# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/19e6e80e10118f855137b90740936c0b11ac397f/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
# Copyright 2024 The Qwen team.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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 Qwen2-VL model compatible with HuggingFace weights."""
import logging
from functools import lru_cache, partial
from typing import Iterable, List, Optional, Tuple, Type, TypedDict

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from transformers import Qwen2VLConfig
from transformers.models.qwen2_vl.configuration_qwen2_vl import Qwen2VLVisionConfig

from sglang.srt.hf_transformers_utils import get_processor
from sglang.srt.layers.activation import QuickGELU
from sglang.srt.layers.attention.vision import VisionAttention
from sglang.srt.layers.linear import ColumnParallelLinear, RowParallelLinear
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.pooler import Pooler, PoolingType
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
from sglang.srt.managers.mm_utils import (
    MultiModalityDataPaddingPatternTokenPairs,
    general_mm_embed_routine,
)
from sglang.srt.managers.schedule_batch import MultimodalInputs
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.qwen2 import Qwen2Model
from sglang.srt.utils import add_prefix

logger = logging.getLogger(__name__)


# === Vision Inputs === #


class Qwen2VLImageInputs(TypedDict):
    pixel_values: torch.Tensor
    """Shape:
    `(num_patches, num_channels * patch_size * patch_size)`
    """

    image_grid_thw: torch.Tensor
    """Shape: `(num_images, 3)`

    This should be in `(grid_t, grid_h, grid_w)` format.
    """


class Qwen2VLVideoInputs(TypedDict):
    pixel_values_videos: torch.Tensor
    """Shape:
    `(num_patches,
      num_channels * temporal_patch_size * patch_size * patch_size)`
    """

    video_grid_thw: torch.Tensor
    """Shape: `(num_videos, 3)`

    This should be in `(grid_t, grid_h, grid_w)` format.
    """


# === Vision Encoder === #


class Qwen2VisionMLP(nn.Module):

    def __init__(
        self,
        in_features: int,
        hidden_features: int = None,
        act_layer: Type[nn.Module] = QuickGELU,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.fc1 = ColumnParallelLinear(
            in_features,
            hidden_features,
            quant_config=quant_config,
            prefix=add_prefix("fc1", prefix),
        )
        self.act = act_layer()
        self.fc2 = RowParallelLinear(
            hidden_features,
            in_features,
            quant_config=quant_config,
            prefix=add_prefix("fc2", prefix),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x_parallel, _ = self.fc1(x)
        x_parallel = self.act(x_parallel)
        x, _ = self.fc2(x_parallel)
        return x


class Qwen2VisionBlock(nn.Module):

    def __init__(
        self,
        dim: int,
        num_heads: int,
        mlp_ratio: float,
        act_layer: Type[nn.Module] = QuickGELU,
        norm_layer: Type[nn.Module] = None,
        attn_implementation: Optional[str] = "sdpa",
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        if norm_layer is None:
            norm_layer = partial(nn.LayerNorm, eps=1e-6)
        self.norm1 = norm_layer(dim)
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        if attn_implementation == "sdpa":
            use_context_forward = False
            softmax_in_single_precision = False
        elif attn_implementation == "flash_attention_2":
            softmax_in_single_precision = False
            use_context_forward = True
        elif attn_implementation == "eager":
            softmax_in_single_precision = True
            use_context_forward = False

        self.attn = VisionAttention(
            embed_dim=dim,
            num_heads=num_heads,
            projection_size=dim,
            use_qkv_parallel=False,
            use_context_forward=use_context_forward,
            softmax_in_single_precision=softmax_in_single_precision,
            flatten_batch=True,
            quant_config=quant_config,
            prefix=add_prefix("attn", prefix),
        )
        self.mlp = Qwen2VisionMLP(
            dim,
            mlp_hidden_dim,
            act_layer=act_layer,
            quant_config=quant_config,
            prefix=add_prefix("mlp", prefix),
        )

