# coding=utf-8
# Copyright 2024 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 os
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    LCMScheduler,
    UNet2DConditionModel,
)
from diffusers.utils.import_utils import is_peft_available
from diffusers.utils.testing_utils import (
    floats_tensor,
    require_peft_backend,
    require_peft_version_greater,
    torch_device,
)


if is_peft_available():
    from peft import LoraConfig
    from peft.tuners.tuners_utils import BaseTunerLayer
    from peft.utils import get_peft_model_state_dict


def state_dicts_almost_equal(sd1, sd2):
    sd1 = dict(sorted(sd1.items()))
    sd2 = dict(sorted(sd2.items()))

    models_are_equal = True
    for ten1, ten2 in zip(sd1.values(), sd2.values()):
        if (ten1 - ten2).abs().max() > 1e-3:
            models_are_equal = False

    return models_are_equal


def check_if_lora_correctly_set(model) -> bool:
    """
    Checks if the LoRA layers are correctly set with peft
    """
    for module in model.modules():
        if isinstance(module, BaseTunerLayer):
            return True
    return False


@require_peft_backend
class PeftLoraLoaderMixinTests:
    pipeline_class = None
    scheduler_cls = None
    scheduler_kwargs = None
    has_two_text_encoders = False
    unet_kwargs = None
    vae_kwargs = None

    def get_dummy_components(self, scheduler_cls=None):
        scheduler_cls = self.scheduler_cls if scheduler_cls is None else LCMScheduler
        rank = 4

        torch.manual_seed(0)
        unet = UNet2DConditionModel(**self.unet_kwargs)

        scheduler = scheduler_cls(**self.scheduler_kwargs)

        torch.manual_seed(0)
        vae = AutoencoderKL(**self.vae_kwargs)

        text_encoder = CLIPTextModel.from_pretrained("peft-internal-testing/tiny-clip-text-2")
        tokenizer = CLIPTokenizer.from_pretrained("peft-internal-testing/tiny-clip-text-2")

        if self.has_two_text_encoders:
            text_encoder_2 = CLIPTextModelWithProjection.from_pretrained("peft-internal-testing/tiny-clip-text-2")
            tokenizer_2 = CLIPTokenizer.from_pretrained("peft-internal-testing/tiny-clip-text-2")

        text_lora_config = LoraConfig(
            r=rank,
            lora_alpha=rank,
            target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
            init_lora_weights=False,
        )

        unet_lora_config = LoraConfig(
            r=rank, lora_alpha=rank, target_modules=["to_q", "to_k", "to_v", "to_out.0"], init_lora_weights=False
        )

        if self.has_two_text_encoders:
            pipeline_components = {
                "unet": unet,
                "scheduler": scheduler,
                "vae": vae,
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "text_encoder_2": text_encoder_2,
                "tokenizer_2": tokenizer_2,
                "image_encoder": None,
                "feature_extractor": None,
            }
        else:
            pipeline_components = {
                "unet": unet,
                "scheduler": scheduler,
                "vae": vae,
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "safety_checker": None,
                "feature_extractor": None,
                "image_encoder": None,
            }

        return pipeline_components, text_lora_config, unet_lora_config

    def get_dummy_inputs(self, with_generator=True):
        batch_size = 1
        sequence_length = 10
        num_channels = 4
        sizes = (32, 32)

        generator = torch.manual_seed(0)
        noise = floats_tensor((batch_size, num_channels) + sizes)
        input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)

        pipeline_inputs = {
            "prompt": "A painting of a squirrel eating a burger",
            "num_inference_steps": 5,
            "guidance_scale": 6.0,
            "output_type": "np",
        }
        if with_generator:
            pipeline_inputs.update({"generator": generator})

        return noise, input_ids, pipeline_inputs

    # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb
    def get_dummy_tokens(self):
        max_seq_length = 77

        inputs = torch.randint(2, 56, size=(1, max_seq_length), generator=torch.manual_seed(0))

        prepared_inputs = {}
        prepared_inputs["input_ids"] = inputs
        return prepared_inputs

    def test_simple_inference(self):
        """
        Tests a simple inference and makes sure it works as expected
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)

