# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

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

import torch
import torch.nn as nn
from transformers import (
    BatchFeature,
    Blip2Config,
    Blip2QFormerConfig,
    apply_chunking_to_forward,
)

from vllm.config import CacheConfig, VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.model_executor.layers.activation import get_act_fn
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 (
    BaseDummyInputsBuilder,
    BaseMultiModalProcessor,
    BaseProcessingInfo,
    PromptIndexTargets,
    PromptInsertion,
    PromptUpdate,
)
from vllm.sequence import IntermediateTensors
from vllm.utils.tensor_schema import TensorSchema, TensorShape

from .blip import BlipVisionModel, get_blip_num_patches
from .interfaces import (
    MultiModalEmbeddings,
    SupportsLoRA,
    SupportsMultiModal,
    SupportsPP,
    SupportsQuant,
)
from .module_mapping import MultiModelKeys
from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix


class Blip2ImagePixelInputs(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"]
    data: Annotated[torch.Tensor, TensorShape("bn", 3, "h", "w")]


class Blip2ImageEmbeddingInputs(TensorSchema):
    """
    Dimensions:
        - bn: Batch size * number of images
        - f: Image feature size
        - h: Hidden size (must match the hidden size of language model backbone)
    """

    type: Literal["image_embeds"]
    data: Annotated[torch.Tensor, TensorShape("bn", "f", "h")]


Blip2ImageInputs: TypeAlias = Blip2ImagePixelInputs | Blip2ImageEmbeddingInputs
"""Alias for supported BLIP-2 image input types."""


class Blip2QFormerMultiHeadAttention(nn.Module):
    def __init__(
        self,
        config: Blip2QFormerConfig,
        *,
        quant_config: QuantizationConfig | None,
        cache_config: CacheConfig | None,
        is_cross_attention: bool = False,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config

        if config.hidden_size % config.num_attention_heads != 0:
            raise ValueError(
                f"The hidden size ({config.hidden_size}) is not a multiple of "
                f"the number of attention heads ({config.num_attention_heads})"
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = config.hidden_size // config.num_attention_heads
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.scaling = self.attention_head_size**-0.5

        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        if is_cross_attention:
            kv_hidden_size = config.encoder_hidden_size
        else:
            kv_hidden_size = config.hidden_size
        self.key = nn.Linear(kv_hidden_size, self.all_head_size)
        self.value = nn.Linear(kv_hidden_size, self.all_head_size)

        self.position_embedding_type = getattr(
            config, "position_embedding_type", "absolute"
        )
        if self.position_embedding_type != "absolute":
            raise NotImplementedError(
                f"Unsupported position_embedding_type: {self.position_embedding_type}"
            )

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)

    def transpose_for_scores(self, x):
        x = x.view(*x.size()[:-1], self.num_attention_heads, self.attention_head_size)
        return x.permute(0, 2, 1, 3)

    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.FloatTensor | None = None,
    ):
        is_cross_attention = encoder_hidden_states is not None

        if is_cross_attention:
            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
        else:
            key_layer = self.transpose_for_scores(self.key(hidden_states))
            value_layer = self.transpose_for_scores(self.value(hidden_states))

        mixed_query_layer = self.query(hidden_states)

        query_layer = self.transpose_for_scores(mixed_query_layer)

        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_probs = torch.softmax(attention_scores * self.scaling, dim=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs_dropped = self.dropout(attention_probs)

        context_layer = torch.matmul(attention_probs_dropped, value_layer)

        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        context_layer = context_layer.view(
            *context_layer.size()[:-2], self.all_head_size
        )

        return context_layer


class Blip2QFormerSelfOutput(nn.Module):
    def __init__(self, config: Blip2QFormerConfig, prefix: str = "") -> None:
        super().__init__()

        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(
        self,
        hidden_states: torch.Tensor,
        input_tensor: torch.Tensor,
    ) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class Blip2QFormerAttention(nn.Module):
    def __init__(
        self,
        config: Blip2QFormerConfig,
        *,
        quant_config: QuantizationConfig | None,
        cache_config: CacheConfig | None,
        is_cross_attention: bool = False,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.attention = Blip2QFormerMultiHeadAttention(
            config,
            quant_config=quant_config,
            cache_config=cache_config,
            is_cross_attention=is_cross_attention,
            prefix=f"{prefix}.attention",
        )

        self.output = Blip2QFormerSelfOutput(config, prefix=f"{prefix}.output")

