# Copyright 2025 HuggingFace Inc.
#
# 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 contextlib
import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.hooks import HookRegistry, ModelHook
from diffusers.models import ModelMixin
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.utils import get_logger
from diffusers.utils.import_utils import compare_versions

from ..testing_utils import (
    backend_empty_cache,
    backend_max_memory_allocated,
    backend_reset_peak_memory_stats,
    require_torch_accelerator,
    torch_device,
)


class DummyBlock(torch.nn.Module):
    def __init__(self, in_features: int, hidden_features: int, out_features: int) -> None:
        super().__init__()

        self.proj_in = torch.nn.Linear(in_features, hidden_features)
        self.activation = torch.nn.ReLU()
        self.proj_out = torch.nn.Linear(hidden_features, out_features)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.proj_in(x)
        x = self.activation(x)
        x = self.proj_out(x)
        return x


class DummyModel(ModelMixin):
    def __init__(self, in_features: int, hidden_features: int, out_features: int, num_layers: int) -> None:
        super().__init__()

        self.linear_1 = torch.nn.Linear(in_features, hidden_features)
        self.activation = torch.nn.ReLU()
        self.blocks = torch.nn.ModuleList(
            [DummyBlock(hidden_features, hidden_features, hidden_features) for _ in range(num_layers)]
        )
        self.linear_2 = torch.nn.Linear(hidden_features, out_features)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.linear_1(x)
        x = self.activation(x)
        for block in self.blocks:
            x = block(x)
        x = self.linear_2(x)
        return x


# This model implementation contains one type of block (single_blocks) instantiated before another type of block (double_blocks).
# The invocation order of these blocks, however, is first the double_blocks and then the single_blocks.
# With group offloading implementation before https://github.com/huggingface/diffusers/pull/11375, such a modeling implementation
# would result in a device mismatch error because of the assumptions made by the code. The failure case occurs when using:
#   offload_type="block_level", num_blocks_per_group=2, use_stream=True
# Post the linked PR, the implementation will work as expected.
class DummyModelWithMultipleBlocks(ModelMixin):
    def __init__(
        self, in_features: int, hidden_features: int, out_features: int, num_layers: int, num_single_layers: int
    ) -> None:
        super().__init__()

        self.linear_1 = torch.nn.Linear(in_features, hidden_features)
        self.activation = torch.nn.ReLU()
        self.single_blocks = torch.nn.ModuleList(
            [DummyBlock(hidden_features, hidden_features, hidden_features) for _ in range(num_single_layers)]
        )
        self.double_blocks = torch.nn.ModuleList(
            [DummyBlock(hidden_features, hidden_features, hidden_features) for _ in range(num_layers)]
        )
        self.linear_2 = torch.nn.Linear(hidden_features, out_features)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.linear_1(x)
        x = self.activation(x)
        for block in self.double_blocks:
            x = block(x)
        for block in self.single_blocks:
            x = block(x)
        x = self.linear_2(x)
        return x


# Test for https://github.com/huggingface/diffusers/pull/12077
class DummyModelWithLayerNorm(ModelMixin):
    def __init__(self, in_features: int, hidden_features: int, out_features: int, num_layers: int) -> None:
        super().__init__()

        self.linear_1 = torch.nn.Linear(in_features, hidden_features)
        self.activation = torch.nn.ReLU()
        self.blocks = torch.nn.ModuleList(
            [DummyBlock(hidden_features, hidden_features, hidden_features) for _ in range(num_layers)]
        )
        self.layer_norm = torch.nn.LayerNorm(hidden_features, elementwise_affine=True)
        self.linear_2 = torch.nn.Linear(hidden_features, out_features)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.linear_1(x)
        x = self.activation(x)
        for block in self.blocks:
            x = block(x)
        x = self.layer_norm(x)
        x = self.linear_2(x)
        return x


class DummyPipeline(DiffusionPipeline):
    model_cpu_offload_seq = "model"

    def __init__(self, model: torch.nn.Module) -> None:
        super().__init__()

        self.register_modules(model=model)

    def __call__(self, x: torch.Tensor) -> torch.Tensor:
        for _ in range(2):
            x = x + 0.1 * self.model(x)
        return x


class LayerOutputTrackerHook(ModelHook):
    def __init__(self):
        super().__init__()
        self.outputs = []

    def post_forward(self, module, output):
        self.outputs.append(output)
        return output


