# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Inference-only LLaVa model compatible with HuggingFace weights."""

import math
import re
from typing import Iterable, List, Optional, Tuple

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPVisionConfig,
    CLIPVisionModel,
    LlavaConfig,
    MistralConfig,
    Qwen2Config,
    SiglipVisionModel,
)
from transformers.models.llava.modeling_llava import LlavaMultiModalProjector

from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.managers.schedule_batch import ImageInputs
from sglang.srt.mm_utils import (
    get_anyres_image_grid_shape,
    unpad_image,
    unpad_image_shape,
)
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.model_loader.weight_utils import default_weight_loader
from sglang.srt.models.llama import LlamaForCausalLM
from sglang.srt.models.mistral import MistralForCausalLM
from sglang.srt.models.qwen2 import Qwen2ForCausalLM


class LlavaBaseForCausalLM(nn.Module):
    def pad_input_ids(self, input_ids: List[int], image_inputs: ImageInputs):
        image_sizes, pad_values = image_inputs.image_sizes, image_inputs.pad_values

        # hardcode for spatial_unpad + anyres
        if image_inputs.modalities is not None and (
            "multi-images" in image_inputs.modalities
            or "video" in image_inputs.modalities
        ):
            image_aspect_ratio = "pad"
        else:
            image_aspect_ratio = "anyres"
        offset_list = []
        image_inputs.image_pad_len = []
        for image_idx, image_s in enumerate(image_sizes):
            if len(image_sizes) > 16:
                # 2x2 pooling with stride 2
                new_image_feature_len = (
                    math.ceil(self.image_size / self.patch_size / 2) ** 2
                )
            else:
                new_image_feature_len = self.image_feature_len  # multiimage

            height = width = self.num_patches_per_side
            if "anyres" in image_aspect_ratio:
                num_patch_width, num_patch_height = get_anyres_image_grid_shape(
                    image_s,
                    self.image_grid_pinpoints,
                    self.vision_tower.config.image_size,
                )
                h = num_patch_height * height
                w = num_patch_width * width
                new_h, new_w = unpad_image_shape(h, w, image_s)

                if "anyres_max" in self.config.image_aspect_ratio:
                    matched_anyres_max_num_patches = re.match(
                        r"anyres_max_(\d+)", self.config.image_aspect_ratio
                    )
                    if matched_anyres_max_num_patches:
                        max_num_patches = int(matched_anyres_max_num_patches.group(1))
                    # times = math.sqrt(h * w / (max_num_patches * unit**2))
                    times = math.sqrt(
                        new_h * new_w / (max_num_patches * self.image_feature_len)
                    )
                    if times > 1.1:
                        new_h = int(new_h // times)
                        new_w = int(new_w // times)
                new_image_feature_len += new_h * (new_w + 1)

            try:
                offset = input_ids.index(self.config.image_token_index)
            except ValueError:
                offset = 0
            # old_len + pad_len - 1, because we need to remove image_token_id
            input_ids = (
                input_ids[:offset]
                + [pad_values[image_idx]] * new_image_feature_len
                + input_ids[offset + 1 :]
            )
            offset_list.append(offset)
            image_inputs.image_pad_len.append(new_image_feature_len)

        image_inputs.image_offsets = offset_list
        return input_ids

    def encode_images(self, pixel_values: torch.Tensor) -> torch.Tensor:
        image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
        # NOTE: This is not memory efficient. (output_hidden_states=True) will save all the hidden stated.

        selected_image_feature = image_outputs.hidden_states[self.vision_feature_layer]
        if self.vision_feature_select_strategy in ["default", "patch"]:
            selected_image_feature = selected_image_feature[:, 1:]
        elif self.vision_feature_select_strategy == "full":
            selected_image_feature = selected_image_feature
        else:
            raise ValueError(
                f"Unexpected select feature strategy: {self.config.vision_feature_select_strategy}"
            )
        image_features = self.multi_modal_projector(selected_image_feature)

        return image_features

    @torch.no_grad()
    def forward(
        self,
        input_ids: torch.LongTensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        image_inputs = forward_batch.image_inputs

        if forward_batch.forward_mode.is_extend():
            # Clamp input ids. This is because the input_ids for the image tokens are
            # filled with the hash values of the image for the prefix matching in the radix attention.
            # There values are useless because their embeddings will be replaced by vision embeddings anyway.
            input_ids.clamp_(min=0, max=self.config.vocab_size - 1)

