"""
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.
"""

import json
import logging
import os
from enum import IntEnum, auto
from typing import List, Optional

from transformers import PretrainedConfig

from sglang.srt.hf_transformers_utils import get_config, get_context_length

logger = logging.getLogger(__name__)


class AttentionArch(IntEnum):
    MLA = auto()
    MHA = auto()


class ModelConfig:
    def __init__(
        self,
        path: str,
        trust_remote_code: bool = True,
        revision: Optional[str] = None,
        context_length: Optional[int] = None,
        model_override_args: Optional[dict] = None,
        is_embedding: Optional[bool] = None
    ) -> None:
        # Parse args
        self.model_override_args = json.loads(model_override_args)
        self.hf_config = get_config(
            path,
            trust_remote_code=trust_remote_code,
            revision=revision,
            model_override_args=self.model_override_args,
        )
        self.hf_text_config = get_hf_text_config(self.hf_config)

        # Check model type
        self.is_generation = is_generation_model(self.hf_config.architectures, is_embedding)
        self.is_multimodal = is_multimodal_model(self.hf_config.architectures)
        self.is_encoder_decoder = is_encoder_decoder_model(self.hf_config.architectures)

        # Derive context length
        derived_context_len = get_context_length(self.hf_text_config)
        allow_long_context = os.environ.get(
            "SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN", None
        )

        if context_length is not None:
            if context_length > derived_context_len:
                if allow_long_context:
                    logger.warning(
                        f"Warning: User-specified context_length ({context_length}) is greater than the derived context_length ({derived_context_len}). "
                        f"This may lead to incorrect model outputs or CUDA errors."
                    )
                    self.context_len = context_length
                else:
                    raise ValueError(
                        f"User-specified context_length ({context_length}) is greater than the derived context_length ({derived_context_len}). "
                        f"This may lead to incorrect model outputs or CUDA errors. Note that the derived context_length may differ from max_position_embeddings in the model's config. "
                        f"To allow overriding this maximum, set the env var SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1"
                    )
            else:
                self.context_len = context_length
        else:
            self.context_len = derived_context_len

        # Unify the config keys for hf_text_config
        self.head_dim = getattr(
            self.hf_text_config,
            "head_dim",
            self.hf_text_config.hidden_size // self.hf_text_config.num_attention_heads,
        )

        # FIXME: temporary special judge for MLA architecture
        if "DeepseekV2ForCausalLM" in self.hf_config.architectures:
            self.head_dim = 256
            self.attention_arch = AttentionArch.MLA
            self.kv_lora_rank = self.hf_config.kv_lora_rank
            self.qk_rope_head_dim = self.hf_config.qk_rope_head_dim
        elif "MiniCPM3ForCausalLM" in self.hf_config.architectures:
            self.head_dim = 128
            self.attention_arch = AttentionArch.MLA
            self.kv_lora_rank = self.hf_config.kv_lora_rank
            self.qk_rope_head_dim = self.hf_config.qk_rope_head_dim
        else:
            self.attention_arch = AttentionArch.MHA

        self.num_attention_heads = self.hf_text_config.num_attention_heads
        self.num_key_value_heads = getattr(
            self.hf_text_config, "num_key_value_heads", None
        )

        # for Dbrx and MPT models
        if self.hf_config.model_type in ["dbrx", "mpt"]:
            self.num_key_value_heads = getattr(
                self.hf_config.attn_config, "kv_n_heads", None
            )

        if self.num_key_value_heads is None:
            self.num_key_value_heads = self.num_attention_heads
        self.hidden_size = self.hf_text_config.hidden_size
        self.num_hidden_layers = self.hf_text_config.num_hidden_layers
        self.vocab_size = self.hf_text_config.vocab_size