# Model with only standalone computational layers at top level
class DummyModelWithStandaloneLayers(ModelMixin):
    def __init__(self, in_features: int, hidden_features: int, out_features: int) -> None:
        super().__init__()

        self.layer1 = torch.nn.Linear(in_features, hidden_features)
        self.activation = torch.nn.ReLU()
        self.layer2 = torch.nn.Linear(hidden_features, hidden_features)
        self.layer3 = torch.nn.Linear(hidden_features, out_features)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.layer1(x)
        x = self.activation(x)
        x = self.layer2(x)
        x = self.layer3(x)
        return x


# Model with deeply nested structure
class DummyModelWithDeeplyNestedBlocks(ModelMixin):
    def __init__(self, in_features: int, hidden_features: int, out_features: int) -> None:
        super().__init__()

        self.input_layer = torch.nn.Linear(in_features, hidden_features)
        self.container = ContainerWithNestedModuleList(hidden_features)
        self.output_layer = torch.nn.Linear(hidden_features, out_features)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.input_layer(x)
        x = self.container(x)
        x = self.output_layer(x)
        return x


class ContainerWithNestedModuleList(torch.nn.Module):
    def __init__(self, features: int) -> None:
        super().__init__()

        # Top-level computational layer
        self.proj_in = torch.nn.Linear(features, features)

        # Nested container with ModuleList
        self.nested_container = NestedContainer(features)

        # Another top-level computational layer
        self.proj_out = torch.nn.Linear(features, features)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.proj_in(x)
        x = self.nested_container(x)
        x = self.proj_out(x)
        return x


class NestedContainer(torch.nn.Module):
    def __init__(self, features: int) -> None:
        super().__init__()

        self.blocks = torch.nn.ModuleList([torch.nn.Linear(features, features), torch.nn.Linear(features, features)])
        self.norm = torch.nn.LayerNorm(features)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        for block in self.blocks:
            x = block(x)
        x = self.norm(x)
        return x


@require_torch_accelerator
class GroupOffloadTests(unittest.TestCase):
    in_features = 64
    hidden_features = 256
    out_features = 64
    num_layers = 4

    def setUp(self):
        with torch.no_grad():
            self.model = self.get_model()
            self.input = torch.randn((4, self.in_features)).to(torch_device)

    def tearDown(self):
        super().tearDown()

        del self.model
        del self.input
        gc.collect()
        backend_empty_cache(torch_device)
        backend_reset_peak_memory_stats(torch_device)

    def get_model(self):
        torch.manual_seed(0)
        return DummyModel(
            in_features=self.in_features,
            hidden_features=self.hidden_features,
            out_features=self.out_features,
            num_layers=self.num_layers,
        )

    def test_offloading_forward_pass(self):
        @torch.no_grad()
        def run_forward(model):
            gc.collect()
            backend_empty_cache(torch_device)
            backend_reset_peak_memory_stats(torch_device)
            self.assertTrue(
                all(
                    module._diffusers_hook.get_hook("group_offloading") is not None
                    for module in model.modules()
                    if hasattr(module, "_diffusers_hook")
                )
            )
            model.eval()
            output = model(self.input)[0].cpu()
            max_memory_allocated = backend_max_memory_allocated(torch_device)
            return output, max_memory_allocated

        self.model.to(torch_device)
        output_without_group_offloading, mem_baseline = run_forward(self.model)
        self.model.to("cpu")

        model = self.get_model()
        model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=3)
        output_with_group_offloading1, mem1 = run_forward(model)

        model = self.get_model()
        model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=1)
        output_with_group_offloading2, mem2 = run_forward(model)

        model = self.get_model()
        model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=1, use_stream=True)
        output_with_group_offloading3, mem3 = run_forward(model)

        model = self.get_model()
        model.enable_group_offload(torch_device, offload_type="leaf_level")
        output_with_group_offloading4, mem4 = run_forward(model)

        model = self.get_model()
        model.enable_group_offload(torch_device, offload_type="leaf_level", use_stream=True)
        output_with_group_offloading5, mem5 = run_forward(model)