    def forward(
        self,
        x: torch.Tensor,
        cu_seqlens: torch.Tensor,
        position_embeddings: torch.Tensor,
    ) -> torch.Tensor:
        hidden_states = self.norm1(x)
        hidden_states = rearrange(hidden_states, "s b ... -> b s ...")
        attn = self.attn(
            hidden_states,
            cu_seqlens=cu_seqlens,
            position_embeddings=position_embeddings,
        )
        attn = rearrange(attn, "b s ... -> s b ...")
        x = x + attn
        x = x + self.mlp(self.norm2(x))
        return x


class Qwen2VisionPatchEmbed(nn.Module):

    def __init__(
        self,
        patch_size: int = 14,
        temporal_patch_size: int = 2,
        in_chans: int = 3,
        embed_dim: int = 1152,
    ) -> None:
        super().__init__()
        self.patch_size = patch_size
        self.temporal_patch_size = temporal_patch_size
        self.embed_dim = embed_dim

        kernel_size = [temporal_patch_size, patch_size, patch_size]
        self.proj = nn.Conv3d(
            in_chans, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        L, C = x.shape
        x = x.view(L, -1, self.temporal_patch_size, self.patch_size, self.patch_size)
        x = self.proj(x).view(L, self.embed_dim)
        return x


class Qwen2VisionPatchMerger(nn.Module):

    def __init__(
        self,
        d_model: int,
        context_dim: int,
        norm_layer: Type[nn.Module] = None,
        spatial_merge_size: int = 2,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = context_dim * (spatial_merge_size**2)
        if norm_layer is None:
            norm_layer = partial(nn.LayerNorm, eps=1e-6)
        self.ln_q = norm_layer(context_dim)
        self.mlp = nn.ModuleList(
            [
                ColumnParallelLinear(
                    self.hidden_size,
                    self.hidden_size,
                    bias=True,
                    quant_config=quant_config,
                    prefix=add_prefix("mlp.0", prefix),
                ),
                nn.GELU(),
                RowParallelLinear(
                    self.hidden_size,
                    d_model,
                    bias=True,
                    quant_config=quant_config,
                    prefix=add_prefix("mlp.2", prefix),
                ),
            ]
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.ln_q(x)
        x = x.view(-1, self.hidden_size)

        mlp_fc1, mlp_act, mlp_fc2 = self.mlp
        x_parallel, _ = mlp_fc1(x)
        x_parallel = mlp_act(x_parallel)
        out, _ = mlp_fc2(x_parallel)
        return out


class Qwen2VisionRotaryEmbedding(nn.Module):

    def __init__(self, dim: int, theta: float = 10000.0) -> None:
        super().__init__()
        self.dim = dim
        self.theta = theta
        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self._seq_len_cached = 0
        self._freqs_cached = None

    def update_freqs_cache(self, seqlen: int) -> None:
        if seqlen > self._seq_len_cached:
            seqlen *= 2
            self._seq_len_cached = seqlen
            self.inv_freq = 1.0 / (
                self.theta
                ** (
                    torch.arange(
                        0, self.dim, 2, dtype=torch.float, device=self.inv_freq.device
                    )
                    / self.dim
                )
            )
            seq = torch.arange(
                seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
            )
            freqs = torch.outer(seq, self.inv_freq)
            self._freqs_cached = freqs

    def forward(self, seqlen: int) -> torch.Tensor:
        self.update_freqs_cache(seqlen)
        return self._freqs_cached[:seqlen]


class Qwen2VisionTransformer(nn.Module):

    def __init__(
        self,
        vision_config: Qwen2VLVisionConfig,
        norm_eps: float = 1e-6,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        patch_size: int = vision_config.patch_size
        temporal_patch_size: int = vision_config.temporal_patch_size
        spatial_merge_size: int = vision_config.spatial_merge_size
        in_chans: int = vision_config.in_chans
        hidden_size: int = vision_config.hidden_size
        embed_dim: int = vision_config.embed_dim
        depth: int = vision_config.depth
        num_heads: int = vision_config.num_heads
        mlp_ratio: float = vision_config.mlp_ratio

        self.spatial_merge_size = spatial_merge_size

        self.patch_embed = Qwen2VisionPatchEmbed(
            patch_size=patch_size,
            temporal_patch_size=temporal_patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
        )

