# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Implementation of SiglipVisionModel intended to be only used
within a vision language model."""

import math
from collections.abc import Iterable
from typing import Optional, Union

import torch
from torch import nn
from transformers import SiglipVisionConfig

from vllm.attention.layer import MultiHeadAttention
from vllm.distributed import divide, get_tensor_model_parallel_world_size
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding
from vllm.model_executor.model_loader.weight_utils import (
    default_weight_loader,
    maybe_remap_kv_scale_name,
)

from .vision import (
    VisionEncoderInfo,
    VisionFeatureSelectStrategy,
    resolve_visual_encoder_outputs,
)


class SiglipEncoderInfo(VisionEncoderInfo[SiglipVisionConfig]):
    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
        return self.get_patch_grid_length() ** 2

    def get_image_size(self) -> int:
        return self.vision_config.image_size

    def get_patch_size(self) -> int:
        return self.vision_config.patch_size

    def get_patch_grid_length(self) -> int:
        image_size, patch_size = self.get_image_size(), self.get_patch_size()
        return image_size // patch_size


# Adapted from https://github.com/huggingface/transformers/blob/v4.43.3/src/transformers/models/siglip/modeling_siglip.py#L249 # noqa
class SiglipVisionEmbeddings(nn.Module):
    def __init__(self, config: SiglipVisionConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.image_size = config.image_size
        self.patch_size = config.patch_size

        self.patch_embedding = nn.Conv2d(
            in_channels=config.num_channels,
            out_channels=self.embed_dim,
            kernel_size=self.patch_size,
            stride=self.patch_size,
            padding="valid",
        )

        self.num_patches = (self.image_size // self.patch_size) ** 2
        self.num_positions = self.num_patches
        self.position_embedding = VocabParallelEmbedding(
            self.num_positions, self.embed_dim
        )
        self.register_buffer(
            "position_ids",
            torch.arange(self.num_positions, dtype=torch.int64).expand((1, -1)),
            persistent=False,
        )

    def interpolate_pos_encoding(
        self, embeddings: torch.Tensor, height: int, width: int
    ) -> torch.Tensor:
        """
        This method is an adapted method for SigLIP (due to SigLIP not having
        class embedding unlike other ViTs) that allows the model to interpolate
        the pre-trained position encodings such that it can be usable on higher
        resolution images.

        Source:
        https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
        """
        position_embeddings = self.position_embedding.weight.unsqueeze(0)
        num_patches = embeddings.shape[1]
        num_positions = position_embeddings.shape[1]
        if num_patches == num_positions and height == width:
            return position_embeddings

        dim = embeddings.shape[-1]
        height = height // self.patch_size
        width = width // self.patch_size
        # we add a small number to avoid floating point error
        # in the interpolation
        # see discussion at https://github.com/facebookresearch/dino/issues/8
        height, width = height + 0.1, width + 0.1

        patch_pos_embed = position_embeddings.reshape(
            1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim
        )
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            scale_factor=(
                height / math.sqrt(num_positions),
                width / math.sqrt(num_positions),
            ),
            mode="bicubic",
            align_corners=False,
        )
        if (
            int(height) != patch_pos_embed.shape[-2]
            or int(width) != patch_pos_embed.shape[-1]
        ):
            raise ValueError(
                "Width or height does not match with "
                "the interpolated position embeddings"
            )

        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        return patch_pos_embed

    def forward(
        self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False
    ) -> torch.Tensor:
        _, _, height, width = pixel_values.shape
        target_dtype = self.patch_embedding.weight.dtype
        patch_embeds = self.patch_embedding(
            pixel_values.to(dtype=target_dtype)
        )  # shape = [*, width, grid, grid]
        embeddings = patch_embeds.flatten(2).transpose(1, 2)

        if interpolate_pos_encoding:
            embeddings += self.interpolate_pos_encoding(embeddings, height, width)
        else:
            embeddings += self.position_embedding(self.position_ids)
        return embeddings


class SiglipAttention(nn.Module):
    def __init__(
        self,
        config: SiglipVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config
        self.embed_dim = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.embed_dim // self.num_heads
        if self.head_dim * self.num_heads != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got "
                "`embed_dim`: {self.embed_dim} and `num_heads`:"
                f" {self.num_heads})."
            )

        self.scale = self.head_dim**-0.5
        self.dropout = config.attention_dropout
        self.qkv_proj = QKVParallelLinear(
            hidden_size=self.embed_dim,
            head_size=self.head_dim,
            total_num_heads=self.num_heads,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )

