# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# 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 gc
import unittest

import accelerate

from transformers import AutoModelForCausalLM, AutoTokenizer, HqqConfig
from transformers.testing_utils import (
    backend_empty_cache,
    require_accelerate,
    require_deterministic_for_xpu,
    require_hqq,
    require_torch_accelerator,
    require_torch_multi_accelerator,
    slow,
    torch_device,
)
from transformers.utils import is_hqq_available, is_torch_available


if is_torch_available():
    import torch

if is_hqq_available():
    from hqq.core.quantize import HQQBackend, HQQLinear


class HQQLLMRunner:
    def __init__(self, model_id, quant_config, compute_dtype, device, cache_dir=None):
        self.model = AutoModelForCausalLM.from_pretrained(
            model_id,
            dtype=compute_dtype,
            device_map=device,
            quantization_config=quant_config,
            cache_dir=cache_dir,
        )
        self.tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=cache_dir)
        self.device = self.model.device
        HQQLinear.set_backend(HQQBackend.PYTORCH)


def cleanup():
    backend_empty_cache(torch_device)
    gc.collect()


def check_hqqlayer(test_module, hqq_layer, batch_size=1, context_size=1024):
    # Test HQQ layer
    W_dequant = hqq_layer.dequantize()  # Reconstructed weights
    inputs = (
        torch.randn(
            (batch_size, context_size, hqq_layer.meta["shape"][1]),
            device=hqq_layer.device,
            dtype=hqq_layer.compute_dtype,
        )
        / 10.0
    )
    with torch.no_grad():
        outputs = hqq_layer(inputs)
    test_module.assertEqual(outputs.shape[-1], W_dequant.shape[0])
    test_module.assertEqual(outputs.dtype, hqq_layer.compute_dtype)
    del W_dequant, inputs, outputs
    cleanup()


def check_forward(test_module, model, batch_size=1, context_size=1024):
    # Test forward pass
    with torch.no_grad():
        out = model(torch.zeros([batch_size, context_size], device=model.device, dtype=torch.int32)).logits
    test_module.assertEqual(out.shape[0], batch_size)
    test_module.assertEqual(out.shape[1], context_size)
    cleanup()


MODEL_ID = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"


@require_torch_accelerator
@require_hqq
class HqqConfigTest(unittest.TestCase):
    def test_to_dict(self):
        """
        Makes sure the config format is properly set
        """
        quantization_config = HqqConfig()
        hqq_orig_config = quantization_config.to_dict()

        self.assertEqual(quantization_config.quant_config, hqq_orig_config["quant_config"])


@slow
@require_torch_accelerator
@require_accelerate
@require_hqq
class HQQTest(unittest.TestCase):
    def tearDown(self):
        cleanup()

    def test_fp16_quantized_model(self):
        """
        Simple LLM model testing fp16
        """
        quant_config = HqqConfig(nbits=8, group_size=64)

        hqq_runner = HQQLLMRunner(
            model_id=MODEL_ID, quant_config=quant_config, compute_dtype=torch.float16, device=torch_device
        )

        check_hqqlayer(self, hqq_runner.model.model.layers[0].self_attn.v_proj)
        check_forward(self, hqq_runner.model)

    def test_quantized_model_to_new_device_and_new_dtype(self):
        """
        Simple LLM model testing different devices and dtypes
        """
        quant_config = HqqConfig(nbits=8, group_size=64)

        hqq_runner = HQQLLMRunner(
            model_id=MODEL_ID, quant_config=quant_config, compute_dtype=torch.float16, device=torch_device
        )

        check_hqqlayer(self, hqq_runner.model.model.layers[0].self_attn.v_proj)
        check_forward(self, hqq_runner.model)

        # Remove `accelerate` hooks to enable move the model to a new device
        accelerate.hooks.remove_hook_from_module(hqq_runner.model, recurse=True)

        hqq_runner.model.to("cpu", torch.bfloat16)
        check_hqqlayer(self, hqq_runner.model.model.layers[0].self_attn.v_proj)
        check_forward(self, hqq_runner.model)

        hqq_runner.model.to(torch_device)
        check_hqqlayer(self, hqq_runner.model.model.layers[0].self_attn.v_proj)
        check_forward(self, hqq_runner.model)

    def test_quantized_model_fake_weight_dtype(self):
        quant_config = HqqConfig(nbits=8, group_size=64)

        hqq_runner = HQQLLMRunner(
            model_id=MODEL_ID, quant_config=quant_config, compute_dtype=torch.float16, device=torch_device
        )

        # We use a hack to inject a fake weight to HQQLinear. Check that it works
        self.assertEqual(hqq_runner.model.model.layers[0].self_attn.v_proj.weight.dtype, torch.float16)


