# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

import math
from collections.abc import Iterable, Mapping, Sequence
from functools import partial
from typing import List, Optional, Tuple, Union

import numpy as np

from .....utils.deps import is_dep_available

if all(
    map(is_dep_available, ("einops", "torch", "transformers", "vllm", "flash-attn"))
):
    import torch
    import torch.nn as nn
    from einops import rearrange, repeat
    from transformers import BatchFeature
    from transformers.activations import GELUActivation
    from transformers.modeling_outputs import (
        BaseModelOutput,
        BaseModelOutputWithPooling,
    )
    from transformers.utils import torch_int
    from vllm.compilation.decorators import support_torch_compile
    from vllm.config import VllmConfig
    from vllm.distributed import 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.logits_processor import LogitsProcessor
    from vllm.model_executor.layers.quantization import QuantizationConfig
    from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
    from vllm.model_executor.model_loader.weight_utils import (
        default_weight_loader,
        maybe_remap_kv_scale_name,
    )
    from vllm.model_executor.models.vision import get_vit_attn_backend
    from vllm.platforms import _Backend, current_platform

    try:
        from vllm.model_executor.models.ernie45 import Ernie4_5_ForCausalLM
    except ImportError:
        from vllm.model_executor.models.ernie45 import (
            Ernie4_5ForCausalLM as Ernie4_5_ForCausalLM,
        )
    from vllm.model_executor.models.interfaces import SupportsMultiModal
    from vllm.model_executor.models.utils import (
        AutoWeightsLoader,
        PPMissingLayer,
        is_pp_missing_parameter,
        merge_multimodal_embeddings,
    )
    from vllm.multimodal import MULTIMODAL_REGISTRY
    from vllm.multimodal.inputs import (
        MultiModalDataDict,
        MultiModalFieldConfig,
        MultiModalKwargs,
        NestedTensors,
    )
    from vllm.multimodal.parse import (
        ImageProcessorItems,
        ImageSize,
        MultiModalDataItems,
    )
    from vllm.multimodal.processing import (
        BaseMultiModalProcessor,
        BaseProcessingInfo,
        PromptReplacement,
        PromptUpdate,
    )
    from vllm.multimodal.profiling import BaseDummyInputsBuilder
    from vllm.sequence import IntermediateTensors

    def smart_resize(
        height: int,
        width: int,
        factor: int = 28,
        min_pixels: int = 28 * 28 * 130,
        max_pixels: int = 28 * 28 * 1280,
    ):
        """Rescales the image so that the following conditions are met:

        1. Both dimensions (height and width) are divisible by 'factor'.

        2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].

        3. The aspect ratio of the image is maintained as closely as possible.

        """

        if height < factor:
            width = round((width * factor) / height)
            height = factor

        if width < factor:
            height = round((height * factor) / width)
            width = factor

        if max(height, width) / min(height, width) > 200:
            raise ValueError(
                f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}"
            )
        h_bar = round(height / factor) * factor
        w_bar = round(width / factor) * factor
        if h_bar * w_bar > max_pixels:
            beta = math.sqrt((height * width) / max_pixels)
            h_bar = math.floor(height / beta / factor) * factor
            w_bar = math.floor(width / beta / factor) * factor
        elif h_bar * w_bar < min_pixels:
            beta = math.sqrt(min_pixels / (height * width))
            h_bar = math.ceil(height * beta / factor) * factor
            w_bar = math.ceil(width * beta / factor) * factor
        return h_bar, w_bar

    class PaddleOCRVLProcessingInfo(BaseProcessingInfo):

        def get_hf_config(self):
            return self.ctx.get_hf_config()

        def get_hf_processor(self, **kwargs: object):
            return self.ctx.get_hf_processor(**kwargs)

        def get_image_processor(self, **kwargs: object):
            return self.get_hf_processor(**kwargs).image_processor

        def get_supported_mm_limits(self):
            return {"image": None}

        def get_num_image_tokens(
            self,
            *,
            image_width: int,
            image_height: int,
            image_processor,
        ) -> int:
            if image_processor is None:
                image_processor = self.get_image_processor()

