"""
# 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 os
from typing import List, Optional, Tuple, Union

import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddleformers.transformers.model_utils import PretrainedModel

from fastdeploy.model_executor.utils import h2d_copy, slice_fn

from .config import PaddleOCRVisionConfig
from .siglip_ops import get_activation_fn, neox_rope_embedding


class SiglipAttention(nn.Layer):
    def __init__(self, config):
        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
        assert self.head_dim * self.num_heads == self.embed_dim
        self.scale = self.head_dim**-0.5

        # qkv_linear
        self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3, bias_attr=True)
        self.qkv_proj.weight.weight_loader = self.qkv_weight_loader
        self.qkv_proj.bias.weight_loader = self.qkv_weight_loader

        # out_linear
        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
        self.out_proj.weight.weight_loader = self.out_proj_weight_loader

        enable_fa3 = False
        flash_attn_version = int(os.environ.get("FLAGS_flash_attn_version", "2"))
        if flash_attn_version == 3:
            prop = paddle.device.cuda.get_device_properties()
            cc = prop.major * 10 + prop.minor
            is_current_sm_supported = cc >= 90
            is_paddle_supported = any(num >= 90 for num in paddle.version.cuda_archs())
            enable_fa3 = is_current_sm_supported and is_paddle_supported

        if enable_fa3:
            from paddle.nn.functional.flash_attention import flash_attention_v3_varlen

            self.flash_attn_func = flash_attention_v3_varlen
            self.flash_attn_kwargs = {}
        else:
            from paddle.nn.functional.flash_attention import flash_attn_unpadded

            self.flash_attn_func = flash_attn_unpadded
            self.flash_attn_kwargs = {"scale": self.scale, "training": False}

    def qkv_weight_loader(self, param, loaded_weight, loaded_shard_id: Optional[str] = None):
        # Tensor parallelism splits the weight along the output_dim
        if loaded_weight.dim() == 2:
            loaded_weight = loaded_weight.transpose([1, 0])

        if not param._is_initialized():
            param.initialize()
        if loaded_shard_id == "q":
            param_shard_offset = 0
            param_shard_size = self.num_heads * self.head_dim
        elif loaded_shard_id == "k":
            param_shard_offset = self.num_heads * self.head_dim
            param_shard_size = self.num_heads * self.head_dim
        else:
            # loaded_shard_id == "v"
            param_shard_offset = self.num_heads * self.head_dim * 2
            param_shard_size = self.num_heads * self.head_dim

        param = slice_fn(param, -1, start=param_shard_offset, end=param_shard_offset + param_shard_size)
        assert param.shape == loaded_weight.shape, (
            f" Attempted to load weight ({loaded_weight.shape}) " f"into parameter ({param.shape})"
        )
        # Ensure loaded weight dtype matches model param dtype
        if loaded_weight.dtype != param.dtype:
            if loaded_weight.dtype == paddle.int8 and param.dtype == paddle.float8_e4m3fn:
                loaded_weight = loaded_weight.view(param.dtype)
            else:
                loaded_weight = loaded_weight.cast(param.dtype)
        h2d_copy(param, loaded_weight)

    def out_proj_weight_loader(self, param, loaded_weight, loaded_shard_id: Optional[str] = None):
        loaded_weight = loaded_weight.transpose([1, 0])
        if not param._is_initialized():
            param.initialize()
        assert param.shape == loaded_weight.shape, (
            f" Attempted to load weight ({loaded_weight.shape}) " f"into parameter ({param.shape})"
        )
        # Ensure loaded weight dtype matches model param dtype
        if loaded_weight.dtype != param.dtype:
            if loaded_weight.dtype == paddle.int8 and param.dtype == paddle.float8_e4m3fn:
                loaded_weight = loaded_weight.view(param.dtype)
            else:
                loaded_weight = loaded_weight.cast(param.dtype)
        h2d_copy(param, loaded_weight)

