# SPDX-FileCopyrightText: Copyright (c) 2022-2024 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 time
from pathlib import Path

import pytest
from defs.common import (generate_summary_cmd, test_multi_lora_support,
                         venv_check_call)
from defs.conftest import (get_device_memory, get_gpu_device_list,
                           skip_fp8_pre_ada, skip_post_blackwell,
                           skip_pre_hopper)
from defs.trt_test_alternative import check_call


def get_vocab_file(model_path):
    "get vocab file"
    if "keras" in model_path or "ax" in model_path:
        if "2b" in model_path:
            vocab_file = f"{model_path}/../gemma-2b-it-flax/tokenizer.model"
        elif "7b" in model_path:
            vocab_file = f"{model_path}/../gemma-7b-it-flax/tokenizer.model"
    else:
        vocab_file = f"{model_path}/tokenizer.model"

    return vocab_file


def get_ckpt_dir(model_path):
    "get ckpt dir"
    if "ax" in model_path:
        if "2b" in model_path:
            ckpt_dir = f"{model_path}/2b-it"
        elif "7b" in model_path:
            ckpt_dir = f"{model_path}/7b-it"
    else:
        ckpt_dir = model_path

    return ckpt_dir


def get_ckpt_type(model_path):
    "get ckpt type"
    if "torch" in model_path:
        ckpt_type = "torch"
    elif "keras" in model_path:
        ckpt_type = "keras"
    elif "ax" in model_path:
        ckpt_type = "jax"
    else:
        ckpt_type = "hf"

    return ckpt_type


GEMMA_2_9B_IT = "gemma-2-9b-it"
GEMMA_2_27B_IT = "gemma-2-27b-it"
GEMMA_3_1B_IT = "gemma-3-1b-it"
VSWA_ATTENTION = {
    GEMMA_2_9B_IT: [4096, 8192],
    GEMMA_2_27B_IT: [4096, 8192],
    GEMMA_3_1B_IT: [512, 512, 512, 512, 512, 32768]
}
"""
* Gemma-2: (local `4096`: https://huggingface.co/google/gemma-2-9b-it/blob/main/config.json#L27, global `8192`: https://huggingface.co/google/gemma-2-9b-it/blob/main/config.json#L18)
* Gemma-3-1b: (local `512`: https://huggingface.co/google/gemma-3-1b-it/blob/main/config.json#L31, global `32768`: https://huggingface.co/google/gemma-3-1b-it/blob/9b99be8/config.json#L20)
* (global `131072`: All other gemma 3 models https://github.com/huggingface/transformers/blob/ae5ce226644c8576c9047987e6b1d2e9bdeaed24/src/transformers/models/gemma3/modular_gemma3.py#L200C33-L200C40)
"""
VSWA_MODELS = VSWA_ATTENTION.keys()

GEMMA2_MODELS = {GEMMA_2_9B_IT, GEMMA_2_27B_IT}


@skip_pre_hopper
@skip_post_blackwell
@pytest.mark.parametrize("batch_size", [8])
@pytest.mark.parametrize("data_type", ['bfloat16'])
@pytest.mark.parametrize("qformat", ['fp8'])
@pytest.mark.parametrize("gemma_model_root", VSWA_MODELS, indirect=True)
def test_llm_hf_gemma_quantization_1gpu_vswa(batch_size, data_type,
                                             gemma_model_root, llm_venv,
                                             cmodel_dir, engine_dir,
                                             gemma_example_root,
                                             llm_datasets_root, llm_rouge_root,
                                             qformat):
    skip_fp8_pre_ada(use_fp8=qformat == "fp8")
    max_attention_window = VSWA_ATTENTION[Path(gemma_model_root).stem]
    hf_gemma_quantization_1gpu(batch_size, data_type, gemma_model_root,
                               llm_venv, cmodel_dir, engine_dir,
                               gemma_example_root, llm_datasets_root,
                               llm_rouge_root, qformat, max_attention_window)