            do_resize = True
            hf_config = self.get_hf_config()
            vision_config = hf_config.vision_config
            patch_size = vision_config.patch_size
            merge_size = vision_config.spatial_merge_size

            if do_resize:
                resized_height, resized_width = smart_resize(
                    height=image_height,
                    width=image_width,
                    factor=patch_size * merge_size,
                    min_pixels=image_processor.min_pixels,
                    max_pixels=image_processor.max_pixels,
                )
                preprocessed_size = ImageSize(
                    width=resized_width, height=resized_height
                )
            else:
                preprocessed_size = ImageSize(width=image_width, height=image_height)

            grid_t = 1
            grid_h = preprocessed_size.height // patch_size
            grid_w = preprocessed_size.width // patch_size

            num_patches = grid_t * grid_h * grid_w
            num_image_tokens = num_patches // (merge_size**2)

            return num_image_tokens

        def get_image_size_with_most_features(self) -> ImageSize:
            hf_config = self.get_hf_config()
            image_size = hf_config.vision_config.image_size
            return ImageSize(height=image_size, width=image_size)

    class PaddleOCRVLDummyInputsBuilder(
        BaseDummyInputsBuilder[PaddleOCRVLProcessingInfo]
    ):

        def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
            num_images = mm_counts.get("image", 0)

            processor = self.info.get_hf_processor()
            image_token = processor.image_token

            return image_token * num_images

        def get_dummy_mm_data(
            self,
            seq_len: int,
            mm_counts: Mapping[str, int],
        ) -> MultiModalDataDict:
            num_images = mm_counts.get("image", 0)

            (target_width, target_height) = (
                self.info.get_image_size_with_most_features()
            )

            return {
                "image": self._get_dummy_images(
                    width=target_width, height=target_height, num_images=num_images
                )
            }

    class PaddleOCRVLMultiModalProcessor(
        BaseMultiModalProcessor[PaddleOCRVLProcessingInfo]
    ):

        def _call_hf_processor(
            self,
            prompt: str,
            mm_data: Mapping[str, object],
            mm_kwargs: Mapping[str, object],
            tok_kwargs: Mapping[str, object],
        ) -> BatchFeature:
            if mm_data:
                processed_outputs = self.info.ctx.call_hf_processor(
                    self.info.get_hf_processor(**mm_kwargs),
                    dict(text=prompt, **mm_data),
                    dict(**mm_kwargs, **tok_kwargs),
                )
                processed_outputs["pixel_values"] = processed_outputs[
                    "pixel_values"
                ].unsqueeze(0)
            else:
                tokenizer = self.info.get_tokenizer()
                processed_outputs = tokenizer(
                    prompt, add_special_tokens=True, return_tensors="pt"
                )
            return processed_outputs

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

        def _get_prompt_updates(
            self,
            mm_items: MultiModalDataItems,
            hf_processor_mm_kwargs: Mapping[str, object],
            out_mm_kwargs: MultiModalKwargs,
        ) -> Sequence[PromptUpdate]:
            image_processor = self.info.get_image_processor(**hf_processor_mm_kwargs)
            hf_config = self.info.get_hf_config()
            image_token_id = hf_config.image_token_id

            def get_replacement(item_idx: int, image_processor):
                images = mm_items.get_items("image", ImageProcessorItems)

                image_size = images.get_image_size(item_idx)
                num_image_tokens = self.info.get_num_image_tokens(
                    image_width=image_size.width,
                    image_height=image_size.height,
                    image_processor=image_processor,
                )

                return [image_token_id] * num_image_tokens

            return [
                PromptReplacement(
                    modality="image",
                    target=[image_token_id],
                    replacement=partial(
                        get_replacement, image_processor=image_processor
                    ),
                ),
            ]

    class Projector(nn.Module):

        def __init__(
            self,
            text_config,
            vision_config,
            prefix: str = "",
        ):
            super().__init__()
            self.text_config = text_config
            self.vision_config = vision_config
            self.merge_kernel_size = (2, 2)