        # Precision assertions - offloading should not impact the output
        self.assertTrue(torch.allclose(output_without_group_offloading, output_with_group_offloading1, atol=1e-5))
        self.assertTrue(torch.allclose(output_without_group_offloading, output_with_group_offloading2, atol=1e-5))
        self.assertTrue(torch.allclose(output_without_group_offloading, output_with_group_offloading3, atol=1e-5))
        self.assertTrue(torch.allclose(output_without_group_offloading, output_with_group_offloading4, atol=1e-5))
        self.assertTrue(torch.allclose(output_without_group_offloading, output_with_group_offloading5, atol=1e-5))

        # Memory assertions - offloading should reduce memory usage
        self.assertTrue(mem4 <= mem5 < mem2 <= mem3 < mem1 < mem_baseline)

    def test_warning_logged_if_group_offloaded_module_moved_to_accelerator(self):
        if torch.device(torch_device).type not in ["cuda", "xpu"]:
            return
        self.model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=3)
        logger = get_logger("diffusers.models.modeling_utils")
        logger.setLevel("INFO")
        with self.assertLogs(logger, level="WARNING") as cm:
            self.model.to(torch_device)
        self.assertIn(f"The module '{self.model.__class__.__name__}' is group offloaded", cm.output[0])

    def test_warning_logged_if_group_offloaded_pipe_moved_to_accelerator(self):
        if torch.device(torch_device).type not in ["cuda", "xpu"]:
            return
        pipe = DummyPipeline(self.model)
        self.model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=3)
        logger = get_logger("diffusers.pipelines.pipeline_utils")
        logger.setLevel("INFO")
        with self.assertLogs(logger, level="WARNING") as cm:
            pipe.to(torch_device)
        self.assertIn(f"The module '{self.model.__class__.__name__}' is group offloaded", cm.output[0])

    def test_error_raised_if_streams_used_and_no_accelerator_device(self):
        torch_accelerator_module = getattr(torch, torch_device, torch.cuda)
        original_is_available = torch_accelerator_module.is_available
        torch_accelerator_module.is_available = lambda: False
        with self.assertRaises(ValueError):
            self.model.enable_group_offload(
                onload_device=torch.device(torch_device), offload_type="leaf_level", use_stream=True
            )
        torch_accelerator_module.is_available = original_is_available

    def test_error_raised_if_supports_group_offloading_false(self):
        self.model._supports_group_offloading = False
        with self.assertRaisesRegex(ValueError, "does not support group offloading"):
            self.model.enable_group_offload(onload_device=torch.device(torch_device))

    def test_error_raised_if_model_offloading_applied_on_group_offloaded_module(self):
        pipe = DummyPipeline(self.model)
        pipe.model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=3)
        with self.assertRaisesRegex(ValueError, "You are trying to apply model/sequential CPU offloading"):
            pipe.enable_model_cpu_offload()

    def test_error_raised_if_sequential_offloading_applied_on_group_offloaded_module(self):
        pipe = DummyPipeline(self.model)
        pipe.model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=3)
        with self.assertRaisesRegex(ValueError, "You are trying to apply model/sequential CPU offloading"):
            pipe.enable_sequential_cpu_offload()

    def test_error_raised_if_group_offloading_applied_on_model_offloaded_module(self):
        pipe = DummyPipeline(self.model)
        pipe.enable_model_cpu_offload()
        with self.assertRaisesRegex(ValueError, "Cannot apply group offloading"):
            pipe.model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=3)

    def test_error_raised_if_group_offloading_applied_on_sequential_offloaded_module(self):
        pipe = DummyPipeline(self.model)
        pipe.enable_sequential_cpu_offload()
        with self.assertRaisesRegex(ValueError, "Cannot apply group offloading"):
            pipe.model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=3)