    def forward(
        self,
        hidden_states: paddle.Tensor,  # [B, L, D]
        attention_mask: Optional[paddle.Tensor] = None,
        output_attentions: Optional[bool] = False,
        cu_seqlens: Optional[List[paddle.Tensor]] = None,
        max_seqlen: Optional[paddle.Tensor] = None,
        cos_emb: Optional[paddle.Tensor] = None,  # (cos, sin)
        sin_emb: Optional[paddle.Tensor] = None,  # (cos, sin)
    ):
        B, seq_length, D = hidden_states.shape
        qkv = self.qkv_proj(hidden_states)
        q, k, v = neox_rope_embedding(qkv, cos_emb, sin_emb, self.num_heads, self.head_dim)
        attn_output = self.flash_attn_func(
            q,
            k,
            v,
            cu_seqlens,
            cu_seqlens,
            max_seqlen,
            max_seqlen,
            causal=False,
            **self.flash_attn_kwargs,
        )[0]

        attn_output = attn_output.reshape((seq_length, -1))
        attn_output = self.out_proj(attn_output)
        return attn_output


class SiglipVisionEmbeddings(nn.Layer):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size  # 1152
        self.image_size = config.image_size  # 384
        self.patch_size = config.patch_size  # 14

        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  # 729
        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",
            paddle.arange(self.num_positions).unsqueeze(0),
            persistable=False,
        )

    def interpolate_pos_encoding(self, embeddings, height: int, width: int, is_after_patchify: bool = False):

        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 = paddle.to_tensor(num_positions**0.5, dtype=paddle.int64)
        patch_pos_embed = patch_pos_embed.reshape((1, sqrt_num_positions, sqrt_num_positions, dim))
        patch_pos_embed = patch_pos_embed.transpose((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.transpose((0, 2, 3, 1)).reshape((1, -1, dim))
        return patch_pos_embed

    @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 fetch_position_embedding_lfu_cache(self, embeddings, h, w, max_cache=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: paddle.Tensor,  # [B, L, C, H, W]
        position_ids: Optional[paddle.Tensor] = None,  # [B or 1, S]
        image_grid_thw: Optional[List[Union[Tuple[int, int, int], List[Tuple[int, int, int]]]]] = None,
        interpolate_pos_encoding: bool = False,
    ) -> paddle.Tensor:
        if pixel_values.dim() == 4:
            pixel_values = pixel_values.unsqueeze(0)
        if pixel_values.dim() == 5:
            assert position_ids is not None
            from einops import rearrange

            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))  # shape = [*, width, grid, grid]
            embeddings = patch_embeds.flatten(-2).squeeze(-1)
            embeddings = rearrange(embeddings, "(b l) d -> b l d", b=batch_size, l=squence_len)
            # todo: not debug
            if interpolate_pos_encoding and image_grid_thw is not None:
                flatten_image_grid_thw = self.flatten_list(image_grid_thw)
                flatten_image_grid_thw = np.array(flatten_image_grid_thw)
                assert batch_size == 1
                start = 0

                assert sum([np.prod(x) for x in flatten_image_grid_thw]) == embeddings.shape[1], (
                    flatten_image_grid_thw,
                    embeddings.shape,
                )
                embeddings = embeddings.squeeze(0)
                tmp_embeddings = list()
                for image_grid in image_grid_thw:
                    t, h, w = image_grid
                    end = start + t * h * w
                    image_embeddings = embeddings[int(start) : int(end), :]
                    position_embedding = (
                        self.interpolate_pos_encoding(image_embeddings, h, w, True).squeeze(0).tile((t, 1))
                    ).astype(image_embeddings.dtype)
                    image_embeddings = image_embeddings + position_embedding
                    tmp_embeddings.append(image_embeddings)
                    start = end
                embeddings = paddle.concat(tmp_embeddings, axis=0).unsqueeze(0)
            else:
                embeddings = embeddings + self.packing_position_embedding(position_ids)
            return embeddings
        else:
            raise NotImplementedError(str(pixel_values.shape))


class SiglipMLP(nn.Layer):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
        self.fc1.weight.weight_loader = self.weight_loader
        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
        self.fc2.weight.weight_loader = self.weight_loader

    def weight_loader(self, param, loaded_weight, loaded_shard_id: Optional[str] = None):
        loaded_weight = loaded_weight.transpose([1, 0])
        if not param._is_initialized():
            param.initialize()
        assert param.shape == loaded_weight.shape, (
            f" Attempted to load weight ({loaded_weight.shape}) " f"into parameter ({param.shape})"
        )
        # Ensure loaded weight dtype matches model param dtype
        if loaded_weight.dtype != param.dtype:
            if loaded_weight.dtype == paddle.int8 and param.dtype == paddle.float8_e4m3fn:
                loaded_weight = loaded_weight.view(param.dtype)
            else:
                loaded_weight = loaded_weight.cast(param.dtype)
        h2d_copy(param, loaded_weight)