            _, _, inputs = self.get_dummy_inputs()
            output_no_lora = pipe(**inputs).images
            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))

    def test_simple_inference_with_text_lora(self):
        """
        Tests a simple inference with lora attached on the text encoder
        and makes sure it works as expected
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))

            pipe.text_encoder.add_adapter(text_lora_config)
            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config)
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            output_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(
                not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output"
            )

    def test_simple_inference_with_text_lora_and_scale(self):
        """
        Tests a simple inference with lora attached on the text encoder + scale argument
        and makes sure it works as expected
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))

            pipe.text_encoder.add_adapter(text_lora_config)
            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config)
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            output_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(
                not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output"
            )

            output_lora_scale = pipe(
                **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.5}
            ).images
            self.assertTrue(
                not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3),
                "Lora + scale should change the output",
            )

            output_lora_0_scale = pipe(
                **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.0}
            ).images
            self.assertTrue(
                np.allclose(output_no_lora, output_lora_0_scale, atol=1e-3, rtol=1e-3),
                "Lora + 0 scale should lead to same result as no LoRA",
            )

    def test_simple_inference_with_text_lora_fused(self):
        """
        Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model
        and makes sure it works as expected
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))

            pipe.text_encoder.add_adapter(text_lora_config)
            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config)
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            pipe.fuse_lora()
            # Fusing should still keep the LoRA layers
            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")

            if self.has_two_text_encoders:
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            ouput_fused = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertFalse(
                np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
            )

    def test_simple_inference_with_text_lora_unloaded(self):
        """
        Tests a simple inference with lora attached to text encoder, then unloads the lora weights
        and makes sure it works as expected
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))

            pipe.text_encoder.add_adapter(text_lora_config)
            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config)
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            pipe.unload_lora_weights()
            # unloading should remove the LoRA layers
            self.assertFalse(
                check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly unloaded in text encoder"
            )

            if self.has_two_text_encoders:
                self.assertFalse(
                    check_if_lora_correctly_set(pipe.text_encoder_2),
                    "Lora not correctly unloaded in text encoder 2",
                )

            ouput_unloaded = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(
                np.allclose(ouput_unloaded, output_no_lora, atol=1e-3, rtol=1e-3),
                "Fused lora should change the output",
            )

    def test_simple_inference_with_text_lora_save_load(self):
        """
        Tests a simple usecase where users could use saving utilities for LoRA.
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))

            pipe.text_encoder.add_adapter(text_lora_config)
            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config)
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            images_lora = pipe(**inputs, generator=torch.manual_seed(0)).images

            with tempfile.TemporaryDirectory() as tmpdirname:
                text_encoder_state_dict = get_peft_model_state_dict(pipe.text_encoder)
                if self.has_two_text_encoders:
                    text_encoder_2_state_dict = get_peft_model_state_dict(pipe.text_encoder_2)

                    self.pipeline_class.save_lora_weights(
                        save_directory=tmpdirname,
                        text_encoder_lora_layers=text_encoder_state_dict,
                        text_encoder_2_lora_layers=text_encoder_2_state_dict,
                        safe_serialization=False,
                    )
                else:
                    self.pipeline_class.save_lora_weights(
                        save_directory=tmpdirname,
                        text_encoder_lora_layers=text_encoder_state_dict,
                        safe_serialization=False,
                    )

                self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin")))
                pipe.unload_lora_weights()

                pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.bin"))

            images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")

            if self.has_two_text_encoders:
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            self.assertTrue(
                np.allclose(images_lora, images_lora_from_pretrained, atol=1e-3, rtol=1e-3),
                "Loading from saved checkpoints should give same results.",
            )

    def test_simple_inference_save_pretrained(self):
        """
        Tests a simple usecase where users could use saving utilities for LoRA through save_pretrained
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, _ = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))

            pipe.text_encoder.add_adapter(text_lora_config)
            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config)
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            images_lora = pipe(**inputs, generator=torch.manual_seed(0)).images

            with tempfile.TemporaryDirectory() as tmpdirname:
                pipe.save_pretrained(tmpdirname)

                pipe_from_pretrained = self.pipeline_class.from_pretrained(tmpdirname)
                pipe_from_pretrained.to(torch_device)