            self.hidden_size = (
                self.vision_config.hidden_size
                * self.merge_kernel_size[0]
                * self.merge_kernel_size[1]
            )

            self.pre_norm = torch.nn.LayerNorm(
                self.vision_config.hidden_size, eps=1e-05
            )
            self.linear_1 = nn.Linear(self.hidden_size, self.hidden_size, bias=True)
            self.act = GELUActivation()
            self.linear_2 = nn.Linear(
                self.hidden_size, self.text_config.hidden_size, bias=True
            )

        def forward(
            self,
            image_features: torch.Tensor,
            image_grid_thw: List[Tuple[int, int, int]],
        ) -> torch.Tensor:
            m1, m2 = self.merge_kernel_size
            if isinstance(image_features, (list, tuple)):
                processed_features = list()
                for image_feature, image_grid in zip(image_features, image_grid_thw):
                    image_feature = self.pre_norm(image_feature)
                    t, h, w = image_grid

                    image_feature = rearrange(
                        image_feature,
                        "(t h p1 w p2) d -> (t h w) (p1 p2 d)",
                        t=t,
                        h=h // m1,
                        p1=m1,
                        w=w // m2,
                        p2=m2,
                    )
                    hidden_states = self.linear_1(image_feature)
                    hidden_states = self.act(hidden_states)
                    hidden_states = self.linear_2(hidden_states)
                    processed_features.append(hidden_states)

                return processed_features

            dims = image_features.shape[:-1]
            dim = image_features.shape[-1]
            image_features = image_features.view(np.prod(dims), dim)
            hidden_states = self.pre_norm(image_features).view(-1, self.hidden_size)
            hidden_states = self.linear_1(hidden_states)
            hidden_states = self.act(hidden_states)
            hidden_states = self.linear_2(hidden_states)

            return hidden_states.view(*dims, -1)

    class SiglipVisionEmbeddings(nn.Module):

        def __init__(self, config):
            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.cache_position_embedding = dict()
            self.cache_position_count = dict()
            self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
            self.packing_position_embedding = nn.Embedding(32768, self.embed_dim)

            self.register_buffer(
                "position_ids",
                torch.arange(self.num_positions).expand((1, -1)),
                persistent=False,
            )

        def interpolate_pos_encoding(
            self,
            embeddings: torch.Tensor,
            height: int,
            width: int,
            is_after_patchify: bool = False,
        ) -> torch.Tensor:

            num_positions = self.position_embedding.weight.shape[0]

            patch_pos_embed = self.position_embedding.weight.unsqueeze(0)

            dim = embeddings.shape[-1]

            if is_after_patchify:
                new_height = height
                new_width = width
            else:
                new_height = height // self.patch_size
                new_width = width // self.patch_size

            sqrt_num_positions = torch_int(num_positions**0.5)
            patch_pos_embed = patch_pos_embed.reshape(
                1, sqrt_num_positions, sqrt_num_positions, dim
            )
            patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)

            patch_pos_embed = nn.functional.interpolate(
                patch_pos_embed,
                size=(new_height, new_width),
                mode="bilinear",
                align_corners=False,
            )

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

        def fetch_position_embedding_lfu_cache(
            self, embeddings, h, w, max_cache: int = 20
        ):
            grid = (h, w)
            if grid in self.cache_position_embedding:
                self.cache_position_count[grid] += 1
                return self.cache_position_embedding[grid]

            if len(self.cache_position_embedding) >= max_cache:
                min_hit_grid = min(
                    self.cache_position_count,
                    key=self.cache_position_count.get,
                )
                self.cache_position_count.pop(min_hit_grid)
                self.cache_position_embedding.pop(min_hit_grid)

            position_embedding = self.interpolate_pos_encoding(embeddings, h, w, True)
            self.cache_position_count[grid] = 1
            self.cache_position_embedding[grid] = position_embedding
            return position_embedding