            # Embed text inputs
            input_embeds = self.language_model.model.embed_tokens(input_ids)

            # Got List[List[str]] extend it to List[str]
            # The length of the List should be equal to batch size
            modalities_list = []
            max_image_offset = []
            for im in image_inputs:
                if im and im.modalities is not None:
                    modalities_list.extend(im.modalities)
                if im and im.image_offsets:
                    max_image_offset.append(
                        np.max(np.array(im.image_offsets) + np.array(im.image_pad_len))
                    )
                else:
                    max_image_offset.append(-1)

            start_positions = positions[forward_batch.extend_start_loc].cpu().numpy()
            need_vision = start_positions <= np.array(max_image_offset)

            if need_vision.any():
                bs = forward_batch.batch_size
                pixel_values = [
                    image_inputs[i].pixel_values for i in range(bs) if need_vision[i]
                ]
                image_sizes = [
                    image_inputs[i].image_sizes for i in range(bs) if need_vision[i]
                ]

                ########## Encode Image ########

                if pixel_values[0].ndim == 4:
                    # llava-hd: BS, num_patch, C=3, H=336, W=336, num_patch obtained from process_images
                    np.concatenate(pixel_values, axis=0)
                    # ndim=4
                    concat_images = torch.tensor(
                        np.concatenate(pixel_values, axis=0),
                        device=self.vision_tower.device,
                    )
                    image_features = self.encode_images(concat_images)
                    split_sizes = [image.shape[0] for image in pixel_values]
                    image_features = torch.split(image_features, split_sizes, dim=0)
                    # hd image_features: BS, num_patch, 576, 4096
                else:
                    # normal pixel: BS, C=3, H=336, W=336
                    pixel_values = torch.tensor(
                        np.array(pixel_values), device=self.vision_tower.device
                    )
                    image_features = self.encode_images(pixel_values)
                    # image_features: BS, 576, 4096

                if self.mm_patch_merge_type.startswith("spatial"):
                    new_image_features = []
                    height = width = self.num_patches_per_side
                    for image_idx, image_feature in enumerate(image_features):
                        if modalities_list[image_idx] == "image":
                            image_aspect_ratio = (
                                self.config.image_aspect_ratio
                            )  # single image
                        elif (
                            modalities_list[image_idx] == "multi-images"
                            or modalities_list[image_idx] == "video"
                        ):
                            image_aspect_ratio = "pad"  # multi image
                        # image_aspect_ratio = (
                        #     "anyres" if len(image_sizes[image_idx]) == 1 else "pad"
                        # )
                        if (
                            image_feature.shape[0] > 1
                            and "anyres" in image_aspect_ratio
                            and modalities_list[image_idx] == "image"
                        ):
                            base_image_feature = image_feature[0]
                            image_feature = image_feature[1:]
                            assert height * width == base_image_feature.shape[0]

                            if "anyres_max" in image_aspect_ratio:
                                matched_anyres_max_num_patches = re.match(
                                    r"anyres_max_(\d+)", image_aspect_ratio
                                )
                                if matched_anyres_max_num_patches:
                                    max_num_patches = int(
                                        matched_anyres_max_num_patches.group(1)
                                    )

                            if (
                                image_aspect_ratio == "anyres"
                                or "anyres_max" in image_aspect_ratio
                            ):
                                vision_tower_image_size = self.image_size
                                try:
                                    num_patch_width, num_patch_height = (
                                        get_anyres_image_grid_shape(
                                            image_sizes[image_idx][0],
                                            self.config.image_grid_pinpoints,
                                            vision_tower_image_size,
                                        )
                                    )
                                except Exception as e:
                                    print(f"Error: {e}")
                                    num_patch_width, num_patch_height = 2, 2
                                image_feature = image_feature.view(
                                    num_patch_height, num_patch_width, height, width, -1
                                )
                            else:
                                image_feature = image_feature.view(
                                    2, 2, height, width, -1
                                )

                            # (
                            #     num_patch_width,
                            #     num_patch_height,
                            # ) = get_anyres_image_grid_shape(
                            #     image_sizes[image_idx][0],
                            #     self.image_grid_pinpoints,
                            #     self.vision_tower.config.image_size,
                            # )

                            # image_feature = image_feature.view(
                            #     num_patch_height, num_patch_width, height, width, -1
                            # )

