# Copyright 2024 the LlamaFactory 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 os
from typing import Sequence

import torch
from peft import LoraModel, PeftModel
from transformers import AutoModelForCausalLM
from trl import AutoModelForCausalLMWithValueHead

from llamafactory.extras.misc import get_current_device
from llamafactory.hparams import get_infer_args, get_train_args
from llamafactory.model import load_model, load_tokenizer


TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")

TINY_LLAMA_ADAPTER = os.environ.get("TINY_LLAMA_ADAPTER", "llamafactory/tiny-random-Llama-3-lora")

TINY_LLAMA_VALUEHEAD = os.environ.get("TINY_LLAMA_VALUEHEAD", "llamafactory/tiny-random-Llama-3-valuehead")

TRAIN_ARGS = {
    "model_name_or_path": TINY_LLAMA,
    "stage": "sft",
    "do_train": True,
    "finetuning_type": "lora",
    "dataset": "llamafactory/tiny-supervised-dataset",
    "dataset_dir": "ONLINE",
    "template": "llama3",
    "cutoff_len": 1024,
    "overwrite_cache": True,
    "output_dir": "dummy_dir",
    "overwrite_output_dir": True,
    "fp16": True,
}

INFER_ARGS = {
    "model_name_or_path": TINY_LLAMA,
    "adapter_name_or_path": TINY_LLAMA_ADAPTER,
    "finetuning_type": "lora",
    "template": "llama3",
    "infer_dtype": "float16",
}


def load_reference_model() -> "torch.nn.Module":
    model = AutoModelForCausalLM.from_pretrained(TINY_LLAMA)
    return PeftModel.from_pretrained(model, TINY_LLAMA_ADAPTER)


def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module", diff_keys: Sequence[str] = []):
    state_dict_a = model_a.state_dict()
    state_dict_b = model_b.state_dict()
    assert set(state_dict_a.keys()) == set(state_dict_b.keys())
    for name in state_dict_a.keys():
        if any(key in name for key in diff_keys):
            assert torch.allclose(state_dict_a[name], state_dict_b[name]) is False
        else:
            assert torch.allclose(state_dict_a[name], state_dict_b[name]) is True


def test_lora_train_qv_modules():
    model_args, _, _, finetuning_args, _ = get_train_args({"lora_target": "q_proj,v_proj", **TRAIN_ARGS})
    tokenizer_module = load_tokenizer(model_args)
    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)

    linear_modules = set()
    for name, param in model.named_parameters():
        if any(module in name for module in ["lora_A", "lora_B"]):
            linear_modules.add(name.split(".lora_", maxsplit=1)[0].split(".")[-1])
            assert param.requires_grad is True
            assert param.dtype == torch.float32
        else:
            assert param.requires_grad is False
            assert param.dtype == torch.float16

    assert linear_modules == {"q_proj", "v_proj"}


def test_lora_train_all_modules():
    model_args, _, _, finetuning_args, _ = get_train_args({"lora_target": "all", **TRAIN_ARGS})
    tokenizer_module = load_tokenizer(model_args)
    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)

    linear_modules = set()
    for name, param in model.named_parameters():
        if any(module in name for module in ["lora_A", "lora_B"]):
            linear_modules.add(name.split(".lora_", maxsplit=1)[0].split(".")[-1])
            assert param.requires_grad is True
            assert param.dtype == torch.float32
        else:
            assert param.requires_grad is False
            assert param.dtype == torch.float16

    assert linear_modules == {"q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "gate_proj", "down_proj"}


def test_lora_train_extra_modules():
    model_args, _, _, finetuning_args, _ = get_train_args(
        {"lora_target": "all", "additional_target": "embed_tokens,lm_head", **TRAIN_ARGS}
    )
    tokenizer_module = load_tokenizer(model_args)
    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)

    extra_modules = set()
    for name, param in model.named_parameters():
        if any(module in name for module in ["lora_A", "lora_B"]):
            assert param.requires_grad is True
            assert param.dtype == torch.float32
        elif "modules_to_save" in name:
            extra_modules.add(name.split(".modules_to_save", maxsplit=1)[0].split(".")[-1])
            assert param.requires_grad is True
            assert param.dtype == torch.float32
        else:
            assert param.requires_grad is False
            assert param.dtype == torch.float16

    assert extra_modules == {"embed_tokens", "lm_head"}


def test_lora_train_old_adapters():
    model_args, _, _, finetuning_args, _ = get_train_args(
        {"adapter_name_or_path": TINY_LLAMA_ADAPTER, "create_new_adapter": False, **TRAIN_ARGS}
    )
    tokenizer_module = load_tokenizer(model_args)
    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)

    base_model = AutoModelForCausalLM.from_pretrained(
        TINY_LLAMA, torch_dtype=torch.float16, device_map=get_current_device()
    )
    ref_model = PeftModel.from_pretrained(base_model, TINY_LLAMA_ADAPTER, is_trainable=True)
    for param in filter(lambda p: p.requires_grad, ref_model.parameters()):
        param.data = param.data.to(torch.float32)

    compare_model(model, ref_model)


def test_lora_train_new_adapters():
    model_args, _, _, finetuning_args, _ = get_train_args(
        {"adapter_name_or_path": TINY_LLAMA_ADAPTER, "create_new_adapter": True, **TRAIN_ARGS}
    )
    tokenizer_module = load_tokenizer(model_args)
    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)

    base_model = AutoModelForCausalLM.from_pretrained(
        TINY_LLAMA, torch_dtype=torch.float16, device_map=get_current_device()
    )
    ref_model = PeftModel.from_pretrained(base_model, TINY_LLAMA_ADAPTER, is_trainable=True)
    for param in filter(lambda p: p.requires_grad, ref_model.parameters()):
        param.data = param.data.to(torch.float32)

    compare_model(
        model, ref_model, diff_keys=["q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "gate_proj", "down_proj"]
    )


def test_lora_train_valuehead():
    model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
    tokenizer_module = load_tokenizer(model_args)
    model = load_model(
        tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True, add_valuehead=True
    )

    ref_model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(
        TINY_LLAMA_VALUEHEAD, torch_dtype=torch.float16, device_map=get_current_device()
    )
    state_dict = model.state_dict()
    ref_state_dict = ref_model.state_dict()

    assert torch.allclose(state_dict["v_head.summary.weight"], ref_state_dict["v_head.summary.weight"])
    assert torch.allclose(state_dict["v_head.summary.bias"], ref_state_dict["v_head.summary.bias"])


def test_lora_inference():
    model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
    tokenizer_module = load_tokenizer(model_args)
    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=False)

    base_model = AutoModelForCausalLM.from_pretrained(
        TINY_LLAMA, torch_dtype=torch.float16, device_map=get_current_device()
    )
    ref_model: "LoraModel" = PeftModel.from_pretrained(base_model, TINY_LLAMA_ADAPTER)
    ref_model = ref_model.merge_and_unload()
    compare_model(model, ref_model)