        def forward(
            self,
            pixel_values: torch.FloatTensor,
            position_ids: Optional[torch.Tensor] = None,
            image_grid_thw: Optional[
                List[
                    Union[
                        Tuple[int, int, int],
                        List[Tuple[int, int, int]],
                    ]
                ]
            ] = None,
            interpolate_pos_encoding=False,
        ) -> torch.Tensor:
            if pixel_values.dim() == 4:
                pixel_values = pixel_values.unsqueeze(0)
            if pixel_values.dim() == 5:
                if position_ids is None:
                    raise ValueError(
                        "position_ids cannot be None when pixel_values.dim() is 5."
                    )
                (
                    batch_size,
                    squence_len,
                    channel,
                    height,
                    width,
                ) = pixel_values.shape
                target_dtype = self.patch_embedding.weight.dtype
                pixel_values = rearrange(pixel_values, "b l c h w -> (b l) c h w")
                patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))
                embeddings = patch_embeds.flatten(-2).squeeze(-1)

                if interpolate_pos_encoding and image_grid_thw is not None:
                    start = 0
                    tmp_embeddings = list()
                    for image_grid in image_grid_thw:
                        t, h, w = image_grid
                        end = start + t * h * w
                        image_embeddings = embeddings[start:end, :]
                        position_embedding = (
                            self.interpolate_pos_encoding(image_embeddings, h, w, True)
                            .squeeze(0)
                            .repeat(t, 1)
                        )
                        image_embeddings = image_embeddings + position_embedding
                        tmp_embeddings.append(image_embeddings)
                        start = end
                    embeddings = torch.concat(tmp_embeddings, dim=0).unsqueeze(0)
                else:
                    embeddings = embeddings + self.packing_position_embedding(
                        position_ids
                    )
                return embeddings
            else:
                raise ValueError(
                    "Unsupported pixel_values dimension:"
                    f" {pixel_values.dim()}. Expected 4 or 5."
                )

    def rotate_half(x: torch.Tensor, interleaved: bool = False) -> torch.Tensor:
        if not interleaved:
            x1, x2 = x.chunk(2, dim=-1)
            return torch.cat((-x2, x1), dim=-1)
        else:
            x1, x2 = x[..., ::2], x[..., 1::2]
            return rearrange(
                torch.stack((-x2, x1), dim=-1), "... d two -> ... (d two)", two=2
            )

    def apply_rotary_emb_torch(
        x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, interleaved: bool = False
    ) -> torch.Tensor:
        """
        x: (batch_size, seqlen, nheads, headdim)
        cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2)
        """
        ro_dim = cos.shape[-1] * 2
        assert ro_dim <= x.shape[-1]
        cos = repeat(
            cos, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
        )
        sin = repeat(
            sin, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
        )
        return torch.cat(
            [
                x[..., :ro_dim] * cos + rotate_half(x[..., :ro_dim], interleaved) * sin,
                x[..., ro_dim:],
            ],
            dim=-1,
        )

    def apply_rotary_pos_emb_flashatt(
        q: torch.Tensor,
        k: torch.Tensor,
        cos: torch.Tensor,
        sin: torch.Tensor,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        cos = cos.chunk(2, dim=-1)[0].contiguous()
        sin = sin.chunk(2, dim=-1)[0].contiguous()

        apply_rotary_emb = apply_rotary_emb_torch
        if current_platform.is_cuda():
            from vllm.vllm_flash_attn.layers.rotary import apply_rotary_emb

        q_embed = apply_rotary_emb(q.float(), cos.float(), sin.float()).type_as(q)
        k_embed = apply_rotary_emb(k.float(), cos.float(), sin.float()).type_as(k)
        return q_embed, k_embed

    class SiglipAttention(nn.Module):
        """Multi-headed attention from 'Attention Is All You
        Need' paper."""