    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.FloatTensor | None = None,
    ) -> tuple[torch.Tensor]:
        self_output = self.attention(
            hidden_states,
            encoder_hidden_states=encoder_hidden_states,
        )
        attention_output = self.output(self_output, hidden_states)

        return attention_output


class Blip2QFormerIntermediate(nn.Module):
    def __init__(self, config: Blip2QFormerConfig, prefix: str = "") -> None:
        super().__init__()

        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        self.intermediate_act_fn = get_act_fn(config.hidden_act)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states


class Blip2QFormerOutput(nn.Module):
    def __init__(self, config: Blip2QFormerConfig, prefix: str = "") -> None:
        super().__init__()

        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(
        self,
        hidden_states: torch.Tensor,
        input_tensor: torch.Tensor,
    ) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class Blip2QFormerLayer(nn.Module):
    def __init__(
        self,
        config: Blip2QFormerConfig,
        *,
        quant_config: QuantizationConfig | None,
        cache_config: CacheConfig | None,
        layer_idx: int,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = Blip2QFormerAttention(
            config,
            quant_config=quant_config,
            cache_config=cache_config,
            prefix=f"{prefix}.attention",
        )

        self.layer_idx = layer_idx

        if layer_idx % config.cross_attention_frequency == 0:
            self.crossattention = Blip2QFormerAttention(
                config,
                quant_config=quant_config,
                cache_config=cache_config,
                is_cross_attention=True,
                prefix=f"{prefix}.crossattention",
            )
            self.has_cross_attention = True
        else:
            self.has_cross_attention = False

        self.intermediate_query = Blip2QFormerIntermediate(
            config, prefix=f"{prefix}.intermediate_query"
        )
        self.output_query = Blip2QFormerOutput(config, prefix=f"{prefix}.output_query")

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: torch.FloatTensor,
        query_length: int,
    ):
        attention_output = self.attention(hidden_states)

        if query_length > 0:
            query_attention_output = attention_output[:, :query_length, :]

            if self.has_cross_attention:
                query_attention_output = self.crossattention(
                    query_attention_output,
                    encoder_hidden_states=encoder_hidden_states,
                )

            layer_output = apply_chunking_to_forward(
                self.feed_forward_chunk_query,
                self.chunk_size_feed_forward,
                self.seq_len_dim,
                query_attention_output,
            )

            if attention_output.shape[1] > query_length:
                layer_output_text = apply_chunking_to_forward(
                    self.feed_forward_chunk,
                    self.chunk_size_feed_forward,
                    self.seq_len_dim,
                    attention_output[:, query_length:, :],
                )
                layer_output = torch.cat([layer_output, layer_output_text], dim=1)
        else:
            layer_output = apply_chunking_to_forward(
                self.feed_forward_chunk,
                self.chunk_size_feed_forward,
                self.seq_len_dim,
                attention_output,
            )

        return layer_output

    def feed_forward_chunk(self, attention_output: torch.Tensor) -> torch.Tensor:
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output

    def feed_forward_chunk_query(self, attention_output: torch.Tensor) -> torch.Tensor:
        intermediate_output = self.intermediate_query(attention_output)
        layer_output = self.output_query(intermediate_output, attention_output)
        return layer_output


class Blip2QFormerEncoder(nn.Module):
    def __init__(
        self,
        config: Blip2QFormerConfig,
        *,
        quant_config: QuantizationConfig | None,
        cache_config: CacheConfig | None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config

        self.layer = nn.ModuleList(
            [
                Blip2QFormerLayer(
                    config,
                    quant_config=quant_config,
                    cache_config=cache_config,
                    layer_idx=layer_idx,
                    prefix=f"{prefix}.layer.{layer_idx}",
                )
                for layer_idx in range(config.num_hidden_layers)
            ]
        )

