"""Inference-only LLaVa video model compatible with HuggingFace weights."""

from typing import List, Iterable, Optional, Tuple

import numpy as np
import torch
from torch import nn
from transformers import CLIPVisionModel, LlavaConfig
from transformers.models.llava.modeling_llava import LlavaMultiModalProjector
from vllm.config import CacheConfig
from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader

from sglang.srt.managers.controller.infer_batch import ForwardMode
from sglang.srt.managers.controller.model_runner import InputMetadata
from sglang.srt.mm_utils import (
    get_anyres_image_grid_shape,
    unpad_image,
    unpad_image_shape,
)
from sglang.srt.models.llama2 import LlamaForCausalLM


class LlavaVidForCausalLM(nn.Module):
    def __init__(
        self,
        config: LlavaConfig,
        quant_config: Optional[QuantizationConfig] = None,
        cache_config: Optional[CacheConfig] = 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.mm_spatial_pool_stride = getattr(self.config, "mm_spatial_pool_stride", 2)
        self.resampler = nn.AvgPool2d(
            kernel_size=self.mm_spatial_pool_stride, stride=self.mm_spatial_pool_stride
        )
        self.language_model = LlamaForCausalLM(config, quant_config=quant_config)
        self.num_frames = getattr(self.config, "num_frames", 16)
        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)
            )

    def pad_input_ids(self, input_ids, pad_value, pt_shape=None, image_size=None):
        new_image_feature_len = self.image_feature_len
        # now only support spatial_unpad + anyres
        # if self.mm_patch_merge_type.startswith("spatial"):
        #     height = width = self.num_patches_per_side
        #     if pt_shape[0] > 1:
        #         if self.image_aspect_ratio == "anyres":
        #             num_patch_width, num_patch_height = get_anyres_image_grid_shape(
        #                 image_size,
        #                 self.image_grid_pinpoints,
        #                 self.vision_tower.config.image_size,
        #             )
        #         if "unpad" in self.mm_patch_merge_type:
        #             h = num_patch_height * height
        #             w = num_patch_width * width
        #             new_h, new_w = unpad_image_shape(h, w, image_size)
        #             new_image_feature_len += new_h * (new_w + 1)

        pad_ids = pad_value * (
            (new_image_feature_len + len(pad_value)) // len(pad_value)
        )
        offset = input_ids.index(self.config.image_token_index)
        # old_len + pad_len - 1, because we need to remove image_token_id
        new_input_ids = (
            input_ids[:offset]
            + pad_ids[:new_image_feature_len]
            + input_ids[offset + 1 :]
        )
        return new_input_ids, offset

    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}"
            )

        height = width = self.num_patches_per_side
        num_of_frames = selected_image_feature.shape[0]
        selected_image_feature = selected_image_feature.view(
            num_of_frames, height, width, -1
        )
        selected_image_feature = selected_image_feature.permute(0, 3, 1, 2).contiguous()
        selected_image_feature = (
            self.resampler(selected_image_feature)
            .flatten(2)
            .transpose(1, 2)
            .contiguous()
        )

        image_features = self.multi_modal_projector(selected_image_feature)

        return image_features

    def forward(
        self,
        input_ids: torch.LongTensor,
        positions: torch.Tensor,
        input_metadata: InputMetadata,
        pixel_values: Optional[List[Optional[np.array]]] = None,
        image_sizes: Optional[List[List[int]]] = None,
        image_offsets: Optional[List[int]] = None,
    ) -> torch.Tensor:
        if input_metadata.forward_mode == ForwardMode.EXTEND:
            bs = input_metadata.batch_size

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

            # Embed vision input
            need_vision = (
                (positions[input_metadata.extend_start_loc] < self.image_feature_len)
                .cpu()
                .numpy()
            )
            # FIXME: We need to substract the length of the system prompt
            has_pixel = np.array([pixel_values[i] is not None for i in range(bs)])
            need_vision = need_vision & has_pixel

            if need_vision.any():
                pixel_values = [pixel_values[i] for i in range(bs) if need_vision[i]]
                image_sizes = [image_sizes[i] 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)
                    image_features = self.encode_images(
                        concat_images
                    )  # , prompts)#, image_counts, long_video=long_video)
                    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

                new_image_features = []
                for image_idx, image_feature in enumerate(image_features):
                    new_image_features.append(image_feature.flatten(0, 1))
                image_features = new_image_features

                extend_start_loc_cpu = input_metadata.extend_start_loc.cpu().numpy()
                pt = 0
                for i in range(bs):
                    if not need_vision[i]:
                        continue

                    start_idx = extend_start_loc_cpu[i]
                    pad_len, pad_dim = image_features[pt].shape  # 576, 4096
                    dim = input_embeds.shape[1]
                    assert (
                        pad_dim == dim
                    ), "invalid pad_dim={}, input_embed_dim={}!".format(pad_dim, dim)
                    # Fill in the placeholder for the image
                    try:
                        input_embeds[
                            start_idx
                            + image_offsets[i] : start_idx
                            + image_offsets[i]
                            + pad_len
                        ] = image_features[pt]
                    except RuntimeError as e:
                        print(f"RuntimeError in llava image encoding: {e}")
                        print(input_embeds.shape)
                        print(start_idx, image_offsets[i])
                    pt += 1

            return self.language_model(
                input_ids, positions, input_metadata, input_embeds=input_embeds
            )
        elif input_metadata.forward_mode == ForwardMode.DECODE:
            return self.language_model(input_ids, positions, input_metadata)

    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
        vision_path = self.config.mm_vision_tower
        self.vision_tower = CLIPVisionModel.from_pretrained(
            vision_path, torch_dtype=torch.float16
        ).cuda()
        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)

        print(f"target_frames: {self.num_frames}")
        self.image_feature_len = self.num_frames * int(
            (self.image_size / self.patch_size / self.mm_spatial_pool_stride) ** 2
        )
        if self.vision_feature_select_strategy == "patch":
            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_resampler.mm_projector.0": "multi_modal_projector.linear_1",
            "model.vision_resampler.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).
        }
        params_dict = dict(self.named_parameters())
        weights = list(weights)
        for name, loaded_weight in weights:
            # FIXME: why projector weights read two times?
            if "projector" in name or "vision_tower" in name:
                for weight_name, param_name in projector_weights.items():
                    if weight_name in name:
                        name = name.replace(weight_name, param_name)
                if name in params_dict:
                    param = params_dict[name]
                else:
                    print(f"Warning: {name} not found in the model")
                    continue
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)

        # load language model
        self.language_model.load_weights(weights)

        monkey_path_clip_vision_embed_forward()

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


first_call = True


def clip_vision_embed_forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
    batch_size = pixel_values.shape[0]

    # Move this conv layer to CPU to avoid a bug in torch >= 2.1 on A10G.
    global first_call
    if first_call:
        self.patch_embedding.cpu().float()
        first_call = False
    pixel_values = pixel_values.to(dtype=torch.float32, device="cpu")
    patch_embeds = self.patch_embedding(pixel_values).cuda().half()

    patch_embeds = patch_embeds.flatten(2).transpose(1, 2)

    class_embeds = self.class_embedding.expand(batch_size, 1, -1)
    embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
    embeddings = embeddings + self.position_embedding(self.position_ids)
    return embeddings


def monkey_path_clip_vision_embed_forward():
    import transformers

    setattr(
        transformers.models.clip.modeling_clip.CLIPVisionEmbeddings,
        "forward",
        clip_vision_embed_forward,
    )


EntryClass = LlavaVidForCausalLM