    def forward(self, hidden_states: paddle.Tensor) -> paddle.Tensor:
        hidden_states = self.fc1(hidden_states)
        hidden_states = get_activation_fn(self.config.hidden_act)(hidden_states[0])
        hidden_states = self.fc2(hidden_states)
        return hidden_states


class SiglipEncoderLayer(paddle.nn.Layer):
    def __init__(self, config):
        super().__init__()
        self.embed_dim = config.hidden_size
        self.layer_norm1 = paddle.nn.LayerNorm(self.embed_dim, epsilon=config.layer_norm_eps)
        self.self_attn = SiglipAttention(config)
        self.layer_norm2 = paddle.nn.LayerNorm(self.embed_dim, epsilon=config.layer_norm_eps)
        self.mlp = SiglipMLP(config)

    def forward(
        self,
        hidden_states,
        attention_mask,
        output_attentions=False,
        cu_seqlens=None,
        max_seqlen=None,
        cos_emb=None,
        sin_emb=None,
    ):

        residual = hidden_states
        ############################
        ln1_out = self.layer_norm1(hidden_states)

        x = self.self_attn(
            hidden_states=ln1_out,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            cu_seqlens=cu_seqlens,
            max_seqlen=max_seqlen,
            cos_emb=cos_emb,
            sin_emb=sin_emb,
        )

        hs_post_attn = residual + x

        residual = hs_post_attn
        ln2_out = self.layer_norm2(residual)

        mlp_out = self.mlp(ln2_out)

        hidden_states_out = residual + mlp_out

        outputs = (hidden_states_out,)

        return outputs


class SigLIPRotaryEmbedding(nn.Layer):
    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):
        arange = paddle.arange(0, self.dim, 2, dtype="float32")
        inv_freq = 1.0 / (self.theta ** (arange / self.dim))
        self.register_buffer("inv_freq", inv_freq.astype(paddle.get_default_dtype()), persistable=False)

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


class SiglipEncoder(nn.Layer):
    def __init__(self, config):
        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.LayerList([SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)])
        self.rotary_pos_emb = SigLIPRotaryEmbedding(head_dim // 2)
        self.gradient_checkpointing = False

    @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 build_window_index(self, image_grid, window_size):
        """
        返回：
          window_indices: int64 [sum(t*h*w_valid)]
          cu_seqlens_within_windows: int32 [num_windows_total*t]，首位补 0 的前缀和
        """
        from einops import rearrange

        window_indices = list()
        pad_values = -100
        start_window_index = 0
        cu_seqlens_within_windows = list()

        for t, h, w in map(int, image_grid):
            window_index = paddle.arange(t * h * w).reshape((t, h, w))
            pad_h = (-h) % window_size
            pad_w = (-w) % window_size
            assert pad_h >= 0 and pad_w >= 0, (pad_h, pad_w)
            window_index = F.pad(window_index, (0, pad_w, 0, pad_h), value=pad_values)
            window_index = rearrange(
                window_index,
                "t (h p1) (w p2) -> t (h w) (p1 p2)",
                p1=window_size,
                p2=window_size,
            )
            window_seqlens = (window_index != pad_values).long().sum(-1).reshape(-1)
            window_index = window_index.reshape(-1)
            window_index = window_index[window_index != pad_values]
            window_indices.append(window_index + start_window_index)
            cu_seqlens_within_windows.append(window_seqlens.cumsum(0) + start_window_index)
            start_window_index += t * h * w
        window_indices = paddle.concat(window_indices, axis=0)
        cu_seqlens_within_windows = paddle.concat(cu_seqlens_within_windows, axis=0)
        cu_seqlens_within_windows = F.pad(cu_seqlens_within_windows, (1, 0), value=0).astype("int32")
        return window_indices, cu_seqlens_within_windows

    def forward(
        self,
        inputs_embeds: paddle.Tensor,
        attention_mask: Optional[paddle.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        cu_seqlens: Optional[paddle.Tensor] = None,
        image_grid_thw: Optional[List[Union[Tuple[int, int, int], List[Tuple[int, int, int]]]]] = None,
        height_position_ids: Optional[paddle.Tensor] = None,
        width_position_ids: Optional[paddle.Tensor] = None,
        use_rope: Optional[bool] = False,
        window_size: Optional[int] = -1,
        vision_or_text: str = "vision",
    ):
        assert vision_or_text in ["vision", "text"]
        use_window_attn = window_size > 0 and vision_or_text == "vision"
        use_rope = (use_rope is True) and (vision_or_text == "vision")
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

