# Copyright 2025 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import copy
from typing import Iterable, Optional, Set, Tuple

import einops
import torch
import torch.nn.functional as F
from torch import nn
from transformers import (
    ROPE_INIT_FUNCTIONS,
    AutoModel,
    Gemma3TextConfig,
    PretrainedConfig,
    PreTrainedModel,
)

from sglang.srt.distributed import get_tensor_model_parallel_world_size
from sglang.srt.layers.activation import GeluAndMul
from sglang.srt.layers.layernorm import Gemma3RMSNorm
from sglang.srt.layers.linear import (
    MergedColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.layers.rotary_embedding import apply_rotary_pos_emb
from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.model_loader.weight_utils import (
    default_weight_loader,
    maybe_remap_kv_scale_name,
)
from sglang.srt.utils import add_prefix, make_layers


# Adapted from:
# https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/gemma3.py
def extract_layer_index(prefix: str) -> int:
    """Extract the layer index from a prefix string."""
    parts = prefix.split(".")
    for part in parts:
        if part.startswith("layers."):
            layer_str = part.split(".")[-1]
            try:
                return int(layer_str)
            except ValueError:
                continue
    return -1


class Gemma3MLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_activation: str,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size,
            [intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=add_prefix("gate_up_proj", prefix),
        )
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=add_prefix("down_proj", prefix),
        )
        if hidden_activation != "gelu_pytorch_tanh":
            raise ValueError(
                "Gemma3 uses `gelu_pytorch_tanh` as the hidden activation "
                "function. Please set `hidden_activation` to "
                "`gelu_pytorch_tanh`."
            )
        self.act_fn = GeluAndMul()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class Gemma3Attention(nn.Module):
    def __init__(
        self,
        layer_id: int,
        config: Gemma3TextConfig,
        max_position_embeddings: int,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.layer_id = layer_id
        self.config = config
        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_key_value_heads

        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)

        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0

        hidden_size = config.hidden_size

        head_dim = getattr(
            config, "head_dim", hidden_size // config.num_attention_heads
        )
        self.head_dim = head_dim

        self.q_size = self.num_heads * self.head_dim

        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = config.query_pre_attn_scalar**-0.5

        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=config.attention_bias,
            quant_config=quant_config,
            prefix=add_prefix("qkv_proj", prefix),
        )
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=config.attention_bias,
            quant_config=quant_config,
            prefix=add_prefix("o_proj", prefix),
        )

        # Determine if layer uses sliding window based on pattern
        self.is_sliding = bool((layer_id + 1) % config.sliding_window_pattern)

        # Initialize the rotary embedding.
        if self.is_sliding:
            # Local attention. Override the values in config.json.
            self.rope_theta = config.rope_local_base_freq
            self.rope_scaling = {"rope_type": "default"}
            # FIXME(mick): idk why vllm does this
            # self.sliding_window = config.interleaved_sliding_window
            self.sliding_window = config.sliding_window
        else:
            # Global attention. Use the values in config.json.
            self.rope_theta = config.rope_theta
            self.rope_scaling = config.rope_scaling
            self.sliding_window = None

        self.attn = RadixAttention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            layer_id=layer_id,
            logit_cap=getattr(self.config, "attn_logit_softcapping", None),
            sliding_window_size=self.sliding_window,
            prefix=add_prefix("attn", prefix),
        )

        # Gemma3 adds normalization for q and k
        self.q_norm = Gemma3RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)
        self.k_norm = Gemma3RMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)

    def naive_attn_with_masks(
        self,
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        out: torch.Tensor,
        **kwargs,
    ) -> torch.Tensor:
        q = q.view(-1, self.num_heads, self.head_dim)
        # Expand the key and value to handle GQA.
        num_queries_per_kv = self.num_heads // self.num_kv_heads
        k = k.view(-1, self.num_kv_heads, self.head_dim)
        k = k.repeat_interleave(num_queries_per_kv, dim=-2)
        v = v.view(-1, self.num_kv_heads, self.head_dim)
        v = v.repeat_interleave(num_queries_per_kv, dim=-2)

        if self.is_sliding:
            attn_masks = kwargs["local_attn_masks"]
        else:
            attn_masks = kwargs["global_attn_masks"]

        seq_lens = kwargs["seq_lens"]
        start_idx = 0
        for seq_len, attn_mask in zip(seq_lens, attn_masks):
            end_idx = start_idx + seq_len
            query = q[start_idx:end_idx].unsqueeze(0)
            key = k[start_idx:end_idx].unsqueeze(0)
            value = v[start_idx:end_idx].unsqueeze(0)

            # Transpose.
            query = query.transpose(1, 2)
            key = key.transpose(1, 2)
            value = value.transpose(1, 2)

            output = F.scaled_dot_product_attention(
                query,
                key,
                value,
                attn_mask,
                self.scaling,
            )
            output = output.transpose(1, 2).flatten(-2, -1)
            out[start_idx:end_idx] = output
            start_idx = end_idx
        return out

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
        forward_batch: ForwardBatch,
        **kwargs,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        # [s, h * head_dim]
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)