            self.assertTrue(
                check_if_lora_correctly_set(pipe_from_pretrained.text_encoder),
                "Lora not correctly set in text encoder",
            )

            if self.has_two_text_encoders:
                self.assertTrue(
                    check_if_lora_correctly_set(pipe_from_pretrained.text_encoder_2),
                    "Lora not correctly set in text encoder 2",
                )

            images_lora_save_pretrained = pipe_from_pretrained(**inputs, generator=torch.manual_seed(0)).images

            self.assertTrue(
                np.allclose(images_lora, images_lora_save_pretrained, atol=1e-3, rtol=1e-3),
                "Loading from saved checkpoints should give same results.",
            )

    def test_simple_inference_with_text_unet_lora_save_load(self):
        """
        Tests a simple usecase where users could use saving utilities for LoRA for Unet + text encoder
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))

            pipe.text_encoder.add_adapter(text_lora_config)
            pipe.unet.add_adapter(unet_lora_config)

            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config)
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            images_lora = pipe(**inputs, generator=torch.manual_seed(0)).images

            with tempfile.TemporaryDirectory() as tmpdirname:
                text_encoder_state_dict = get_peft_model_state_dict(pipe.text_encoder)
                unet_state_dict = get_peft_model_state_dict(pipe.unet)
                if self.has_two_text_encoders:
                    text_encoder_2_state_dict = get_peft_model_state_dict(pipe.text_encoder_2)

                    self.pipeline_class.save_lora_weights(
                        save_directory=tmpdirname,
                        text_encoder_lora_layers=text_encoder_state_dict,
                        text_encoder_2_lora_layers=text_encoder_2_state_dict,
                        unet_lora_layers=unet_state_dict,
                        safe_serialization=False,
                    )
                else:
                    self.pipeline_class.save_lora_weights(
                        save_directory=tmpdirname,
                        text_encoder_lora_layers=text_encoder_state_dict,
                        unet_lora_layers=unet_state_dict,
                        safe_serialization=False,
                    )

                self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin")))
                pipe.unload_lora_weights()

                pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.bin"))

            images_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")

            if self.has_two_text_encoders:
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            self.assertTrue(
                np.allclose(images_lora, images_lora_from_pretrained, atol=1e-3, rtol=1e-3),
                "Loading from saved checkpoints should give same results.",
            )

    def test_simple_inference_with_text_unet_lora_and_scale(self):
        """
        Tests a simple inference with lora attached on the text encoder + Unet + scale argument
        and makes sure it works as expected
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))

            pipe.text_encoder.add_adapter(text_lora_config)
            pipe.unet.add_adapter(unet_lora_config)
            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config)
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            output_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(
                not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output"
            )

            output_lora_scale = pipe(
                **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.5}
            ).images
            self.assertTrue(
                not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3),
                "Lora + scale should change the output",
            )

            output_lora_0_scale = pipe(
                **inputs, generator=torch.manual_seed(0), cross_attention_kwargs={"scale": 0.0}
            ).images
            self.assertTrue(
                np.allclose(output_no_lora, output_lora_0_scale, atol=1e-3, rtol=1e-3),
                "Lora + 0 scale should lead to same result as no LoRA",
            )

            self.assertTrue(
                pipe.text_encoder.text_model.encoder.layers[0].self_attn.q_proj.scaling["default"] == 1.0,
                "The scaling parameter has not been correctly restored!",
            )

    def test_simple_inference_with_text_lora_unet_fused(self):
        """
        Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model
        and makes sure it works as expected - with unet
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))

            pipe.text_encoder.add_adapter(text_lora_config)
            pipe.unet.add_adapter(unet_lora_config)