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: torch.FloatTensor,
        query_length: int,
    ) -> torch.Tensor:
        for i in range(self.config.num_hidden_layers):
            layer_module = self.layer[i]

            hidden_states = layer_module(
                hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                query_length=query_length,
            )

        return hidden_states


# Adapted from https://github.com/huggingface/transformers/blob/v4.41.2/src/transformers/models/blip_2/modeling_blip_2.py#L1025
class Blip2QFormerModel(nn.Module):
    def __init__(
        self,
        config: Blip2QFormerConfig,
        *,
        quant_config: QuantizationConfig | None,
        cache_config: CacheConfig | None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config

        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

        self.encoder = Blip2QFormerEncoder(
            config,
            quant_config=quant_config,
            cache_config=cache_config,
            prefix=f"{prefix}.encoder",
        )

    def forward(
        self,
        query_embeds: torch.FloatTensor,
        encoder_hidden_states: torch.FloatTensor,
    ) -> torch.Tensor:
        query_length = query_embeds.shape[1]

        embedding_output = self.layernorm(query_embeds)
        embedding_output = self.dropout(embedding_output)

        sequence_output = self.encoder(
            embedding_output,
            encoder_hidden_states=encoder_hidden_states,
            query_length=query_length,
        )

        return sequence_output


class Blip2ProcessingInfo(BaseProcessingInfo):
    def get_hf_config(self):
        return self.ctx.get_hf_config(Blip2Config)

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

    def get_num_image_tokens(self) -> int:
        hf_config = self.get_hf_config()
        return hf_config.num_query_tokens


class Blip2DummyInputsBuilder(BaseDummyInputsBuilder[Blip2ProcessingInfo]):
    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        return ""

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
        mm_options: Mapping[str, BaseDummyOptions],
    ) -> MultiModalDataDict:
        hf_config = self.info.get_hf_config()
        vision_config = hf_config.vision_config

        max_image_size = vision_config.image_size
        num_images = mm_counts.get("image", 0)

        image_overrides = mm_options.get("image")

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


class Blip2MultiModalProcessor(BaseMultiModalProcessor[Blip2ProcessingInfo]):
    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:
            # HF processor always adds placeholders even when there's no image
            tokenizer = self.info.get_tokenizer()
            prompt_ids = tokenizer.encode(prompt)
            return BatchFeature(dict(input_ids=[prompt_ids]), tensor_type="pt")

        return super()._call_hf_processor(
            prompt=prompt,
            mm_data=mm_data,
            mm_kwargs=mm_kwargs,
            tok_kwargs=tok_kwargs,
        )

    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"),
            image_embeds=MultiModalFieldConfig.batched("image"),
        )

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> Sequence[PromptUpdate]:
        tokenizer = self.info.get_tokenizer()
        vocab = tokenizer.get_vocab()

        image_token_id = vocab["<image>"]
        num_image_tokens = self.info.get_num_image_tokens()
        image_tokens = [image_token_id] * num_image_tokens

        return [
            PromptInsertion(
                modality="image",
                target=PromptIndexTargets.start(),
                insertion=image_tokens,
            )
        ]


@MULTIMODAL_REGISTRY.register_processor(
    Blip2MultiModalProcessor,
    info=Blip2ProcessingInfo,
    dummy_inputs=Blip2DummyInputsBuilder,
)
class Blip2ForConditionalGeneration(
    nn.Module, SupportsLoRA, SupportsMultiModal, SupportsPP, SupportsQuant
):
    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
        if modality.startswith("image"):
            return None

        raise ValueError("Only image modality is supported")

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config
        self.config = config
        self.multimodal_config = multimodal_config
        vision_config = config.vision_config
        self._vision_tokens_per_image = (
            get_blip_num_patches(
                image_size=vision_config.image_size,
                patch_size=vision_config.patch_size,
            )
            + 1  # include class token
        )

        with self._mark_tower_model(vllm_config, "image"):
            self.vision_model = BlipVisionModel(vision_config, quant_config)
            self.query_tokens = nn.Parameter(
                torch.zeros(
                    1, config.num_query_tokens, config.qformer_config.hidden_size
                )
            )
            self.qformer = Blip2QFormerModel(
                config.qformer_config,
                cache_config=cache_config,
                quant_config=quant_config,
                prefix=f"{prefix}.qformer",
            )
            self.language_projection = nn.Linear(
                config.qformer_config.hidden_size,
                config.text_config.hidden_size,
                bias=True,
            )

        with self._mark_language_model(vllm_config):
            self.language_model = init_vllm_registered_model(
                vllm_config=vllm_config,
                hf_config=config.text_config,
                prefix=maybe_prefix(prefix, "language_model"),
            )