        encoder_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        hidden_states = inputs_embeds
        attention_mask = attention_mask.to(inputs_embeds.dtype) if attention_mask is not None else None

        if use_rope is True:
            flatten_image_grid_thw = self.flatten_list(image_grid_thw)
            flatten_image_grid_thw = np.array(flatten_image_grid_thw)
            assert sum([np.prod(x) for x in flatten_image_grid_thw]) == hidden_states.shape[1], (
                flatten_image_grid_thw,
                hidden_states.shape,
            )

            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:
                    t, h, w = map(int, (t, h, w))
                    image_pids = paddle.arange(t * h * w) % (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 = paddle.concat(split_wids, axis=0)
                height_position_ids = paddle.concat(split_hids, axis=0)

            window_indices, cu_seqlens_within_windows = None, None

            if use_window_attn:
                window_indices, cu_seqlens_within_windows = self.build_window_index(
                    flatten_image_grid_thw, window_size
                )
                reversed_window_indices = window_indices.argsort()
                height_position_ids = height_position_ids[window_indices]
                width_position_ids = width_position_ids[window_indices]

            pids = paddle.stack([height_position_ids, width_position_ids], axis=-1).astype(paddle.int64)
            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.tile((1, 2))
            cos_emb = rope_emb.cos().astype("float32")
            sin_emb = rope_emb.sin().astype("float32")
            cos_emb = cos_emb.unsqueeze(-2)
            sin_emb = sin_emb.unsqueeze(-2)
        else:
            cos_emb = None
            sin_emb = None

            window_indices, cu_seqlens_within_windows = None, None

            if use_window_attn:
                flatten_image_grid_thw = self.flatten_list(image_grid_thw)
                assert (
                    sum([np.prod(x.astype("float32").cpu().numpy()) for x in flatten_image_grid_thw])
                    == hidden_states.shape[1]
                ), (flatten_image_grid_thw, hidden_states.shape)

                window_indices, cu_seqlens_within_windows = self.build_window_index(
                    flatten_image_grid_thw, window_size
                )
                reversed_window_indices = window_indices.argsort()

        if use_window_attn:
            assert cu_seqlens_within_windows is not None
            attn_cu_seqlens = cu_seqlens_within_windows
            hidden_states = hidden_states[:, window_indices, :]
        else:
            attn_cu_seqlens = cu_seqlens

        return self._run_encoder_layer(
            encoder_states=encoder_states,
            all_attentions=all_attentions,
            attn_cu_seqlens=attn_cu_seqlens,
            output_hidden_states=output_hidden_states,
            reversed_window_indices=reversed_window_indices if output_hidden_states else None,
            use_window_attn=use_window_attn,
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            cos_emb=cos_emb,
            sin_emb=sin_emb,
        )

    # This function will be compiled with CINN when graph_opt_level >= 2
    # TODO(SigureMo): Use a new decorator to mark the function for CINN compilation
    def _run_encoder_layer(
        self,
        encoder_states: Optional[Tuple[()]],
        all_attentions: Optional[Tuple[()]],
        attn_cu_seqlens: Optional[paddle.Tensor],
        output_hidden_states: Optional[bool],
        reversed_window_indices: paddle.Tensor,
        use_window_attn: bool,
        hidden_states: paddle.Tensor,
        attention_mask: Optional[paddle.Tensor],
        output_attentions: bool,
        cos_emb: Optional[paddle.Tensor],
        sin_emb: Optional[paddle.Tensor],
    ) -> paddle.Tensor:
        max_seqlen = (attn_cu_seqlens[1:] - attn_cu_seqlens[:-1]).max().cpu()

        for encoder_layer in self.layers:
            if output_hidden_states:
                encoder_states = encoder_states + (
                    (hidden_states[:, reversed_window_indices, :],) if use_window_attn else (hidden_states,)
                )

            layer_outputs = encoder_layer(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                output_attentions=output_attentions,
                cu_seqlens=attn_cu_seqlens,
                max_seqlen=max_seqlen,
                cos_emb=cos_emb,
                sin_emb=sin_emb,
            )
            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        if use_window_attn:
            hidden_states = hidden_states[:, reversed_window_indices, :]
        if output_hidden_states:
            encoder_states = encoder_states + (hidden_states,)