        norm_layer = partial(nn.LayerNorm, eps=norm_eps)
        head_dim = embed_dim // num_heads
        self.rotary_pos_emb = Qwen2VisionRotaryEmbedding(head_dim // 2)
        self.blocks = nn.ModuleList(
            [
                Qwen2VisionBlock(
                    dim=embed_dim,
                    num_heads=num_heads,
                    mlp_ratio=mlp_ratio,
                    norm_layer=norm_layer,
                    attn_implementation="sdpa",
                    quant_config=quant_config,
                    prefix=add_prefix(f"blocks.{i}", prefix),
                )
                for i in range(depth)
            ]
        )
        self.merger = Qwen2VisionPatchMerger(
            d_model=hidden_size,
            context_dim=embed_dim,
            norm_layer=norm_layer,
            quant_config=quant_config,
            prefix=add_prefix("merger", prefix),
        )

    @property
    def dtype(self) -> torch.dtype:
        return next(self.parameters()).dtype

    @property
    def device(self) -> torch.device:
        return self.blocks[0].mlp.fc2.weight.device

    def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
        pos_ids = []
        for i in range(grid_thw.size(0)):
            t, h, w = grid_thw[i].tolist()
            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
            hpos_ids = (
                hpos_ids.reshape(
                    h // self.spatial_merge_size,
                    self.spatial_merge_size,
                    w // self.spatial_merge_size,
                    self.spatial_merge_size,
                )
                .permute(0, 2, 1, 3)
                .flatten()
            )
            wpos_ids = (
                wpos_ids.reshape(
                    h // self.spatial_merge_size,
                    self.spatial_merge_size,
                    w // self.spatial_merge_size,
                    self.spatial_merge_size,
                )
                .permute(0, 2, 1, 3)
                .flatten()
            )
            pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
        pos_ids = torch.cat(pos_ids, dim=0)
        max_grid_size = grid_thw[:, 1:].max()
        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
        return rotary_pos_emb

    def forward(
        self,
        x: torch.Tensor,
        grid_thw: torch.Tensor,
    ) -> torch.Tensor:
        # patchify
        x = x.to(device=self.device, dtype=self.dtype)
        x = self.patch_embed(x)

        # compute position embedding
        rotary_pos_emb = self.rot_pos_emb(grid_thw)
        emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
        position_embeddings = (emb.cos(), emb.sin())
        # compute cu_seqlens
        cu_seqlens = torch.repeat_interleave(
            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
        ).cumsum(dim=0, dtype=torch.int32)
        cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)

        # transformers
        x = x.unsqueeze(1)
        for blk in self.blocks:
            x = blk(x, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings)

        # adapter
        x = self.merger(x)
        return x


cached_get_processor = lru_cache(get_processor)


class Qwen2VLForConditionalGeneration(nn.Module):
    def calculate_num_image_tokens(self, image_grid_thw: Tuple[int, int, int]):
        processor = cached_get_processor(self.config._name_or_path)
        grid_t, grid_h, grid_w = image_grid_thw
        num_image_tokens = (
            grid_t
            * grid_h
            * grid_w
            // processor.image_processor.merge_size
            // processor.image_processor.merge_size
        )
        return num_image_tokens

    def __init__(
        self,
        config: Qwen2VLConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config
        self.visual = Qwen2VisionTransformer(
            config.vision_config,
            norm_eps=getattr(config, "rms_norm_eps", 1e-6),
            # NOTE: Qwen2-VL vision encoder does not support any
            # quantization method now.
            quant_config=None,
            prefix=add_prefix("visual", prefix),
        )

        self.model = Qwen2Model(
            config, quant_config, prefix=add_prefix("model", prefix)
        )

        if config.tie_word_embeddings:
            self.lm_head = self.model.embed_tokens
        else:
            self.lm_head = ParallelLMHead(
                config.vocab_size,
                config.hidden_size,
                quant_config=quant_config,
                prefix=add_prefix("lm_head", prefix),
            )

        self.logits_processor = LogitsProcessor(config)
        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)