@slow
@require_torch_accelerator
@require_torch_multi_accelerator
@require_accelerate
@require_hqq
class HQQTestMultiGPU(unittest.TestCase):
    def tearDown(self):
        cleanup()

    def test_fp16_quantized_model_multipgpu(self):
        """
        Simple LLM model testing fp16 with multi-gpu
        """

        quant_config = HqqConfig(nbits=8, group_size=64)

        hqq_runner = HQQLLMRunner(
            model_id=MODEL_ID, quant_config=quant_config, compute_dtype=torch.float16, device="auto"
        )

        check_hqqlayer(self, hqq_runner.model.model.layers[0].self_attn.v_proj)
        check_forward(self, hqq_runner.model)


@slow
@require_torch_accelerator
@require_accelerate
@require_hqq
class HQQTestBias(unittest.TestCase):
    def tearDown(self):
        cleanup()

    def test_fp16_quantized_model(self):
        """
        Simple LLM model testing fp16 with bias
        """
        quant_config = HqqConfig(nbits=8, group_size=64)

        hqq_runner = HQQLLMRunner(
            model_id="facebook/opt-125m", quant_config=quant_config, compute_dtype=torch.float16, device=torch_device
        )

        check_hqqlayer(self, hqq_runner.model.model.decoder.layers[0].self_attn.v_proj)
        check_forward(self, hqq_runner.model)

    @require_deterministic_for_xpu
    def test_save_and_load_quantized_model(self):
        """
        Test saving and loading a quantized model with bias
        """
        import tempfile

        quant_config = HqqConfig(nbits=8, group_size=64)

        hqq_runner = HQQLLMRunner(
            model_id="facebook/opt-125m", quant_config=quant_config, compute_dtype=torch.float16, device=torch_device
        )

        input_tensor = torch.zeros((1, 8), dtype=torch.int32, device=torch_device)

        # Get reference logits
        with torch.no_grad():
            logits_ref = hqq_runner.model.forward(input_tensor).logits

        with tempfile.TemporaryDirectory() as tmpdirname:
            hqq_runner.model.save_pretrained(tmpdirname)

            del hqq_runner.model
            backend_empty_cache(torch_device)

            model_loaded = AutoModelForCausalLM.from_pretrained(
                tmpdirname, dtype=torch.float16, device_map=torch_device
            )

            with torch.no_grad():
                logits_loaded = model_loaded.forward(input_tensor).logits

            self.assertEqual((logits_loaded - logits_ref).abs().mean().item(), 0)


@slow
@require_torch_accelerator
@require_accelerate
@require_hqq
class HQQSerializationTest(unittest.TestCase):
    def tearDown(self):
        cleanup()

    def test_model_serialization(self):
        """
        Simple HQQ LLM save/load test
        """
        quant_config = HqqConfig(nbits=4, group_size=64)

        hqq_runner = HQQLLMRunner(
            model_id=MODEL_ID, quant_config=quant_config, compute_dtype=torch.float16, device=torch_device
        )

        input_tensor = torch.zeros((1, 8), dtype=torch.int32, device=torch_device)

        with torch.no_grad():
            logits_ref = hqq_runner.model.forward(input_tensor).logits

        # Save
        saved_model_id = "quant_model"
        hqq_runner.model.save_pretrained(saved_model_id)

        # Remove old model
        del hqq_runner.model
        backend_empty_cache(torch_device)

        # Load and check if the logits match
        model_loaded = AutoModelForCausalLM.from_pretrained(
            "quant_model",
            dtype=torch.float16,
            device_map=torch_device,
        )

        with torch.no_grad():
            logits_loaded = model_loaded.forward(input_tensor).logits

        self.assertEqual((logits_loaded - logits_ref).abs().mean().item(), 0)

    def test_model_serialization_dynamic_quant_with_skip(self):
        """
        Simple HQQ LLM save/load test with dynamic quant
        """
        q4_config = {"nbits": 4, "group_size": 64}
        q3_config = {"nbits": 3, "group_size": 64}

        quant_config = HqqConfig(
            dynamic_config={
                "self_attn.q_proj": q4_config,
                "self_attn.k_proj": q4_config,
                "self_attn.v_proj": q4_config,
                "self_attn.o_proj": q4_config,
                "mlp.gate_proj": q3_config,
                "mlp.up_proj": q3_config,
            },
            skip_modules=["lm_head", "down_proj"],
        )

        hqq_runner = HQQLLMRunner(
            model_id=MODEL_ID, quant_config=quant_config, compute_dtype=torch.float16, device=torch_device
        )

        model = hqq_runner.model

        input_tensor = torch.zeros((1, 8), dtype=torch.int32, device=torch_device)
        with torch.no_grad():
            model.forward(input_tensor).logits

        self.assertEqual(isinstance(model.model.layers[1].mlp.down_proj, torch.nn.Linear), True)
        self.assertEqual(model.model.layers[1].self_attn.v_proj.quant_config["weight_quant_params"]["nbits"], 4)
        self.assertEqual(model.model.layers[1].mlp.gate_proj.quant_config["weight_quant_params"]["nbits"], 3)