            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config)
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            pipe.fuse_lora()
            # Fusing should still keep the LoRA layers
            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in unet")

            if self.has_two_text_encoders:
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            ouput_fused = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertFalse(
                np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
            )

    def test_simple_inference_with_text_unet_lora_unloaded(self):
        """
        Tests a simple inference with lora attached to text encoder and unet, then unloads the lora weights
        and makes sure it works as expected
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))

            pipe.text_encoder.add_adapter(text_lora_config)
            pipe.unet.add_adapter(unet_lora_config)
            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config)
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            pipe.unload_lora_weights()
            # unloading should remove the LoRA layers
            self.assertFalse(
                check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly unloaded in text encoder"
            )
            self.assertFalse(check_if_lora_correctly_set(pipe.unet), "Lora not correctly unloaded in Unet")

            if self.has_two_text_encoders:
                self.assertFalse(
                    check_if_lora_correctly_set(pipe.text_encoder_2),
                    "Lora not correctly unloaded in text encoder 2",
                )

            ouput_unloaded = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(
                np.allclose(ouput_unloaded, output_no_lora, atol=1e-3, rtol=1e-3),
                "Fused lora should change the output",
            )

    def test_simple_inference_with_text_unet_lora_unfused(self):
        """
        Tests a simple inference with lora attached to text encoder and unet, then unloads the lora weights
        and makes sure it works as expected
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            pipe.text_encoder.add_adapter(text_lora_config)
            pipe.unet.add_adapter(unet_lora_config)

            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config)
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            pipe.fuse_lora()

            output_fused_lora = pipe(**inputs, generator=torch.manual_seed(0)).images

            pipe.unfuse_lora()

            output_unfused_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
            # unloading should remove the LoRA layers
            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Unfuse should still keep LoRA layers")
            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Unfuse should still keep LoRA layers")

            if self.has_two_text_encoders:
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Unfuse should still keep LoRA layers"
                )

            # Fuse and unfuse should lead to the same results
            self.assertTrue(
                np.allclose(output_fused_lora, output_unfused_lora, atol=1e-3, rtol=1e-3),
                "Fused lora should change the output",
            )

    def test_simple_inference_with_text_unet_multi_adapter(self):
        """
        Tests a simple inference with lora attached to text encoder and unet, attaches
        multiple adapters and set them
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images

            pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
            pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")

            pipe.unet.add_adapter(unet_lora_config, "adapter-1")
            pipe.unet.add_adapter(unet_lora_config, "adapter-2")

            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
                pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-2")
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            pipe.set_adapters("adapter-1")

            output_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0)).images

            pipe.set_adapters("adapter-2")
            output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0)).images

            pipe.set_adapters(["adapter-1", "adapter-2"])

            output_adapter_mixed = pipe(**inputs, generator=torch.manual_seed(0)).images

            # Fuse and unfuse should lead to the same results
            self.assertFalse(
                np.allclose(output_adapter_1, output_adapter_2, atol=1e-3, rtol=1e-3),
                "Adapter 1 and 2 should give different results",
            )

            self.assertFalse(
                np.allclose(output_adapter_1, output_adapter_mixed, atol=1e-3, rtol=1e-3),
                "Adapter 1 and mixed adapters should give different results",
            )

            self.assertFalse(
                np.allclose(output_adapter_2, output_adapter_mixed, atol=1e-3, rtol=1e-3),
                "Adapter 2 and mixed adapters should give different results",
            )

            pipe.disable_lora()

            output_disabled = pipe(**inputs, generator=torch.manual_seed(0)).images

            self.assertTrue(
                np.allclose(output_no_lora, output_disabled, atol=1e-3, rtol=1e-3),
                "output with no lora and output with lora disabled should give same results",
            )

    def test_simple_inference_with_text_unet_multi_adapter_delete_adapter(self):
        """
        Tests a simple inference with lora attached to text encoder and unet, attaches
        multiple adapters and set/delete them
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images

            pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
            pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")

            pipe.unet.add_adapter(unet_lora_config, "adapter-1")
            pipe.unet.add_adapter(unet_lora_config, "adapter-2")