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

            hidden_size = config.hidden_size
            self.hidden_size = config.hidden_size
            tp_size = get_tensor_model_parallel_world_size()
            self.total_num_heads = config.num_attention_heads
            assert self.total_num_heads % tp_size == 0
            self.num_heads = self.total_num_heads // tp_size
            self.total_num_kv_heads = config.num_attention_heads
            if self.total_num_kv_heads >= tp_size:
                assert self.total_num_kv_heads % tp_size == 0
            else:
                assert tp_size % self.total_num_kv_heads == 0
            self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
            self.head_dim = config.hidden_size // self.total_num_heads
            self.q_size = self.num_heads * self.head_dim
            self.kv_size = self.num_kv_heads * self.head_dim
            self.scale = self.head_dim**-0.5

            self.qkv_proj = QKVParallelLinear(
                hidden_size,
                self.head_dim,
                self.total_num_heads,
                self.total_num_kv_heads,
                bias=True,
                quant_config=quant_config,
                prefix=f"{prefix}.qkv_proj",
            )
            self.out_proj = RowParallelLinear(
                input_size=hidden_size,
                output_size=hidden_size,
                quant_config=quant_config,
                prefix=f"{prefix}.out_proj",
            )

            # Detect attention implementation.
            self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
            if self.attn_backend not in {
                _Backend.FLASH_ATTN,
                _Backend.TORCH_SDPA,
                _Backend.XFORMERS,
            }:
                raise RuntimeError(
                    f"PaddleOCR-VL does not support {self.attn_backend} backend now."
                )

        def forward(
            self,
            hidden_states: torch.Tensor,
            cu_seqlens: Optional[List[torch.Tensor]] = None,
            rope_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        ) -> torch.Tensor:
            batch_size, seq_length, embed_dim = hidden_states.shape

            qkv_states, _ = self.qkv_proj(hidden_states)
            q, k, v = qkv_states.chunk(3, dim=-1)

            q = q.view(batch_size, seq_length, self.num_heads, self.head_dim)
            k = k.view(batch_size, seq_length, self.num_heads, self.head_dim)
            v = v.view(batch_size, seq_length, self.num_heads, self.head_dim)

            if rope_emb is not None:
                cos, sin = rope_emb
                q, k = apply_rotary_pos_emb_flashatt(q, k, cos, sin)

            if self.attn_backend == _Backend.FLASH_ATTN:
                from flash_attn import flash_attn_varlen_func

                q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])
                max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
                output = flash_attn_varlen_func(
                    q,
                    k,
                    v,
                    cu_seqlens_q=cu_seqlens,
                    cu_seqlens_k=cu_seqlens,
                    max_seqlen_q=max_seqlen,
                    max_seqlen_k=max_seqlen,
                )

                context_layer = rearrange(output, "(b s) ... -> b s ...", b=batch_size)
            elif self.attn_backend == _Backend.TORCH_SDPA:
                # Execute attention entry by entry for speed & less VRAM.
                import torch.nn.functional as F

                outputs = []
                for i in range(1, len(cu_seqlens)):
                    start_idx = cu_seqlens[i - 1]
                    end_idx = cu_seqlens[i]
                    q_i = q[:, start_idx:end_idx]
                    k_i = k[:, start_idx:end_idx]
                    v_i = v[:, start_idx:end_idx]
                    q_i, k_i, v_i = (
                        rearrange(x, "b s h d -> b h s d") for x in [q_i, k_i, v_i]
                    )
                    output_i = F.scaled_dot_product_attention(
                        q_i, k_i, v_i, dropout_p=0.0
                    )
                    output_i = rearrange(output_i, "b h s d -> b s h d ")
                    outputs.append(output_i)
                context_layer = torch.cat(outputs, dim=1)
            elif self.attn_backend == _Backend.XFORMERS:
                from xformers import ops as xops
                from xformers.ops.fmha.attn_bias import BlockDiagonalMask

                seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
                attn_bias = BlockDiagonalMask.from_seqlens(
                    q_seqlen=seqlens, kv_seqlen=None, device=q.device
                )

                context_layer = xops.memory_efficient_attention_forward(
                    q, k, v, attn_bias=attn_bias, p=0, scale=None
                )

            context_layer = rearrange(
                context_layer, "b s h d -> b s (h d)"
            ).contiguous()

            output, _ = self.out_proj(context_layer)
            return output

    class SigLIPRotaryEmbedding(nn.Module):

        def __init__(self, dim: int, theta: float = 10000.0) -> None:
            super().__init__()
            self.dim = dim
            self.theta = theta
            self.rope_init()

