # Filter out all image generation components:
        # - 'moe_gen': MoE generation weights
        # - 'latent_pos_embed': Latent position embeddings for VAE
        # - 'llm2vae', 'vae2llm': LLM-VAE projections
        # - 'time_embedder': Timestep embeddings for diffusion
        # - VAE encoder/decoder: Use specific prefixes to avoid matching vision encoder
        generation_keywords = [
            "moe_gen",
            "latent_pos_embed",
            "llm2vae",
            "vae2llm",
            "time_embedder",
        ]
        vae_prefixes = [
            "decoder.",
            "encoder.",
        ]  # VAE encoder/decoder, not vision encoder
        filtered_weights = []
        for name, tensor in weights:
            # Skip generation-related keywords
            if any(skip in name for skip in generation_keywords):
                continue
            if any(name.startswith(prefix) for prefix in vae_prefixes):
                continue

            if "patch_embedding.weight" in name and tensor.ndim == 2:
                out_channels = tensor.shape[0]
                in_features = tensor.shape[1]
                patch_size = self.config.vit_config.patch_size
                in_channels = self.config.vit_config.num_channels
                if in_features == in_channels * patch_size * patch_size:
                    tensor = tensor.reshape(
                        out_channels, patch_size, patch_size, in_channels
                    )
                    tensor = tensor.permute(0, 3, 1, 2).contiguous()

            filtered_weights.append((name, tensor))

        loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes)
        return loader.load_weights(filtered_weights, mapper=self.hf_to_vllm_mapper)