@skip_post_blackwell
@skip_pre_hopper
@pytest.mark.parametrize("batch_size", [8])
@pytest.mark.parametrize("data_type", ['bfloat16', 'float16'])
@pytest.mark.parametrize("qformat", ['fp8', 'int4_awq', 'int8_sq'])
@pytest.mark.parametrize("gemma_model_root",
                         ["gemma-2b", "gemma-7b", *GEMMA2_MODELS],
                         indirect=True)
def test_llm_hf_gemma_quantization_1gpu(batch_size, data_type, gemma_model_root,
                                        llm_venv, cmodel_dir, engine_dir,
                                        gemma_example_root, llm_datasets_root,
                                        llm_rouge_root, qformat):
    hf_gemma_quantization_1gpu(batch_size, data_type, gemma_model_root,
                               llm_venv, cmodel_dir, engine_dir,
                               gemma_example_root, llm_datasets_root,
                               llm_rouge_root, qformat)


def hf_gemma_quantization_1gpu(batch_size,
                               data_type,
                               gemma_model_root,
                               llm_venv,
                               cmodel_dir,
                               engine_dir,
                               gemma_example_root,
                               llm_datasets_root,
                               llm_rouge_root,
                               qformat,
                               max_attention_window: list[int] | None = None):
    "run gemma quantization tests"
    print("Convert checkpoint by modelopt...")
    kv_cache_dtype = 'fp8' if qformat == 'fp8' else 'int8'
    convert_cmd = [
        f"{gemma_example_root}/../../../quantization/quantize.py",
        f"--model_dir={gemma_model_root}",
        f"--calib_dataset={llm_datasets_root}/cnn_dailymail",
        f"--dtype={data_type}",
        f"--qformat={qformat}",
        f"--kv_cache_dtype={kv_cache_dtype}",
        f"--output_dir={cmodel_dir}",
        "--device_map=sequential",
    ]
    venv_check_call(llm_venv, convert_cmd)

    print("Build engines...")
    build_cmd = [
        "trtllm-build",
        f"--checkpoint_dir={cmodel_dir}",
        f"--output_dir={engine_dir}",
        f"--gpt_attention_plugin={data_type}",
        f"--gemm_plugin={data_type}",
        "--max_beam_width=1",
        "--max_input_len=3000",
        "--max_seq_len=3100",
        f"--max_batch_size={batch_size}",
    ]

    check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)

    print("Run summarize...")
    # Currently, gemma-7b has poor performance on FP8.
    # We should use mmlu to verify in future.
    threshold_score = 19.5
    if "gemma-7b" in gemma_model_root:
        threshold_score = 18

    window = {
        'max_attention_window_size': max_attention_window
    } if max_attention_window is not None else {}
    summary_cmd = generate_summary_cmd(
        gemma_example_root,
        engine_dir=engine_dir,
        max_ite=40,
        batch_size=batch_size,
        tensorrt_llm_rouge1_threshold=threshold_score,
        dataset_dir=llm_datasets_root,
        rouge_dir=llm_rouge_root,
        **window)

    ckpt_type = get_ckpt_type(gemma_model_root)
    vocab_file = get_vocab_file(gemma_model_root)
    if ckpt_type == "hf":
        summary_cmd.extend([
            f"--hf_model_dir={gemma_model_root}",
            f"--tokenizer_dir={gemma_model_root}"
        ])
    else:
        summary_cmd.append(f"--vocab_file={vocab_file}")
    venv_check_call(llm_venv, summary_cmd)


