# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 pytest

from tensorrt_llm._torch.auto_deploy import LLM as AutoDeployLLM
from tensorrt_llm.sampling_params import SamplingParams

from ..conftest import llm_models_root
from .accuracy_core import MMLU, CnnDailymail, LlmapiAccuracyTestHarness


class TestLlama3_1_8B(LlmapiAccuracyTestHarness):
    MODEL_NAME = "meta-llama/Llama-3.1-8B"
    MODEL_PATH = f"{llm_models_root()}/llama-3.1-model/Meta-Llama-3.1-8B"

    def get_default_kwargs(self):
        return {
            'skip_tokenizer_init': False,
            'trust_remote_code': True,
            # TODO(https://github.com/NVIDIA/TensorRT-LLM/issues/7142):
            # AutoDeploy does not support cache reuse yet.
            'kv_cache_config': {
                'enable_block_reuse': False,
            },
            'max_batch_size': 512,
            # 131072 is the max seq len for the model
            'max_seq_len': 8192,
            # max num tokens is derived in the build_config, which is not used by AutoDeploy llmargs.
            # Set it explicitly here to 8192 which is the default in build_config.
            'max_num_tokens': 8192,
            'skip_loading_weights': False,
            'compile_backend': 'torch-opt',
            'free_mem_ratio': 0.7,
            'cuda_graph_batch_sizes': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]
        }

    def get_default_sampling_params(self):
        eos_id = -1
        beam_width = 1
        return SamplingParams(end_id=eos_id,
                              pad_id=eos_id,
                              n=beam_width,
                              use_beam_search=beam_width > 1)

    @pytest.mark.skip_less_device_memory(32000)
    def test_auto_dtype(self):
        kwargs = self.get_default_kwargs()
        sampling_params = self.get_default_sampling_params()
        with AutoDeployLLM(model=self.MODEL_PATH,
                           tokenizer=self.MODEL_PATH,
                           **kwargs) as llm:
            task = CnnDailymail(self.MODEL_NAME)
            task.evaluate(llm)
            task = MMLU(self.MODEL_NAME)
            task.evaluate(llm, sampling_params=sampling_params)