        self.out_proj = RowParallelLinear(
            input_size=self.embed_dim,
            output_size=self.embed_dim,
            quant_config=quant_config,
            prefix=f"{prefix}.out_proj",
        )

        self.tp_size = get_tensor_model_parallel_world_size()
        self.num_heads_per_partition = divide(self.num_heads, self.tp_size)

        self.attn = MultiHeadAttention(
            self.num_heads_per_partition, self.head_dim, self.scale
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        """Input shape: Batch x Time x Channel"""
        qkv_states, _ = self.qkv_proj(hidden_states)
        query_states, key_states, value_states = qkv_states.chunk(3, dim=-1)

        out = self.attn(query_states, key_states, value_states)
        attn_output, _ = self.out_proj(out)

        return attn_output, None


class SiglipMLP(nn.Module):
    def __init__(
        self,
        config: SiglipVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config
        self.activation_fn = get_act_fn(config.hidden_act)
        # Special handling for BNB and torchao quantization
        if quant_config and quant_config.get_name() in ["bitsandbytes", "torchao"]:
            quantizable = True
        else:
            # For other quantization, we require the hidden size to be a
            # multiple of 64
            quantizable = (
                config.hidden_size % 64 == 0 and config.intermediate_size % 64 == 0
            )
        self.fc1 = ColumnParallelLinear(
            config.hidden_size,
            config.intermediate_size,
            quant_config=quant_config if quantizable else None,
            prefix=f"{prefix}.fc1",
        )
        self.fc2 = RowParallelLinear(
            config.intermediate_size,
            config.hidden_size,
            quant_config=quant_config if quantizable else None,
            prefix=f"{prefix}.fc2",
        )

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


class SiglipEncoderLayer(nn.Module):
    def __init__(
        self,
        config: SiglipVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.embed_dim = config.hidden_size

        self.self_attn = SiglipAttention(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn",
        )
        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
        self.mlp = SiglipMLP(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.mlp",
        )
        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
    ) -> tuple[torch.Tensor, None]:
        residual = hidden_states

        hidden_states = self.layer_norm1(hidden_states)
        hidden_states, _ = self.self_attn(hidden_states=hidden_states)
        hidden_states += residual

        residual = hidden_states
        hidden_states = self.layer_norm2(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states += residual

        return hidden_states, None


class SiglipEncoder(nn.Module):
    def __init__(
        self,
        config: SiglipVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        num_hidden_layers_override: Optional[int] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config

        if num_hidden_layers_override is None:
            num_hidden_layers = config.num_hidden_layers
        else:
            num_hidden_layers = num_hidden_layers_override

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

    def forward(
        self,
        inputs_embeds: torch.Tensor,
        return_all_hidden_states: bool,
    ) -> Union[torch.Tensor, list[torch.Tensor]]:
        hidden_states_pool = [inputs_embeds]
        hidden_states = inputs_embeds

        for encoder_layer in self.layers:
            hidden_states, _ = encoder_layer(hidden_states)
            if return_all_hidden_states:
                hidden_states_pool.append(hidden_states)
        # If we have multiple feature sample layers, we return all hidden
        # states in order and grab the ones we need by index.
        if return_all_hidden_states:
            return hidden_states_pool
        return hidden_states


class SiglipMultiheadAttentionPoolingHead(nn.Module):
    """Multihead Attention Pooling."""

    def __init__(
        self,
        config: SiglipVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size))
        # TODO(ChristopherCho): Implement vLLM version of MultiheadAttention
        self.attention = torch.nn.MultiheadAttention(
            config.hidden_size, config.num_attention_heads, batch_first=True
        )
        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.mlp = SiglipMLP(
            config=config, quant_config=quant_config, prefix=f"{prefix}.mlp"
        )