    def test_block_level_stream_with_invocation_order_different_from_initialization_order(self):
        if torch.device(torch_device).type not in ["cuda", "xpu"]:
            return

        model = DummyModelWithMultipleBlocks(
            in_features=self.in_features,
            hidden_features=self.hidden_features,
            out_features=self.out_features,
            num_layers=self.num_layers,
            num_single_layers=self.num_layers + 1,
        )
        model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=1, use_stream=True)

        context = contextlib.nullcontext()
        if compare_versions("diffusers", "<=", "0.33.0"):
            # Will raise a device mismatch RuntimeError mentioning weights are on CPU but input is on device
            context = self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device")

        with context:
            model(self.input)

    @parameterized.expand([("block_level",), ("leaf_level",)])
    def test_block_level_offloading_with_parameter_only_module_group(self, offload_type: str):
        if torch.device(torch_device).type not in ["cuda", "xpu"]:
            return

        def apply_layer_output_tracker_hook(model: DummyModelWithLayerNorm):
            for name, module in model.named_modules():
                registry = HookRegistry.check_if_exists_or_initialize(module)
                hook = LayerOutputTrackerHook()
                registry.register_hook(hook, "layer_output_tracker")

        model_ref = DummyModelWithLayerNorm(128, 256, 128, 2)
        model = DummyModelWithLayerNorm(128, 256, 128, 2)

        model.load_state_dict(model_ref.state_dict(), strict=True)

        model_ref.to(torch_device)
        model.enable_group_offload(torch_device, offload_type=offload_type, num_blocks_per_group=1, use_stream=True)

        apply_layer_output_tracker_hook(model_ref)
        apply_layer_output_tracker_hook(model)

        x = torch.randn(2, 128).to(torch_device)

        out_ref = model_ref(x)
        out = model(x)
        self.assertTrue(torch.allclose(out_ref, out, atol=1e-5), "Outputs do not match.")

        num_repeats = 2
        for i in range(num_repeats):
            out_ref = model_ref(x)
            out = model(x)

        self.assertTrue(torch.allclose(out_ref, out, atol=1e-5), "Outputs do not match after multiple invocations.")

        for (ref_name, ref_module), (name, module) in zip(model_ref.named_modules(), model.named_modules()):
            assert ref_name == name
            ref_outputs = (
                HookRegistry.check_if_exists_or_initialize(ref_module).get_hook("layer_output_tracker").outputs
            )
            outputs = HookRegistry.check_if_exists_or_initialize(module).get_hook("layer_output_tracker").outputs
            cumulated_absmax = 0.0
            for i in range(len(outputs)):
                diff = ref_outputs[0] - outputs[i]
                absdiff = diff.abs()
                absmax = absdiff.max().item()
                cumulated_absmax += absmax
            self.assertLess(
                cumulated_absmax, 1e-5, f"Output differences for {name} exceeded threshold: {cumulated_absmax:.5f}"
            )

    def test_vae_like_model_without_streams(self):
        """Test VAE-like model with block-level offloading but without streams."""
        if torch.device(torch_device).type not in ["cuda", "xpu"]:
            return

        config = self.get_autoencoder_kl_config()
        model = AutoencoderKL(**config)

        model_ref = AutoencoderKL(**config)
        model_ref.load_state_dict(model.state_dict(), strict=True)
        model_ref.to(torch_device)

        model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=1, use_stream=False)

        x = torch.randn(2, 3, 32, 32).to(torch_device)

        with torch.no_grad():
            out_ref = model_ref(x).sample
            out = model(x).sample

        self.assertTrue(
            torch.allclose(out_ref, out, atol=1e-5), "Outputs do not match for VAE-like model without streams."
        )

    def test_model_with_only_standalone_layers(self):
        """Test that models with only standalone layers (no ModuleList/Sequential) work with block-level offloading."""
        if torch.device(torch_device).type not in ["cuda", "xpu"]:
            return