                            if "unpad" in self.mm_patch_merge_type:
                                unit = image_feature.shape[2]
                                image_feature = image_feature.permute(
                                    4, 0, 2, 1, 3
                                ).contiguous()
                                image_feature = image_feature.flatten(1, 2).flatten(
                                    2, 3
                                )
                                image_feature = unpad_image(
                                    image_feature, image_sizes[image_idx][0]
                                )
                                if (
                                    "anyres_max" in image_aspect_ratio
                                    and matched_anyres_max_num_patches
                                ):
                                    c, h, w = image_feature.shape
                                    times = math.sqrt(
                                        h * w / (max_num_patches * unit**2)
                                    )
                                    if times > 1.1:
                                        image_feature = image_feature[None]
                                        image_feature = nn.functional.interpolate(
                                            image_feature,
                                            [int(h // times), int(w // times)],
                                            mode="bilinear",
                                        )[0]
                                image_feature = torch.cat(
                                    (
                                        image_feature,
                                        self.language_model.model.image_newline[
                                            :, None, None
                                        ].expand(*image_feature.shape[:-1], 1),
                                    ),
                                    dim=-1,
                                )
                                image_feature = image_feature.flatten(1, 2).transpose(
                                    0, 1
                                )
                            else:
                                image_feature = image_feature.permute(
                                    0, 2, 1, 3, 4
                                ).contiguous()
                                image_feature = image_feature.flatten(0, 3)
                            image_feature = torch.cat(
                                (base_image_feature, image_feature), dim=0
                            )
                            image_feature = image_feature.unsqueeze(0)
                        else:
                            if modalities_list[image_idx] == "video":  # video
                                # 2x2 pooling
                                num_of_frames = image_feature.shape[0]
                                image_feature = image_feature.view(
                                    num_of_frames, height, width, -1
                                )
                                image_feature = image_feature.permute(
                                    0, 3, 1, 2
                                ).contiguous()  # N, C, H, W
                                height, weight = image_feature.shape[2:]
                                scaled_shape = [
                                    math.ceil(height / 2),
                                    math.ceil(weight / 2),
                                ]
                                image_feature = nn.functional.interpolate(
                                    image_feature, size=scaled_shape, mode="bilinear"
                                )
                                image_feature = (
                                    image_feature.flatten(2)
                                    .transpose(1, 2)
                                    .contiguous()
                                )  # N, C, H*W
                            if "unpad" in self.mm_patch_merge_type:
                                image_feature = torch.cat(
                                    (
                                        image_feature,
                                        # Expand to (bs, 1, hidden_dim) and concat at the end of the image tokens
                                        self.language_model.model.image_newline[
                                            None, None
                                        ].expand(
                                            image_feature.shape[0],
                                            1,
                                            image_feature.shape[-1],
                                        ),
                                    ),
                                    dim=1,
                                )

                        new_image_features.append(image_feature)
                    image_features = new_image_features

                # Fill in the placeholder for the image
                extend_start_loc_cpu = forward_batch.extend_start_loc.cpu().numpy()
                extend_seq_lens = forward_batch.extend_seq_lens.cpu().numpy()
                prefix_lens_cpu = forward_batch.extend_prefix_lens_cpu
                pt = 0
                for i in range(bs):
                    if not need_vision[i]:
                        continue

                    start_idx = extend_start_loc_cpu[i]
                    seq_len = extend_seq_lens[i]
                    prefix_len = prefix_lens_cpu[i]

                    # Multiple images
                    for image_idx, image_offset in enumerate(
                        image_inputs[i].image_offsets
                    ):
                        if (
                            image_offset + image_inputs[i].image_pad_len[image_idx]
                            <= prefix_len
                        ):
                            continue
                        if image_offset >= prefix_len + seq_len:
                            break

                        tmp_image_feature = image_features[pt][image_idx]
                        pad_len = tmp_image_feature.shape[0]

                        input_offset = image_offset - prefix_len
                        left_idx = start_idx + input_offset
                        right_idx = left_idx + pad_len
                        assert right_idx > start_idx
                        if input_offset < 0:
                            left_idx = start_idx
                            tmp_image_feature = tmp_image_feature[-input_offset:]
                        if right_idx > start_idx + seq_len:
                            tmp_image_feature = tmp_image_feature[
                                : start_idx + seq_len - right_idx
                            ]
                            right_idx = start_idx + seq_len
                        try:
                            input_embeds[left_idx:right_idx] = tmp_image_feature
                        except RuntimeError as e:
                            print(f"RuntimeError in image encoding: {e}")
                            print(f"{input_embeds.shape=}, {tmp_image_feature.shape=}")
                            print(
                                f"{start_idx=}, {image_offset=}, {prefix_len=}, {pad_len=}"
                            )
                    pt += 1

            return self.language_model(
                input_ids, positions, forward_batch, input_embeds=input_embeds
            )
        elif forward_batch.forward_mode.is_decode():
            return self.language_model(input_ids, positions, forward_batch)

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        # Load clip vision model by cfg['mm_vision_tower']:
        # huggingface_name or path_of_clip_relative_to_llava_model_dir
        # We put the initialization here instead of __init__ to allow it being reused by other subclasses.
        vision_path = self.config.mm_vision_tower
        if "clip" in vision_path:
            self.vision_tower = CLIPVisionModel.from_pretrained(
                vision_path, torch_dtype=torch.float16
            ).cuda()
        elif "siglip" in vision_path:
            self.vision_tower = SiglipVisionModel.from_pretrained(
                vision_path, torch_dtype=torch.float16
            ).cuda()
            # Siglip needs all feature tokens
            self.config.mm_vision_select_feature = "full"
        self.vision_tower.eval()