        # [s, h, head_dim]
        q = q.unflatten(-1, (self.num_heads, self.head_dim))
        # -> [h, s, head_dim]
        q = q.transpose(0, 1).unsqueeze(0)
        q = self.q_norm(q)
        k = k.unflatten(-1, (self.num_kv_heads, self.head_dim))
        # -> [h, s, head_dim]
        k = k.transpose(0, 1).unsqueeze(0)
        k = self.k_norm(k)

        # q, k = self.rotary_emb(positions, q, k)
        cos, sin = position_embeddings
        q, k = apply_rotary_pos_emb(q, k, cos, sin)

        # [b, h, s, head_dim] ->  [b, s, h, head_dim]
        q = q.permute(0, 2, 1, 3)
        k = k.permute(0, 2, 1, 3)

        attn_output = self.attn(q, k, v, forward_batch=forward_batch)
        output, _ = self.o_proj(attn_output)
        return output


class Gemma3DecoderLayer(nn.Module):
    def __init__(
        self,
        layer_id: int,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = Gemma3Attention(
            layer_id=layer_id,
            config=config,
            max_position_embeddings=config.max_position_embeddings,
            quant_config=quant_config,
            prefix=add_prefix("self_attn", prefix),
        )
        self.hidden_size = config.hidden_size
        self.mlp = Gemma3MLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_activation=config.hidden_activation,
            quant_config=quant_config,
            prefix=add_prefix("mlp", prefix),
        )
        self.input_layernorm = Gemma3RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )
        self.post_attention_layernorm = Gemma3RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )
        self.pre_feedforward_layernorm = Gemma3RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )
        self.post_feedforward_layernorm = Gemma3RMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )
        self.is_sliding = self.self_attn.is_sliding
        self.layer_id = layer_id

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        position_embeddings_global: torch.Tensor,
        position_embeddings_local: torch.Tensor,
        forward_batch: ForwardBatch,
        **kwargs,
    ) -> tuple[
        torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]
    ]:
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)

        # apply global RoPE to non-sliding layer only
        if self.self_attn.is_sliding:
            position_embeddings = position_embeddings_local
        else:
            position_embeddings = position_embeddings_global

        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
            position_embeddings=position_embeddings,
            forward_batch=forward_batch,
            **kwargs,
        )
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.pre_feedforward_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = self.post_feedforward_layernorm(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        return outputs


class Gemma3RotaryEmbedding(nn.Module):
    def __init__(self, config: Gemma3TextConfig, device=None):
        super().__init__()
        # BC: "rope_type" was originally "type"
        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
            self.rope_type = config.rope_scaling.get(
                "rope_type", config.rope_scaling.get("type")
            )
        else:
            self.rope_type = "default"
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config
        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]

        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.original_inv_freq = self.inv_freq

    def _dynamic_frequency_update(self, position_ids, device):
        """
        dynamic RoPE layers should recompute `inv_freq` in the following situations:
        1 - growing beyond the cached sequence length (allow scaling)
        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
        """
        seq_len = torch.max(position_ids) + 1
        if seq_len > self.max_seq_len_cached:  # growth
            inv_freq, self.attention_scaling = self.rope_init_fn(
                self.config, device, seq_len=seq_len
            )
            self.register_buffer(
                "inv_freq", inv_freq, persistent=False
            )  # TODO joao: may break with compilation
            self.max_seq_len_cached = seq_len

        if (
            seq_len < self.original_max_seq_len
            and self.max_seq_len_cached > self.original_max_seq_len
        ):  # reset
            # This .to() is needed if the model has been moved to a device after being initialized (because
            # the buffer is automatically moved, but not the original copy)
            self.original_inv_freq = self.original_inv_freq.to(device)
            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
            self.max_seq_len_cached = self.original_max_seq_len

    @torch.no_grad()
    def forward(self, x, position_ids):
        if "dynamic" in self.rope_type:
            self._dynamic_frequency_update(position_ids, device=x.device)

        # Core RoPE block
        inv_freq_expanded = (
            self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
        )
        position_ids_expanded = position_ids[:, None, :].float()
        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
        device_type = x.device.type
        device_type = (
            device_type
            if isinstance(device_type, str) and device_type != "mps"
            else "cpu"
        )
        with torch.autocast(device_type=device_type, enabled=False):
            freqs = (
                inv_freq_expanded.float().to(x.device) @ position_ids_expanded.float()
            ).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos()
            sin = emb.sin()

        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
        cos = cos * self.attention_scaling
        sin = sin * self.attention_scaling

        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


class Gemma3TextScaledWordEmbedding(nn.Embedding):
    """
    This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
    """

    def __init__(
        self,
        num_embeddings: int,
        embedding_dim: int,
        padding_idx: int,
        embed_scale: Optional[float] = 1.0,
    ):
        super().__init__(num_embeddings, embedding_dim, padding_idx)
        self.embed_scale = embed_scale

    def forward(self, input_ids: torch.Tensor):
        return super().forward(input_ids) * self.embed_scale


class Gemma3TextModel(PreTrainedModel):
    def __init__(
        self,
        config: Gemma3TextConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__(config=config)
        self.config = config
        self.quant_config = quant_config