        def rope_init(self):
            inv_freq = 1.0 / (
                self.theta
                ** (torch.arange(0, self.dim, 2, dtype=torch.float) / self.dim)
            )
            self.register_buffer("inv_freq", inv_freq, persistent=False)

        def forward(self, seqlen: int) -> torch.Tensor:
            seq = torch.arange(
                seqlen,
                device=self.inv_freq.device,
                dtype=self.inv_freq.dtype,
            )
            freqs = torch.outer(seq, self.inv_freq)
            return freqs

    class SiglipMLP(nn.Module):

        def __init__(
            self,
            config,
            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,
            quant_config: Optional[QuantizationConfig] = None,
            prefix: str = "",
        ):
            super().__init__()
            self.embed_dim = config.hidden_size
            self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
            self.self_attn = SiglipAttention(
                config,
                quant_config=quant_config,
                prefix=f"{prefix}.self_attn",
            )
            self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
            self.mlp = SiglipMLP(
                config,
                quant_config=quant_config,
                prefix=f"{prefix}.mlp",
            )

        def forward(
            self,
            hidden_states: torch.Tensor,
            cu_seqlens: Optional[List[torch.Tensor]] = None,
            rope_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
        ) -> Tuple[torch.FloatTensor]:

            residual = hidden_states

            hidden_states = self.layer_norm1(hidden_states)
            hidden_states = self.self_attn(
                hidden_states=hidden_states,
                cu_seqlens=cu_seqlens,
                rope_emb=rope_emb,
            )

            hidden_states = residual + hidden_states

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

            hidden_states = residual + hidden_states

            return hidden_states

    class SiglipEncoder(nn.Module):

        def __init__(
            self,
            config,
            quant_config: Optional[QuantizationConfig] = None,
            prefix: str = "",
        ):
            super().__init__()
            self.config = config
            embed_dim = config.hidden_size
            num_heads = config.num_attention_heads
            head_dim = embed_dim // num_heads
            self.layers = nn.ModuleList(
                [
                    SiglipEncoderLayer(
                        config,
                        quant_config=quant_config,
                        prefix=f"{prefix}.layers.{layer_idx}",
                    )
                    for layer_idx in range(config.num_hidden_layers)
                ]
            )
            self.rotary_pos_emb = SigLIPRotaryEmbedding(head_dim // 2)

        @staticmethod
        def flatten_list(image_grid_thw):
            tmp_image_grid_thw = list()
            for image_grid in image_grid_thw:
                if isinstance(image_grid, list):
                    tmp_image_grid_thw.extend(image_grid)
                else:
                    tmp_image_grid_thw.append(image_grid)
            return tmp_image_grid_thw

        def forward(
            self,
            inputs_embeds,
            cu_seqlens: Optional[List[torch.Tensor]] = None,
            image_grid_thw: Optional[
                List[
                    Union[
                        Tuple[int, int, int],
                        List[Tuple[int, int, int]],
                    ]
                ]
            ] = None,
            height_position_ids: Optional[torch.Tensor] = None,
            width_position_ids: Optional[torch.Tensor] = None,
        ) -> BaseModelOutput:
            device = inputs_embeds.device
            hidden_states = inputs_embeds

            flatten_image_grid_thw = self.flatten_list(image_grid_thw)

            if width_position_ids is None or height_position_ids is None:
                split_hids = list()
                split_wids = list()
                for t, h, w in flatten_image_grid_thw:
                    image_pids = torch.arange(t * h * w, device=device) % (h * w)
                    sample_hids = image_pids // w
                    sample_wids = image_pids % w
                    split_hids.append(sample_hids)
                    split_wids.append(sample_wids)
                width_position_ids = torch.concat(split_wids, dim=0)
                height_position_ids = torch.concat(split_hids, dim=0)

            pids = torch.stack(
                [height_position_ids, width_position_ids],
                dim=-1,
            )
            max_grid_size = pids.max() + 1
            rope_emb_max_grid = self.rotary_pos_emb(max_grid_size)
            rope_emb = rope_emb_max_grid[pids].flatten(1)
            rope_emb = rope_emb.repeat(1, 2)