        self.vision_feature_layer = self.config.mm_vision_select_layer
        self.vision_feature_select_strategy = self.config.mm_vision_select_feature
        self.image_size = self.vision_tower.config.image_size
        self.patch_size = self.vision_tower.config.patch_size

        self.mm_patch_merge_type = getattr(self.config, "mm_patch_merge_type", "flat")
        self.image_aspect_ratio = getattr(self.config, "image_aspect_ratio", "square")
        self.image_grid_pinpoints = getattr(self.config, "image_grid_pinpoints", None)

        self.image_feature_len = int((self.image_size // self.patch_size) ** 2)
        if (
            self.vision_feature_select_strategy == "patch"
            or self.vision_feature_select_strategy == "full"
        ):
            pass
        elif self.vision_feature_select_strategy == "cls_patch":
            self.image_feature_len += 1
        else:
            raise ValueError(f"Unexpected select feature: {self.select_feature}")

        # load mm_projector
        projector_weights = {
            "model.mm_projector.0": "multi_modal_projector.linear_1",
            "model.mm_projector.2": "multi_modal_projector.linear_2",
            "model.vision_tower.vision_tower": "vision_tower",  # Update the vision tower weights if we find them in the checkpoint (it may be finetuned).
            "model.image_newline": "language_model.model.image_newline",
        }
        params_dict = dict(self.named_parameters())
        for name, loaded_weight in weights:
            if "projector" in name or "vision_tower" in name or "image_newline" in name:
                for weight_name, param_name in projector_weights.items():
                    if weight_name in name:
                        name = name.replace(weight_name, param_name)
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)
            else:
                self.language_model.load_weights([(name, loaded_weight)])

    @property
    def num_patches_per_side(self):
        return self.image_size // self.patch_size


class LlavaLlamaForCausalLM(LlavaBaseForCausalLM):
    def __init__(
        self,
        config: LlavaConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()

        self.config = config
        self.vision_tower = None
        self.config.vision_config.hidden_size = config.mm_hidden_size
        self.config.text_config.hidden_size = config.hidden_size

        self.multi_modal_projector = LlavaMultiModalProjector(config)
        self.language_model = LlamaForCausalLM(config, quant_config=quant_config)
        if "unpad" in getattr(config, "mm_patch_merge_type", ""):
            self.language_model.model.image_newline = nn.Parameter(
                torch.empty(config.text_config.hidden_size, dtype=torch.float16)
            )


class LlavaQwenForCausalLM(LlavaBaseForCausalLM):
    def __init__(
        self,
        config: LlavaConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()

        self.config = config
        self.vision_tower = None

        if getattr(self.config, "vision_config", None) is None:
            self.config.vision_config = CLIPVisionConfig(self.config.mm_vision_tower)
        if getattr(self.config, "text_config", None) is None:
            self.config.text_config = Qwen2Config(self.config._name_or_path)

        self.config.vision_config.hidden_size = config.mm_hidden_size
        self.config.text_config.hidden_size = config.hidden_size

        if getattr(self.config, "projector_hidden_act", None) is None:
            self.config.projector_hidden_act = "gelu"
        if getattr(self.config, "image_token_index", None) is None:
            self.config.image_token_index = 151646

        self.multi_modal_projector = LlavaMultiModalProjector(config)
        self.language_model = Qwen2ForCausalLM(config, quant_config=quant_config)
        if "unpad" in getattr(config, "mm_patch_merge_type", ""):
            self.language_model.model.image_newline = nn.Parameter(
                torch.empty(config.text_config.hidden_size, dtype=torch.float16)
            )


class LlavaMistralForCausalLM(LlavaBaseForCausalLM):
    def __init__(
        self,
        config: LlavaConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()

        self.config = config
        self.vision_tower = None

        if getattr(self.config, "vision_config", None) is None:
            self.config.vision_config = CLIPVisionConfig(self.config.mm_vision_tower)
        if getattr(self.config, "text_config", None) is None:
            self.config.text_config = MistralConfig(self.config._name_or_path)

        self.config.vision_config.hidden_size = config.mm_hidden_size
        self.config.text_config.hidden_size = config.hidden_size

        if getattr(self.config, "projector_hidden_act", None) is None:
            self.config.projector_hidden_act = "gelu"
        if getattr(self.config, "image_token_index", None) is None:
            self.config.image_token_index = 32000

        self.multi_modal_projector = LlavaMultiModalProjector(config)
        self.language_model = MistralForCausalLM(config, quant_config=quant_config)
        if "unpad" in getattr(config, "mm_patch_merge_type", ""):
            self.language_model.model.image_newline = nn.Parameter(
                torch.empty(config.text_config.hidden_size, dtype=torch.float16)
            )


EntryClass = [LlavaLlamaForCausalLM, LlavaQwenForCausalLM, LlavaMistralForCausalLM]