        model = DummyModelWithStandaloneLayers(in_features=64, hidden_features=128, out_features=64)

        model_ref = DummyModelWithStandaloneLayers(in_features=64, hidden_features=128, out_features=64)
        model_ref.load_state_dict(model.state_dict(), strict=True)
        model_ref.to(torch_device)

        model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=1, use_stream=True)

        x = torch.randn(2, 64).to(torch_device)

        with torch.no_grad():
            for i in range(2):
                out_ref = model_ref(x)
                out = model(x)
                self.assertTrue(
                    torch.allclose(out_ref, out, atol=1e-5),
                    f"Outputs do not match at iteration {i} for model with standalone layers.",
                )

    @parameterized.expand([("block_level",), ("leaf_level",)])
    def test_standalone_conv_layers_with_both_offload_types(self, offload_type: str):
        """Test that standalone Conv2d layers work correctly with both block-level and leaf-level offloading."""
        if torch.device(torch_device).type not in ["cuda", "xpu"]:
            return

        config = self.get_autoencoder_kl_config()
        model = AutoencoderKL(**config)

        model_ref = AutoencoderKL(**config)
        model_ref.load_state_dict(model.state_dict(), strict=True)
        model_ref.to(torch_device)

        model.enable_group_offload(torch_device, offload_type=offload_type, num_blocks_per_group=1, use_stream=True)

        x = torch.randn(2, 3, 32, 32).to(torch_device)

        with torch.no_grad():
            out_ref = model_ref(x).sample
            out = model(x).sample

        self.assertTrue(
            torch.allclose(out_ref, out, atol=1e-5),
            f"Outputs do not match for standalone Conv layers with {offload_type}.",
        )

    def test_multiple_invocations_with_vae_like_model(self):
        """Test that multiple forward passes work correctly with VAE-like model."""
        if torch.device(torch_device).type not in ["cuda", "xpu"]:
            return

        config = self.get_autoencoder_kl_config()
        model = AutoencoderKL(**config)

        model_ref = AutoencoderKL(**config)
        model_ref.load_state_dict(model.state_dict(), strict=True)
        model_ref.to(torch_device)

        model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=1, use_stream=True)

        x = torch.randn(2, 3, 32, 32).to(torch_device)

        with torch.no_grad():
            for i in range(2):
                out_ref = model_ref(x).sample
                out = model(x).sample
                self.assertTrue(torch.allclose(out_ref, out, atol=1e-5), f"Outputs do not match at iteration {i}.")

    def test_nested_container_parameters_offloading(self):
        """Test that parameters from non-computational layers in nested containers are handled correctly."""
        if torch.device(torch_device).type not in ["cuda", "xpu"]:
            return

        model = DummyModelWithDeeplyNestedBlocks(in_features=64, hidden_features=128, out_features=64)

        model_ref = DummyModelWithDeeplyNestedBlocks(in_features=64, hidden_features=128, out_features=64)
        model_ref.load_state_dict(model.state_dict(), strict=True)
        model_ref.to(torch_device)

        model.enable_group_offload(torch_device, offload_type="block_level", num_blocks_per_group=1, use_stream=True)

        x = torch.randn(2, 64).to(torch_device)

        with torch.no_grad():
            for i in range(2):
                out_ref = model_ref(x)
                out = model(x)
                self.assertTrue(
                    torch.allclose(out_ref, out, atol=1e-5),
                    f"Outputs do not match at iteration {i} for nested parameters.",
                )

    def get_autoencoder_kl_config(self, block_out_channels=None, norm_num_groups=None):
        block_out_channels = block_out_channels or [2, 4]
        norm_num_groups = norm_num_groups or 2
        init_dict = {
            "block_out_channels": block_out_channels,
            "in_channels": 3,
            "out_channels": 3,
            "down_block_types": ["DownEncoderBlock2D"] * len(block_out_channels),
            "up_block_types": ["UpDecoderBlock2D"] * len(block_out_channels),
            "latent_channels": 4,
            "norm_num_groups": norm_num_groups,
            "layers_per_block": 1,
        }
        return init_dict