    # adapted from https://github.com/vllm-project/vllm/blob/main/vllm/config.py#L289
    def get_total_num_kv_heads(self) -> int:
        """Returns the total number of KV heads."""
        # For GPTBigCode & Falcon:
        # NOTE: for falcon, when new_decoder_architecture is True, the
        # multi_query flag is ignored and we use n_head_kv for the number of
        # KV heads.
        falcon_model_types = ["falcon", "RefinedWeb", "RefinedWebModel"]
        new_decoder_arch_falcon = (
            self.hf_config.model_type in falcon_model_types
            and getattr(self.hf_config, "new_decoder_architecture", False)
        )
        if not new_decoder_arch_falcon and getattr(
            self.hf_text_config, "multi_query", False
        ):
            # Multi-query attention, only one KV head.
            # Currently, tensor parallelism is not supported in this case.
            return 1

        # For DBRX and MPT
        if self.hf_config.model_type in ["mpt"]:
            if "kv_n_heads" in self.hf_config.attn_config:
                return self.hf_config.attn_config["kv_n_heads"]
            return self.hf_config.num_attention_heads
        if self.hf_config.model_type in ["dbrx"]:
            return getattr(
                self.hf_config.attn_config,
                "kv_n_heads",
                self.hf_config.num_attention_heads,
            )

        attributes = [
            # For Falcon:
            "n_head_kv",
            "num_kv_heads",
            # For LLaMA-2:
            "num_key_value_heads",
            # For ChatGLM:
            "multi_query_group_num",
        ]
        for attr in attributes:
            num_kv_heads = getattr(self.hf_text_config, attr, None)
            if num_kv_heads is not None:
                return num_kv_heads

        # For non-grouped-query attention models, the number of KV heads is
        # equal to the number of attention heads.
        return self.hf_text_config.num_attention_heads

    def get_num_kv_heads(self, tensor_parallel_size) -> int:
        """Returns the number of KV heads per GPU."""
        total_num_kv_heads = self.get_total_num_kv_heads()
        # If tensor parallelism is used, we divide the number of KV heads by
        # the tensor parallel size. We will replicate the KV heads in the
        # case where the number of KV heads is smaller than the tensor
        # parallel size so each GPU has at least one KV head.
        return max(1, total_num_kv_heads // tensor_parallel_size)


def get_hf_text_config(config: PretrainedConfig):
    """Get the "sub" config relevant to llm for multi modal models.
    No op for pure text models.
    """
    class_name = config.architectures[0]
    if class_name.startswith("Llava") and class_name.endswith("ForCausalLM"):
        # We support non-hf version of llava models, so we do not want to
        # read the wrong values from the unused default text_config.
        return config

    if hasattr(config, "text_config"):
        # The code operates under the assumption that text_config should have
        # `num_attention_heads` (among others). Assert here to fail early
        # if transformers config doesn't align with this assumption.
        assert hasattr(config.text_config, "num_attention_heads")
        return config.text_config
    else:
        return config


def is_generation_model(model_architectures: List[str], is_embedding: bool = False):
    # We have two ways to determine whether a model is a generative model.
    # 1. Check the model architectue
    # 2. check the `is_embedding` server args

    if (
        "LlamaEmbeddingModel" in model_architectures
        or "MistralModel" in model_architectures
        or "LlamaForSequenceClassification" in model_architectures
        or "LlamaForSequenceClassificationWithNormal_Weights" in model_architectures
    ):
        return False
    else:
        return not is_embedding


def is_multimodal_model(model_architectures: List[str]):
    if (
        "LlavaLlamaForCausalLM" in model_architectures
        or "LlavaQwenForCausalLM" in model_architectures
        or "LlavaMistralForCausalLM" in model_architectures
        or "LlavaVidForCausalLM" in model_architectures
        or "MllamaForConditionalGeneration" in model_architectures
        or "Qwen2VLForConditionalGeneration" in model_architectures
    ):
        return True
    else:
        return False


def is_encoder_decoder_model(model_architectures: List[str]):
    return "MllamaForConditionalGeneration" in model_architectures
