# 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 dataclasses
from typing import List

import torch


@dataclasses.dataclass
class LoRAAdaptor:
    name: str
    prefill_tolerance: float = None
    decode_tolerance: float = None
    rouge_l_tolerance: float = None


@dataclasses.dataclass
class LoRAModelCase:
    base: str
    adaptors: List[LoRAAdaptor]
    tp_size: int = 1
    prefill_tolerance: float = 1e-1
    decode_tolerance: float = 1e-1
    rouge_l_tolerance: float = 1.0
    max_loras_per_batch: int = 1
    skip_long_prompt: bool = False

    def __post_init__(self):
        if len(self.adaptors) > self.max_loras_per_batch:
            raise ValueError(
                f"For base '{self.base}', number of adaptors ({len(self.adaptors)}) "
                f"must be <= max_loras_per_batch ({self.max_loras_per_batch})"
            )


TORCH_DTYPES = [torch.float16]
BACKENDS = ["triton"]