"""
Copyright 2023-2024 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.
"""

from typing import Iterable, Optional, Tuple

import torch
from torch import nn
from transformers import LlamaConfig
from vllm.config import CacheConfig
from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader

from sglang.srt.layers.logits_processor import LogitsProcessorOutput
from sglang.srt.model_executor.forward_batch_info import InputMetadata
from sglang.srt.models.llama import LlamaForCausalLM, LlamaModel


class LlamaForClassification(nn.Module):
    def __init__(
        self,
        config: LlamaConfig,
        quant_config: Optional[QuantizationConfig] = None,
        cache_config: Optional[CacheConfig] = None,
    ) -> None:
        super().__init__()
        self.config = config
        self.quant_config = quant_config
        self.model = LlamaModel(config, quant_config=quant_config)

        self.classification_head = nn.Linear(
            config.hidden_size, config.classification_out_size, bias=False
        )
        self.eos_token_id = config.eos_token_id

        self.param_dict = dict(self.named_parameters())

    @torch.no_grad()
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        input_metadata: InputMetadata,
        input_embeds: torch.Tensor = None,
    ) -> torch.Tensor:
        hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
        is_eos_token = input_ids == self.eos_token_id
        hidden_states = hidden_states[is_eos_token]
        scores = self.classification_head(hidden_states)

        if scores.shape[0] != input_metadata.batch_size:
            print("Warning: the EOS tokens are missing in some sentences.")
            scores = torch.ones(
                (input_metadata.batch_size, self.config.classification_out_size)
            ).to(input_ids.device)

        logits_output = LogitsProcessorOutput(
            next_token_logits=scores,
            next_token_logprobs=scores,
            normalized_prompt_logprobs=scores,
            input_token_logprobs=torch.ones_like(input_ids),
            input_top_logprobs=None,
            output_top_logprobs=None,
        )

        return logits_output

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        params_dict = self.param_dict

        for name, loaded_weight in weights:
            if "classification_head" in name:
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)
            elif "lm_head" in name:
                continue
            else:
                LlamaForCausalLM.load_weights(self, [(name, loaded_weight)])


EntryClass = LlamaForClassification