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors
        )

    def _parse_and_validate_image_input(
        self, **kwargs: object
    ) -> Blip2ImageInputs | None:
        pixel_values = kwargs.pop("pixel_values", 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:
            expected_h = expected_w = self.config.vision_config.image_size
            return Blip2ImagePixelInputs(
                type="pixel_values",
                data=pixel_values,
                resolve_bindings={"h": expected_h, "w": expected_w},
            )

        if image_embeds is not None:
            return Blip2ImageEmbeddingInputs(
                type="image_embeds",
                data=image_embeds,
            )

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

    def _image_pixels_to_features(
        self, vision_model: BlipVisionModel, pixel_values: torch.Tensor
    ) -> torch.Tensor:
        # NOTE: we skip the step to select the vision feature layer since
        # this is already done inside the vision tower
        image_features = vision_model(pixel_values)

        return image_features

    def _process_image_pixels(self, inputs: Blip2ImagePixelInputs) -> torch.Tensor:
        pixel_values = inputs["data"]

        return self._image_pixels_to_features(self.vision_model, pixel_values)

    def _process_image_input(self, image_input: Blip2ImageInputs) -> torch.Tensor:
        if image_input["type"] == "image_embeds":
            return image_input["data"]

        image_features = self._process_image_pixels(image_input)

        query_tokens = self.query_tokens.expand(image_features.shape[0], -1, -1)
        query_output = self.qformer(
            query_embeds=query_tokens,
            encoder_hidden_states=image_features,
        )

        return self.language_projection(query_output)

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

    def forward(
        self,
        input_ids: torch.Tensor | None,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs: object,
    ) -> IntermediateTensors:
        """Run forward pass for BLIP-2.

        One key thing to understand is the `input_ids` already accounts for the
        positions of the to-be-inserted image embeddings.

        Concretely, consider a text prompt:
        `"Question: What's the content of the image? Answer:"`.

        Tokenizer outputs:
        `[2, 45641, 35, 653, 18, 5, 1383, 9, 5, 2274, 116, 31652, 35]`.

        To reserve space in KV cache, we have to insert placeholder tokens
        before they are inputted to the model, so the input processor prepends
        dummy tokens (denoted as `50265`), resulting in:
        `[50265, ..., 50265, 2, 45641, 35, ..., 31652, 35]`.

        We insert 32 tokens since it corresponds to the number of query
        embeddings outputted by the Q-Former and inputted to the language model.

        This way, the `positions` and `attn_metadata` are consistent
        with the `input_ids`.

        Args:
            input_ids: Flattened (concatenated) input_ids corresponding to a
                batch.

        Info:
            [`Blip2ImageInputs`][vllm.model_executor.models.blip2.Blip2ImageInputs]
        """

        if intermediate_tensors is not None:
            inputs_embeds = 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)

    def get_mm_mapping(self) -> MultiModelKeys:
        return MultiModelKeys.from_string_field(
            language_model="language_model",
            connector=["qformer", "language_projection"],
            tower_model="vision_model",
        )

    def get_num_mm_encoder_tokens(
        self,
        num_image_tokens: int,
    ) -> int:
        if num_image_tokens <= 0:
            return 0
        assert num_image_tokens % self.config.num_query_tokens == 0, (
            "The number of image tokens must be a multiple of "
            "the number of query tokens."
        )
        num_images = num_image_tokens / self.config.num_query_tokens
        return num_images * self._vision_tokens_per_image

    def get_num_mm_connector_tokens(
        self,
        num_vision_tokens: int,
    ) -> int:
        if num_vision_tokens <= 0:
            return 0
        assert num_vision_tokens % self._vision_tokens_per_image == 0, (
            "The number of vision tokens must be a multiple of "
            "the number of tokens per image."
        )
        num_images = num_vision_tokens / self._vision_tokens_per_image
        return num_images * self.config.num_query_tokens