    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
        batch_size = hidden_state.shape[0]
        probe = self.probe.repeat(batch_size, 1, 1)

        hidden_state = self.attention(probe, hidden_state, hidden_state)[0]

        residual = hidden_state
        hidden_state = self.layernorm(hidden_state)
        hidden_state = self.mlp(hidden_state)
        hidden_state += residual

        return hidden_state[:, 0]


class SiglipVisionTransformer(nn.Module):
    def __init__(
        self,
        config: SiglipVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        *,
        num_hidden_layers_override: Optional[int] = None,
        require_post_norm: Optional[bool] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config
        embed_dim = config.hidden_size

        self.embeddings = SiglipVisionEmbeddings(config)

        self.encoder = SiglipEncoder(
            config,
            quant_config=quant_config,
            num_hidden_layers_override=num_hidden_layers_override,
            prefix=f"{prefix}.encoder",
        )

        num_hidden_layers = config.num_hidden_layers
        if len(self.encoder.layers) > config.num_hidden_layers:
            raise ValueError(
                f"The original encoder only has {num_hidden_layers} "
                f"layers, but you requested {len(self.encoder.layers)} layers."
            )

        # If possible, skip post_layernorm to conserve memory
        if require_post_norm is None:
            require_post_norm = len(self.encoder.layers) == num_hidden_layers

        if require_post_norm:
            self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
        else:
            self.post_layernorm = None

        self.use_head = (
            True if not hasattr(config, "vision_use_head") else config.vision_use_head
        )
        if self.use_head:
            self.head = SiglipMultiheadAttentionPoolingHead(
                config=config,
                quant_config=quant_config,
                prefix=f"{prefix}.head",
            )

    def forward(
        self,
        pixel_values: torch.Tensor,
        *,
        interpolate_pos_encoding: bool = False,
        select_layers: Optional[list[int]] = None,
        feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None,
    ) -> torch.Tensor:
        hidden_states = self.embeddings(
            pixel_values,
            interpolate_pos_encoding=interpolate_pos_encoding,
        )

        # Produces either the last layer output or all of the hidden states,
        # depending on if we have select_layers or not
        encoder_outputs = self.encoder(
            inputs_embeds=hidden_states,
            return_all_hidden_states=select_layers is not None,
        )

        # Handle post-norm (if applicable) and stacks feature layers if needed
        encoder_outputs = resolve_visual_encoder_outputs(
            encoder_outputs,
            self.post_layernorm,
            select_layers=select_layers,
            max_possible_layers=self.config.num_hidden_layers,
            feature_select_strategy=feature_select_strategy,
        )

        # TODO: add this back when pooled_output is used in inference.
        # if self.use_head:
        # pooled_output = self.head(encoder_outputs)

        return encoder_outputs


class SiglipVisionModel(nn.Module):
    config_class = SiglipVisionConfig
    main_input_name = "pixel_values"

    def __init__(
        self,
        config: SiglipVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        *,
        num_hidden_layers_override: Optional[int] = None,
        require_post_norm: Optional[bool] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.vision_model = SiglipVisionTransformer(
            config,
            quant_config,
            num_hidden_layers_override=num_hidden_layers_override,
            require_post_norm=require_post_norm,
            prefix=f"{prefix}.vision_model",
        )

    def get_input_embeddings(self) -> nn.Module:
        return self.vision_model.embeddings.patch_embedding

    @property
    def dtype(self):
        return self.get_input_embeddings().weight.dtype

    def forward(
        self,
        pixel_values: torch.Tensor,
        interpolate_pos_encoding: bool = False,
        select_layers: Optional[list[int]] = None,
        feature_select_strategy: Optional[VisionFeatureSelectStrategy] = None,
    ) -> torch.Tensor:
        return self.vision_model(
            pixel_values=pixel_values,
            interpolate_pos_encoding=interpolate_pos_encoding,
            select_layers=select_layers,
            feature_select_strategy=feature_select_strategy,
        )

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        layer_count = len(self.vision_model.encoder.layers)

        for name, loaded_weight in weights:
            # post_layernorm is optional in SiglipVisionModel
            if (
                name.startswith("vision_model.post_layernorm")
                and self.vision_model.post_layernorm is None
            ):
                continue

            # omit layers when num_hidden_layers_override is set
            if name.startswith("vision_model.encoder.layers"):
                layer_idx = int(name.split(".")[3])
                if layer_idx >= layer_count:
                    continue

            # Check if this is a scale parameter that needs remapping first
            if name.endswith((".k_scale", ".v_scale", ".q_scale", ".prob_scale")):
                # Try to remap the scale name first
                remapped_name = maybe_remap_kv_scale_name(name, params_dict)
                if remapped_name is not None and remapped_name in params_dict:
                    # Successfully remapped, use the remapped name
                    param = params_dict[remapped_name]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(param, loaded_weight)
                    loaded_params.add(remapped_name)
                    continue
                # If remapping failed, continue with normal processing

            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)

                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params
