# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2025 The vLLM team.
# Copyright 2025 Google Inc. HuggingFace Inc. team. 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.
"""Gemma 4 model implementation for vLLM."""

from collections.abc import Iterable
from dataclasses import replace
from itertools import islice

import regex as re
import torch
from torch import nn

from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import (
    get_pp_group,
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
)
from vllm.forward_context import get_forward_context
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import GeluAndMul
from vllm.model_executor.layers.attention import Attention
from vllm.model_executor.layers.fused_moe import FusedMoE, GateLinear
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (
    ColumnParallelLinear,
    MergedColumnParallelLinear,
    QKVParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import (
    default_weight_loader,
    maybe_remap_kv_scale_name,
)
from vllm.sequence import IntermediateTensors
from vllm.v1.attention.backends.utils import KVSharingFastPrefillMetadata

from .interfaces import (
    EagleModelMixin,
    MixtureOfExperts,
    SupportsEagle3,
    SupportsLoRA,
    SupportsPP,
)
from .utils import (
    AutoWeightsLoader,
    WeightsMapper,
    extract_layer_index,
    is_pp_missing_parameter,
    make_layers,
    maybe_prefix,
)

logger = init_logger(__name__)


def _get_text_config(config):
    """Dereference text_config if config is a nested Gemma4Config.

    Gemma4 checkpoints use architectures=["Gemma4ForConditionalGeneration"]
    which yields a Gemma4Config with nested text_config. This function
    transparently returns the text config regardless of nesting.
    """
    if hasattr(config, "text_config"):
        return config.text_config
