# coding=utf-8
# Copyright 2026 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 gc

import pytest
import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.torch_utils import randn_tensor

from ...testing_utils import (
    backend_empty_cache,
    enable_full_determinism,
    load_hf_numpy,
    require_torch_accelerator,
    require_torch_accelerator_with_fp16,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_all_close,
    torch_device,
)
from ..testing_utils import BaseModelTesterConfig, MemoryTesterMixin, ModelTesterMixin, TrainingTesterMixin
from .testing_utils import NewAutoencoderTesterMixin


enable_full_determinism()


class AutoencoderKLTesterConfig(BaseModelTesterConfig):
    @property
    def model_class(self):
        return AutoencoderKL

    @property
    def main_input_name(self) -> str:
        return "sample"

    @property
    def output_shape(self):
        return (3, 32, 32)

    @property
    def generator(self):
        return torch.Generator("cpu").manual_seed(0)

    def get_init_dict(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
        return {
            "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,
        }

    def get_dummy_inputs(self):
        batch_size = 4
        num_channels = 3
        sizes = (32, 32)
        image = randn_tensor((batch_size, num_channels, *sizes), generator=self.generator, device=torch_device)
        return {"sample": image}


class TestAutoencoderKL(AutoencoderKLTesterConfig, ModelTesterMixin, TrainingTesterMixin):
    @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16], ids=["fp16", "bf16"])
    def test_from_save_pretrained_dtype_inference(self, tmp_path, dtype):
        # The reference and reloaded models hold identical weights, so any output difference is
        # half-precision kernel nondeterminism between the two module instances rather than a save/load
        # fidelity issue. The default zero-rtol, 1e-4-atol tolerance is too tight for that noise, and it has to
        # be relaxed per dtype: bf16's coarser mantissa produces a larger absolute noise floor (~1 ULP, e.g.
        # 3.9e-3 near magnitude 0.5) than fp16's (~1e-3), so a single shared tolerance is either too tight for
        # bf16 or needlessly loose for fp16. Each dtype gets an absolute floor sized to its own noise plus
        # `torch.testing.assert_close`'s recommended rtol (1e-3 for fp16, 1.6e-2 for bf16) so the allowed error
        # also scales with the output magnitude.
        atol, rtol = {
            torch.float16: (2e-3, 1e-3),
            torch.bfloat16: (5e-3, 1.6e-2),
        }[dtype]
        super().test_from_save_pretrained_dtype_inference(tmp_path, dtype, atol=atol, rtol=rtol)

    def test_gradient_checkpointing_is_applied(self):
        expected_set = {"Decoder", "Encoder", "UNetMidBlock2D"}
        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)

    def test_from_pretrained_hub(self):
        model, loading_info = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy", output_loading_info=True)
        assert model is not None
        assert len(loading_info["missing_keys"]) == 0

        model.to(torch_device)
        image = model(**self.get_dummy_inputs())

        assert image is not None, "Make sure output is not None"

    def test_output_pretrained(self):
        model = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy")
        model = model.to(torch_device)
        model.eval()

        # Keep generator on CPU for non-CUDA devices to compare outputs with CPU result tensors
        generator_device = "cpu" if not torch_device.startswith(torch_device) else torch_device
        if torch_device != "mps":
            generator = torch.Generator(device=generator_device).manual_seed(0)
        else:
            generator = torch.manual_seed(0)

        image = torch.randn(
            1,
            model.config.in_channels,
            model.config.sample_size,
            model.config.sample_size,
            generator=torch.manual_seed(0),
        )
        image = image.to(torch_device)
        with torch.no_grad():
            output = model(image, sample_posterior=True, generator=generator).sample

        output_slice = output[0, -1, -3:, -3:].flatten().cpu()

        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
        # the expected output slices are not the same for CPU and GPU.
        if torch_device == "mps":
            expected_output_slice = torch.tensor(
                [
                    -4.0078e-01,
                    -3.8323e-04,
                    -1.2681e-01,
                    -1.1462e-01,
                    2.0095e-01,
                    1.0893e-01,
                    -8.8247e-02,
                    -3.0361e-01,
                    -9.8644e-03,
                ]
            )
        elif generator_device == "cpu":
            expected_output_slice = torch.tensor(
                [
                    -0.1352,
                    0.0878,
                    0.0419,
                    -0.0818,
                    -0.1069,
                    0.0688,
                    -0.1458,
                    -0.4446,
                    -0.0026,
                ]
            )
        else:
            expected_output_slice = torch.tensor(
                [
                    -0.2421,
                    0.4642,
                    0.2507,
                    -0.0438,
                    0.0682,
                    0.3160,
                    -0.2018,
                    -0.0727,
                    0.2485,
                ]
            )

        assert torch_all_close(output_slice, expected_output_slice, rtol=1e-2)


class TestAutoencoderKLMemory(AutoencoderKLTesterConfig, MemoryTesterMixin):
    """Memory optimization tests for AutoencoderKL."""


class TestAutoencoderKLSlicingTiling(AutoencoderKLTesterConfig, NewAutoencoderTesterMixin):
    """Slicing and tiling tests for AutoencoderKL."""