        return hidden_states


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

    def __init__(self, config: PaddleOCRVisionConfig):
        super().__init__()

        self.probe = self.create_parameter(
            shape=(1, 1, config.hidden_size),
            default_initializer=paddle.nn.initializer.Normal(),
        )
        self.attention = nn.MultiHeadAttention(config.hidden_size, config.num_attention_heads)
        self.layernorm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
        self.mlp = SiglipMLP(config)

    def forward(self, hidden_state, key_padding_mask=None):
        batch_size = hidden_state.shape[0]
        probe = self.probe.tile((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 = residual + self.mlp(hidden_state)

        return hidden_state[:, 0]


class SiglipVisionTransformer(nn.Layer):
    def __init__(self, config: PaddleOCRVisionConfig):
        super().__init__()
        self.config = config
        embed_dim = config.hidden_size

        self.embeddings = SiglipVisionEmbeddings(config)
        self.encoder = SiglipEncoder(config)
        self.post_layernorm = nn.LayerNorm(embed_dim, epsilon=config.layer_norm_eps)
        self.use_head = True if not hasattr(config, "vision_use_head") else config.vision_use_head
        if self.use_head:
            self.head = SiglipMultiheadAttentionPoolingHead(config)

    def forward(
        self,
        pixel_values,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        interpolate_pos_encoding: Optional[bool] = False,
        attention_mask=None,
        sample_indices=None,
        image_indices=None,
        position_ids=None,
        height_position_ids=None,
        width_position_ids=None,
        cu_seqlens=None,
        padding_mask=None,
        vision_return_embed_list: Optional[bool] = False,
        image_grid_thw: Optional[List[Union[Tuple[int, int, int], List[Tuple[int, int, int]]]]] = None,
        return_pooler_output: Optional[bool] = True,
        use_rope: Optional[bool] = False,
        window_size: Optional[bool] = -1,
    ):
        hidden_states = self.embeddings(
            pixel_values,
            interpolate_pos_encoding=interpolate_pos_encoding,
            position_ids=position_ids,
            image_grid_thw=image_grid_thw,
        )
        last_hidden_state = self.encoder(
            inputs_embeds=hidden_states,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            attention_mask=attention_mask,
            cu_seqlens=cu_seqlens,
            image_grid_thw=image_grid_thw,
            use_rope=use_rope,
            height_position_ids=height_position_ids,
            width_position_ids=width_position_ids,
            window_size=window_size,
            vision_or_text="vision",
        )
        last_hidden_state = self.post_layernorm(last_hidden_state)

        sample_hidden_state = list()
        assert cu_seqlens is not None
        for i in range(cu_seqlens.shape[0] - 1):
            start = cu_seqlens[i]
            end = cu_seqlens[i + 1]
            tensor = last_hidden_state[:, start:end, :].squeeze(0)
            sample_hidden_state.append(tensor)

        return sample_hidden_state


class SiglipVisionModel(PretrainedModel):
    config_class = PaddleOCRVisionConfig
    main_input_name = "pixel_values"

    def __init__(self, config: PaddleOCRVisionConfig, prefix=""):
        super().__init__(config)
        self.prefix_name = prefix
        self.vision_model = SiglipVisionTransformer(config)

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

    def forward(
        self,
        pixel_values,
        sample_indices=None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        interpolate_pos_encoding: bool = False,
        position_ids=None,
        vision_return_embed_list: Optional[bool] = False,
        image_grid_thw: Optional[List[Union[Tuple[int, int, int], List[Tuple[int, int, int]]]]] = None,
        cu_seqlens=None,
        return_pooler_output: Optional[bool] = True,
        use_rope: Optional[bool] = False,
        window_size: Optional[bool] = -1,
    ):
        return self.vision_model(
            pixel_values=pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            interpolate_pos_encoding=interpolate_pos_encoding,
            position_ids=position_ids,
            vision_return_embed_list=vision_return_embed_list,
            image_grid_thw=image_grid_thw,
            sample_indices=sample_indices,
            cu_seqlens=cu_seqlens,
            return_pooler_output=return_pooler_output,
            use_rope=use_rope,
            window_size=window_size,
        )