# max_seq_len=3100, one local value that won't slide, and one that will
@skip_post_blackwell
@pytest.mark.parametrize("batch_size", [8])
@pytest.mark.parametrize("data_type", ['bfloat16'])
@pytest.mark.parametrize("test_case", ['other'])
@pytest.mark.parametrize("gemma_model_root", VSWA_MODELS, indirect=True)
def test_llm_gemma_1gpu_summary_vswa(batch_size, data_type, gemma_model_root,
                                     llm_venv, cmodel_dir, engine_dir,
                                     gemma_example_root, llm_datasets_root,
                                     llm_rouge_root, test_case):
    max_attention_window = VSWA_ATTENTION[Path(gemma_model_root).stem]
    gemma_1gpu_summary(batch_size, data_type, gemma_model_root, llm_venv,
                       cmodel_dir, engine_dir, gemma_example_root,
                       llm_datasets_root, llm_rouge_root, test_case,
                       max_attention_window)


@pytest.mark.timeout(7200)
@pytest.mark.parametrize("batch_size", [8])
@pytest.mark.parametrize("data_type", ['float16', 'bfloat16'])
@pytest.mark.parametrize("test_case", [
    'other',
    pytest.param('fp8_kv_cache', marks=skip_post_blackwell),
    pytest.param('smooth_quant', marks=skip_post_blackwell),
    pytest.param('wo_int8', marks=skip_post_blackwell),
    pytest.param('wo_int4', marks=skip_post_blackwell),
    pytest.param('int8_kv_cache', marks=skip_post_blackwell)
])
@pytest.mark.parametrize("gemma_model_root", [
    "gemma-2b", "gemma-7b", "gemma-2b-torch", "gemma-7b-torch",
    "gemma-2b-keras", "gemma-7b-keras", "gemma-2b-it-flax", "gemma-7b-it-flax",
    *GEMMA2_MODELS
],
                         indirect=True)
def test_llm_gemma_1gpu_summary(batch_size, data_type, gemma_model_root,
                                llm_venv, cmodel_dir, engine_dir,
                                gemma_example_root, llm_datasets_root,
                                llm_rouge_root, test_case):
    if "27b" in gemma_model_root and "GH200" in get_gpu_device_list(
    )[0] and "other" in test_case:
        pytest.skip("OOM on GH200. https://nvbugs/5250460")

    gemma_1gpu_summary(batch_size, data_type, gemma_model_root, llm_venv,
                       cmodel_dir, engine_dir, gemma_example_root,
                       llm_datasets_root, llm_rouge_root, test_case)


def gemma_1gpu_summary(batch_size,
                       data_type,
                       gemma_model_root,
                       llm_venv,
                       cmodel_dir,
                       engine_dir,
                       gemma_example_root,
                       llm_datasets_root,
                       llm_rouge_root,
                       test_case,
                       max_attention_window: list[int] | None = None):
    "run gemm test on 1 gpu"
    skip_fp8_pre_ada(use_fp8=test_case == "fp8_kv_cache")
    if "smooth_quant" in test_case and "bfloat16" in data_type:
        pytest.skip("TensorRT LLM does not support SmoothQuant with bfloat16.")

    if any(params in gemma_model_root for params in
           ["gemma-7b", "9b", "27b"]) and get_device_memory() < 50000:
        pytest.skip(f"Insufficient device memory for {gemma_model_root}.")

    ckpt_type = get_ckpt_type(gemma_model_root)
    ckpt_dir = get_ckpt_dir(gemma_model_root)
    vocab_file = get_vocab_file(gemma_model_root)

    print("Convert checkpoint ...")
    convert_cmd = [
        f"{gemma_example_root}/convert_checkpoint.py",
        f"--ckpt-type={ckpt_type}",
        f"--model-dir={ckpt_dir}",
        f"--dtype={data_type}",
        f"--output-model-dir={cmodel_dir}",
    ]