    # Use grid_t * grid_w * grid_h to pad tokens for each image
    # add replaced padding by unique image hash
    def pad_input_ids(self, input_ids: List[int], multi_modal_inputs: MultimodalInputs):
        # Get all special token IDs
        im_start_id: int = multi_modal_inputs.im_start_id
        im_end_id: int = multi_modal_inputs.im_end_id

        media_token_pairs = [(im_start_id, im_end_id)]
        pattern = MultiModalityDataPaddingPatternTokenPairs(media_token_pairs)
        return pattern.pad_input_tokens(input_ids, multi_modal_inputs)

    def get_image_feature(self, image_input: MultimodalInputs) -> torch.Tensor:
        pixel_values = image_input.pixel_values.type(self.visual.dtype)
        image_embeds = self.visual(pixel_values, grid_thw=image_input.image_grid_thws)
        return image_embeds

    def _process_video_input(self, video_input: Qwen2VLVideoInputs) -> torch.Tensor:
        pixel_values_videos = video_input["pixel_values_videos"].type(self.visual.dtype)
        video_embeds = self.visual(
            pixel_values_videos, grid_thw=video_input["video_grid_thw"]
        )
        return video_embeds

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        get_embedding: bool = False,
    ):
        """Run forward pass for Qwen2-VL.

        Args:
            input_ids: Flattened (concatenated) input_ids corresponding to a
                batch.
            positions: Flattened (concatenated) position ids corresponding to a
                batch.
                **NOTE**: If mrope is enabled (default setting for Qwen2-VL
                opensource models), the shape will be `(3, seq_len)`,
                otherwise it will be `(seq_len,).
                (Use input_metadata.mrope_positions to replace it)
        """
        if getattr(self.config, "rope_scaling", {}).get("type", None) == "mrope":
            positions = forward_batch.mrope_positions

        if not (
            forward_batch.forward_mode.is_decode()
            or not forward_batch.contains_image_inputs()
        ):
            if getattr(self.config, "rope_scaling", {}).get("type", None) == "mrope":
                assert positions.ndim == 2 and positions.size(0) == 3, (
                    "multimodal section rotary embedding requires "
                    f"(3, seq_len) positions, but got {positions.size()}"
                )

        inputs_embeds = general_mm_embed_routine(
            input_ids=input_ids,
            forward_batch=forward_batch,
            embed_tokens=self.get_input_embeddings(),
            mm_data_embedding_func=self.get_image_feature,
        )

        hidden_states = self.model(
            input_ids=None,
            positions=positions,
            forward_batch=forward_batch,
            input_embeds=inputs_embeds,
        )

        if not get_embedding:
            return self.logits_processor(
                input_ids, hidden_states, self.lm_head, forward_batch
            )
        else:
            return self.pooler(hidden_states, forward_batch)

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("gate_up_proj", "up_proj", 1),
            ("gate_up_proj", "gate_proj", 0),
        ]
        params_dict = dict(self.named_parameters(remove_duplicate=False))
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
            if self.config.tie_word_embeddings and "lm_head.weight" in name:
                continue

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

                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:

                if "visual" in name and "qkv.weight" in name:
                    visual_num_heads = self.config.vision_config.num_heads
                    visual_embed_dim = self.config.vision_config.embed_dim
                    head_size = visual_embed_dim // visual_num_heads
                    loaded_weight = loaded_weight.view(
                        3, visual_num_heads, head_size, visual_embed_dim
                    )
                    loaded_weight = loaded_weight.transpose(0, 1)
                    loaded_weight = loaded_weight.reshape(-1, visual_embed_dim)
                elif "visual" in name and "qkv.bias" in name:
                    visual_num_heads = self.config.vision_config.num_heads
                    visual_embed_dim = self.config.vision_config.embed_dim
                    head_size = visual_embed_dim // visual_num_heads
                    loaded_weight = loaded_weight.view(3, visual_num_heads, head_size)
                    loaded_weight = loaded_weight.transpose(0, 1)
                    loaded_weight = loaded_weight.reshape(-1)

                if "visual" in name:
                    # adapt to VisionAttention
                    name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")

                try:
                    # Skip loading extra bias for GPTQ models.
                    if name.endswith(".bias") and name not in params_dict:
                        continue
                    param = params_dict[name]
                except KeyError:
                    print(params_dict.keys())
                    raise

                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)


EntryClass = Qwen2VLForConditionalGeneration