@slow
class AutoencoderKLIntegrationTests:
    def get_file_format(self, seed, shape):
        return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"

    def teardown_method(self):
        # clean up the VRAM after each test
        gc.collect()
        backend_empty_cache(torch_device)

    def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False):
        dtype = torch.float16 if fp16 else torch.float32
        image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
        return image

    def get_sd_vae_model(self, model_id="CompVis/stable-diffusion-v1-4", fp16=False):
        revision = "fp16" if fp16 else None
        torch_dtype = torch.float16 if fp16 else torch.float32

        model = AutoencoderKL.from_pretrained(
            model_id,
            subfolder="vae",
            torch_dtype=torch_dtype,
            revision=revision,
        )
        model.to(torch_device)

        return model

    def get_generator(self, seed=0):
        generator_device = "cpu" if not torch_device.startswith(torch_device) else torch_device
        if torch_device != "mps":
            return torch.Generator(device=generator_device).manual_seed(seed)
        return torch.manual_seed(seed)

    @parameterized.expand(
        [
            # fmt: off
            [
                33,
                [-0.1556, 0.9848, -0.0410, -0.0642, -0.2685, 0.8381, -0.2004, -0.0700],
                [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824],
            ],
            [
                47,
                [-0.2376, 0.1200, 0.1337, -0.4830, -0.2504, -0.0759, -0.0486, -0.4077],
                [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131],
            ],
            # fmt: on
        ]
    )
    def test_stable_diffusion(self, seed, expected_slice, expected_slice_mps):
        model = self.get_sd_vae_model()
        image = self.get_sd_image(seed)
        generator = self.get_generator(seed)

        with torch.no_grad():
            sample = model(image, generator=generator, sample_posterior=True).sample

        assert sample.shape == image.shape

        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
        expected_output_slice = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice)

        assert torch_all_close(output_slice, expected_output_slice, atol=3e-3)

    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
            [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
            # fmt: on
        ]
    )
    @require_torch_accelerator_with_fp16
    def test_stable_diffusion_fp16(self, seed, expected_slice):
        model = self.get_sd_vae_model(fp16=True)
        image = self.get_sd_image(seed, fp16=True)
        generator = self.get_generator(seed)

        with torch.no_grad():
            sample = model(image, generator=generator, sample_posterior=True).sample

        assert sample.shape == image.shape

        output_slice = sample[-1, -2:, :2, -2:].flatten().float().cpu()
        expected_output_slice = torch.tensor(expected_slice)

        assert torch_all_close(output_slice, expected_output_slice, atol=1e-2)

    @parameterized.expand(
        [
            # fmt: off
            [
                33,
                [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814],
                [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824],
            ],
            [
                47,
                [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085],
                [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131],
            ],
            # fmt: on
        ]
    )
    def test_stable_diffusion_mode(self, seed, expected_slice, expected_slice_mps):
        model = self.get_sd_vae_model()
        image = self.get_sd_image(seed)

        with torch.no_grad():
            sample = model(image).sample

        assert sample.shape == image.shape

        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
        expected_output_slice = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice)

        assert torch_all_close(output_slice, expected_output_slice, atol=3e-3)

    @parameterized.expand(
        [
            # fmt: off
            [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
            [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
            # fmt: on
        ]
    )
    @require_torch_accelerator
    @skip_mps
    def test_stable_diffusion_decode(self, seed, expected_slice):
        model = self.get_sd_vae_model()
        encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))

        with torch.no_grad():
            sample = model.decode(encoding).sample

        assert list(sample.shape) == [3, 3, 512, 512]

        output_slice = sample[-1, -2:, :2, -2:].flatten().cpu()
        expected_output_slice = torch.tensor(expected_slice)

        assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)

    @parameterized.expand(
        [
            # fmt: off
            [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
            [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
            # fmt: on
        ]
    )
    @require_torch_accelerator_with_fp16
    def test_stable_diffusion_decode_fp16(self, seed, expected_slice):
        model = self.get_sd_vae_model(fp16=True)
        encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64), fp16=True)

        with torch.no_grad():
            sample = model.decode(encoding).sample

        assert list(sample.shape) == [3, 3, 512, 512]

        output_slice = sample[-1, -2:, :2, -2:].flatten().float().cpu()
        expected_output_slice = torch.tensor(expected_slice)

        assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)

    @parameterized.expand([(13,), (16,), (27,)])
    @require_torch_gpu
    @pytest.mark.skipif(not is_xformers_available(), reason="xformers is not required when using PyTorch 2.0.")
    def test_stable_diffusion_decode_xformers_vs_2_0_fp16(self, seed):
        model = self.get_sd_vae_model(fp16=True)
        encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64), fp16=True)

        with torch.no_grad():
            sample = model.decode(encoding).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
            sample_2 = model.decode(encoding).sample

        assert list(sample.shape) == [3, 3, 512, 512]

        assert torch_all_close(sample, sample_2, atol=1e-1)

    @parameterized.expand([(13,), (16,), (37,)])
    @require_torch_gpu
    @pytest.mark.skipif(not is_xformers_available(), reason="xformers is not required when using PyTorch 2.0.")
    def test_stable_diffusion_decode_xformers_vs_2_0(self, seed):
        model = self.get_sd_vae_model()
        encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))

        with torch.no_grad():
            sample = model.decode(encoding).sample

        model.enable_xformers_memory_efficient_attention()
        with torch.no_grad():
            sample_2 = model.decode(encoding).sample

        assert list(sample.shape) == [3, 3, 512, 512]

        assert torch_all_close(sample, sample_2, atol=1e-2)

    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
            [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
            # fmt: on
        ]
    )
    def test_stable_diffusion_encode_sample(self, seed, expected_slice):
        model = self.get_sd_vae_model()
        image = self.get_sd_image(seed)
        generator = self.get_generator(seed)

        with torch.no_grad():
            dist = model.encode(image).latent_dist
            sample = dist.sample(generator=generator)

        assert list(sample.shape) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

        output_slice = sample[0, -1, -3:, -3:].flatten().cpu()
        expected_output_slice = torch.tensor(expected_slice)

        tolerance = 3e-3 if torch_device != "mps" else 1e-2
        assert torch_all_close(output_slice, expected_output_slice, atol=tolerance)