    if "fp8_kv" in test_case:
        convert_cmd.extend(["--enable_fp8", "--fp8_kv_cache"])
    elif "smooth" in test_case:
        convert_cmd.append("--use_smooth_quant_plugin=0.5")
        convert_cmd.append(f"--tokenizer_dir={vocab_file}")
        convert_cmd.append(
            f"--calib_dataset={llm_datasets_root}/ccdv/cnn_dailymail")
    elif "int8_kv" in test_case:
        convert_cmd.append("--calibrate_kv_cache")
        convert_cmd.append(f"--tokenizer_dir={vocab_file}")
        convert_cmd.append(
            f"--calib_dataset={llm_datasets_root}/ccdv/cnn_dailymail")
    elif 'wo_int4' in test_case:
        if ckpt_type != "jax":
            pytest.skip("Only verify int4_wo on jax checkpoint.")
        convert_cmd.append("--use-weight-only-with-precision=int4")
    elif 'wo_int8' in test_case:
        convert_cmd.append("--use-weight-only-with-precision=int8")

    venv_check_call(llm_venv, convert_cmd)

    print("Build engines...")
    build_cmd = [
        "trtllm-build",
        f"--checkpoint_dir={cmodel_dir}",
        f"--output_dir={engine_dir}",
        f"--max_batch_size={batch_size}",
        f"--gpt_attention_plugin={data_type}",
        f"--gemm_plugin={data_type}",
        "--max_beam_width=1",
        "--max_input_len=3000",
        "--max_seq_len=3100",
    ]

    check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)

    window = {
        'max_attention_window_size': max_attention_window
    } if max_attention_window is not None else {}

    print("Run summarize...")
    summary_cmd = generate_summary_cmd(gemma_example_root,
                                       engine_dir=engine_dir,
                                       max_ite=40,
                                       batch_size=batch_size,
                                       tensorrt_llm_rouge1_threshold=15,
                                       dataset_dir=llm_datasets_root,
                                       rouge_dir=llm_rouge_root,
                                       **window)

    if ckpt_type == "hf":
        summary_cmd.extend([
            f"--hf_model_dir={gemma_model_root}",
            f"--tokenizer_dir={gemma_model_root}"
        ])
    else:
        summary_cmd.append(f"--vocab_file={vocab_file}")

    venv_check_call(llm_venv, summary_cmd)


@pytest.mark.parametrize("batch_size", [8])
@pytest.mark.parametrize("data_type", ['float16', 'bfloat16'])
@pytest.mark.parametrize("test_case", [
    'other', 'fp8_kv_cache', 'smooth_quant', 'wo_int8', 'wo_int4',
    'int8_kv_cache'
])
@pytest.mark.parametrize("gemma_model_root", [
    "gemma-2b", "gemma-7b", "gemma-2b-torch", "gemma-7b-torch",
    "gemma-2b-keras", "gemma-7b-keras", "gemma-2b-it-flax", "gemma-7b-it-flax"
],
                         indirect=True)
def test_llm_gemma_1gpu_mmlu(batch_size, data_type, gemma_model_root, llm_venv,
                             cmodel_dir, engine_dir, gemma_example_root,
                             llm_rouge_root, llm_datasets_root, test_case):
    "run gemm test on 1 gpu"
    if "smooth_quant" in test_case and "bfloat16" in data_type:
        pytest.skip("TensorRT LLM does not support SmoothQuant with bfloat16.")
    ckpt_type = get_ckpt_type(gemma_model_root)
    ckpt_dir = get_ckpt_dir(gemma_model_root)
    vocab_file = get_vocab_file(gemma_model_root)

    print("Download checkpoint")
    data_path = Path(engine_dir) / "data"
    data_path.mkdir(parents=True, exist_ok=True)

    print("Convert checkpoint ...")
    convert_cmd = [
        f"{gemma_example_root}/convert_checkpoint.py",
        f"--ckpt-type={ckpt_type}",
        f"--model-dir={ckpt_dir}",
        f"--dtype={data_type}",
        f"--output-model-dir={cmodel_dir}",
    ]