        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        # Gemma3 downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5. See https://github.com/huggingface/transformers/pull/29402
        self.embed_tokens = Gemma3TextScaledWordEmbedding(
            config.vocab_size,
            config.hidden_size,
            self.padding_idx,
            embed_scale=self.config.hidden_size**0.5,
        )

        self.norm = Gemma3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = Gemma3RotaryEmbedding(config=config)
        self.gradient_checkpointing = False

        # when we want to create a local RoPE layer. Config defaults should hold values for global RoPE
        config = copy.deepcopy(config)
        config.rope_theta = config.rope_local_base_freq
        config.rope_scaling = {"rope_type": "default"}
        self.rotary_emb_local = Gemma3RotaryEmbedding(config=config)

        self.layers = make_layers(
            config.num_hidden_layers,
            lambda idx, prefix: Gemma3DecoderLayer(
                layer_id=idx,
                config=config,
                quant_config=quant_config,
                prefix=prefix,
            ),
            prefix=add_prefix("layers", prefix),
        )
        self.norm = Gemma3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_init()

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        input_embeds: torch.Tensor = None,
        **kwargs,
    ) -> torch.Tensor:
        if input_embeds is None:
            hidden_states = self.embed_tokens(input_ids)
        else:
            hidden_states = input_embeds

        if positions.dim() == 1:
            positions = einops.rearrange(positions, "s -> 1 s")

        position_embeddings_global = self.rotary_emb(hidden_states, positions)
        position_embeddings_local = self.rotary_emb_local(hidden_states, positions)
        for layer in self.layers:
            layer_outputs = layer(
                positions=positions,
                position_embeddings_global=position_embeddings_global,
                position_embeddings_local=position_embeddings_local,
                hidden_states=hidden_states,
                forward_batch=forward_batch,
                **kwargs,
            )
            hidden_states = layer_outputs[0]

        hidden_states = self.norm(hidden_states)

        return hidden_states


class Gemma3ForCausalLM(PreTrainedModel):
    config_class = Gemma3TextConfig

    _tied_weights_keys = ["lm_head.weight"]
    _tp_plan = {"lm_head": "colwise_rep"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
    config_class = Gemma3TextConfig
    base_model_prefix = "language_model"

    # BitandBytes specific attributes
    default_bitsandbytes_target_modules = [
        ".gate_proj.",
        ".down_proj.",
        ".up_proj.",
        ".q_proj.",
        ".k_proj.",
        ".v_proj.",
        ".o_proj.",
    ]
    bitsandbytes_stacked_params_mapping = {
        # shard_name, weight_name, index
        "q_proj": ("qkv_proj", 0),
        "k_proj": ("qkv_proj", 1),
        "v_proj": ("qkv_proj", 2),
        "gate_proj": ("gate_up_proj", 0),
        "up_proj": ("gate_up_proj", 1),
    }

    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

    # LoRA specific attributes
    supported_lora_modules = [
        "qkv_proj",
        "o_proj",
        "gate_up_proj",
        "down_proj",
    ]
    # Gemma does not apply LoRA to the embedding layer.
    embedding_modules = {}
    embedding_padding_modules = []
    supports_lora = True

    def __init__(
        self,
        config: Gemma3TextConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__(config=config)
        self.config = config
        self.quant_config = quant_config
        self.model = Gemma3TextModel(
            config, quant_config, prefix=add_prefix("model", prefix)
        )
        self.logits_processor = LogitsProcessor(config)

        if self.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.post_init()

    def get_input_embeddings(self) -> nn.Embedding:
        return self.model.embed_tokens

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

    @torch.no_grad()
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        input_embeds: torch.Tensor = None,
        **kwargs,
    ) -> LogitsProcessor:

        hidden_states = self.model(
            input_ids, positions, forward_batch, input_embeds, **kwargs
        )

        return self.logits_processor(
            input_ids, hidden_states, self.model.embed_tokens, 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", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]
        params_dict = dict(self.named_parameters())
        loaded_params: Set[str] = set()
        for name, loaded_weight in weights:
            for param_name, shard_name, shard_id in stacked_params_mapping:
                # if param_name in name:
                # print(f"{param_name} is already in {name}")
                if shard_name not in name:
                    continue
                name = name.replace(shard_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:
                # lm_head is not used in vllm as it is tied with embed_token.
                # To prevent errors, skip loading lm_head.weight.
                if "lm_head.weight" in name:
                    continue
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                # Remapping the name of FP8 kv-scale.
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    continue

                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        # unloaded_params = params_dict.keys() - loaded_params
        # if unloaded_params:
        #     logger.warning(
        #         "Some weights are not initialized from checkpoints: %s", unloaded_params
        #     )
        return loaded_params


EntryClass = Gemma3ForCausalLM
AutoModel.register(Gemma3TextConfig, Gemma3ForCausalLM, exist_ok=True)