    if "fp8_kv" in test_case:
        convert_cmd.extend(["--enable_fp8", "--fp8_kv_cache"])
    elif "smooth" in test_case:
        convert_cmd.append("--use_smooth_quant_plugin=0.5")
        convert_cmd.append(f"--tokenizer_dir={vocab_file}")
        convert_cmd.append(
            f"--calib_dataset={llm_datasets_root}/ccdv/cnn_dailymail")
    elif "int8_kv" in test_case:
        convert_cmd.append("--calibrate_kv_cache")
        convert_cmd.append(f"--tokenizer_dir={vocab_file}")
        convert_cmd.append(
            f"--calib_dataset={llm_datasets_root}/ccdv/cnn_dailymail")
    elif 'wo_int4' in test_case:
        if ckpt_type != "jax":
            pytest.skip("Only verify int4_wo on jax checkpoint.")
        convert_cmd.append("--use-weight-only-with-precision=int4")
    elif 'wo_int8' in test_case:
        convert_cmd.append("--use-weight-only-with-precision=int8")

    venv_check_call(llm_venv, convert_cmd)

    print("Build engines...")
    build_cmd = [
        "trtllm-build",
        f"--checkpoint_dir={cmodel_dir}",
        f"--output_dir={engine_dir}",
        f"--max_batch_size={batch_size}",
        f"--gpt_attention_plugin={data_type}",
        f"--gemm_plugin={data_type}",
        "--max_beam_width=1",
        "--max_input_len=3000",
        "--max_seq_len=3100",
    ]

    check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env)

    print("Run mmlu...")
    mmlu_cmd = [
        "trtllm-eval", f"--model={engine_dir}",
        f"--tokenizer={gemma_model_root}", "--backend=tensorrt", "mmlu",
        f"--dataset_path={llm_datasets_root}/mmlu", "--check_accuracy",
        f"--accuracy_threshold={37}"
    ]
    check_call(" ".join(mmlu_cmd), shell=True, env=llm_venv._new_env)


@skip_pre_hopper
@skip_post_blackwell
@pytest.mark.parametrize(
    "gemma_model_root",
    ["gemma-2b", "gemma-7b", *GEMMA2_MODELS, "gemma-3-1b-it"],
    indirect=True)
def test_hf_gemma_fp8_base_bf16_multi_lora(gemma_model_root,
                                           llm_venv,
                                           cmodel_dir,
                                           engine_dir,
                                           gemma_example_root,
                                           llm_datasets_root,
                                           data_type='bfloat16',
                                           qformat='fp8',
                                           batch_size=8):
    "Run Gemma models with multiple dummy LoRAs."

    start_time = time.time()
    print("Convert checkpoint by modelopt...")
    convert_start = time.time()
    kv_cache_dtype = 'fp8' if qformat == 'fp8' else 'int8'
    convert_cmd = [
        f"{gemma_example_root}/../../../quantization/quantize.py",
        f"--model_dir={gemma_model_root}",
        f"--calib_dataset={llm_datasets_root}/cnn_dailymail",
        f"--dtype={data_type}",
        f"--qformat={qformat}",
        f"--kv_cache_dtype={kv_cache_dtype}",
        f"--output_dir={cmodel_dir}",
    ]
    venv_check_call(llm_venv, convert_cmd)
    convert_end = time.time()
    print(
        f"Convert checkpoint completed in {(convert_end - convert_start):.2f} seconds."
    )

    test_multi_lora_start = time.time()
    print("Calling test_multi_lora_support...")
    test_multi_lora_support(
        hf_model_dir=gemma_model_root,
        tllm_ckpt_dir=cmodel_dir,
        engine_dir=engine_dir,
        llm_venv=llm_venv,
        example_root=gemma_example_root,
        num_loras=2,
        lora_rank=8,
        target_hf_modules=["q_proj", "k_proj", "v_proj"],
        target_trtllm_modules=["attn_q", "attn_k", "attn_v"],
        zero_lora_weights=True,
    )
    test_multi_lora_end = time.time()
    print(
        f"test_multi_lora_support completed in {(test_multi_lora_end - test_multi_lora_start):.2f} seconds"
    )

    total_time = time.time() - start_time
    print(f"Total function execution time: {total_time:.2f} seconds")