            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
                pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-2")
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            pipe.set_adapters("adapter-1")

            output_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0)).images

            pipe.set_adapters("adapter-2")
            output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0)).images

            pipe.set_adapters(["adapter-1", "adapter-2"])

            output_adapter_mixed = pipe(**inputs, generator=torch.manual_seed(0)).images

            self.assertFalse(
                np.allclose(output_adapter_1, output_adapter_2, atol=1e-3, rtol=1e-3),
                "Adapter 1 and 2 should give different results",
            )

            self.assertFalse(
                np.allclose(output_adapter_1, output_adapter_mixed, atol=1e-3, rtol=1e-3),
                "Adapter 1 and mixed adapters should give different results",
            )

            self.assertFalse(
                np.allclose(output_adapter_2, output_adapter_mixed, atol=1e-3, rtol=1e-3),
                "Adapter 2 and mixed adapters should give different results",
            )

            pipe.delete_adapters("adapter-1")
            output_deleted_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0)).images

            self.assertTrue(
                np.allclose(output_deleted_adapter_1, output_adapter_2, atol=1e-3, rtol=1e-3),
                "Adapter 1 and 2 should give different results",
            )

            pipe.delete_adapters("adapter-2")
            output_deleted_adapters = pipe(**inputs, generator=torch.manual_seed(0)).images

            self.assertTrue(
                np.allclose(output_no_lora, output_deleted_adapters, atol=1e-3, rtol=1e-3),
                "output with no lora and output with lora disabled should give same results",
            )

            pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
            pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")

            pipe.unet.add_adapter(unet_lora_config, "adapter-1")
            pipe.unet.add_adapter(unet_lora_config, "adapter-2")

            pipe.set_adapters(["adapter-1", "adapter-2"])
            pipe.delete_adapters(["adapter-1", "adapter-2"])

            output_deleted_adapters = pipe(**inputs, generator=torch.manual_seed(0)).images

            self.assertTrue(
                np.allclose(output_no_lora, output_deleted_adapters, atol=1e-3, rtol=1e-3),
                "output with no lora and output with lora disabled should give same results",
            )

    def test_simple_inference_with_text_unet_multi_adapter_weighted(self):
        """
        Tests a simple inference with lora attached to text encoder and unet, attaches
        multiple adapters and set them
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images

            pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
            pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")

            pipe.unet.add_adapter(unet_lora_config, "adapter-1")
            pipe.unet.add_adapter(unet_lora_config, "adapter-2")

            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
                pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-2")
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            pipe.set_adapters("adapter-1")

            output_adapter_1 = pipe(**inputs, generator=torch.manual_seed(0)).images

            pipe.set_adapters("adapter-2")
            output_adapter_2 = pipe(**inputs, generator=torch.manual_seed(0)).images

            pipe.set_adapters(["adapter-1", "adapter-2"])

            output_adapter_mixed = pipe(**inputs, generator=torch.manual_seed(0)).images

            # Fuse and unfuse should lead to the same results
            self.assertFalse(
                np.allclose(output_adapter_1, output_adapter_2, atol=1e-3, rtol=1e-3),
                "Adapter 1 and 2 should give different results",
            )

            self.assertFalse(
                np.allclose(output_adapter_1, output_adapter_mixed, atol=1e-3, rtol=1e-3),
                "Adapter 1 and mixed adapters should give different results",
            )

            self.assertFalse(
                np.allclose(output_adapter_2, output_adapter_mixed, atol=1e-3, rtol=1e-3),
                "Adapter 2 and mixed adapters should give different results",
            )

            pipe.set_adapters(["adapter-1", "adapter-2"], [0.5, 0.6])
            output_adapter_mixed_weighted = pipe(**inputs, generator=torch.manual_seed(0)).images

            self.assertFalse(
                np.allclose(output_adapter_mixed_weighted, output_adapter_mixed, atol=1e-3, rtol=1e-3),
                "Weighted adapter and mixed adapter should give different results",
            )

            pipe.disable_lora()

            output_disabled = pipe(**inputs, generator=torch.manual_seed(0)).images

            self.assertTrue(
                np.allclose(output_no_lora, output_disabled, atol=1e-3, rtol=1e-3),
                "output with no lora and output with lora disabled should give same results",
            )

    def test_lora_fuse_nan(self):
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")

            pipe.unet.add_adapter(unet_lora_config, "adapter-1")

            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")

            # corrupt one LoRA weight with `inf` values
            with torch.no_grad():
                pipe.unet.mid_block.attentions[0].transformer_blocks[0].attn1.to_q.lora_A["adapter-1"].weight += float(
                    "inf"
                )

            # with `safe_fusing=True` we should see an Error
            with self.assertRaises(ValueError):
                pipe.fuse_lora(safe_fusing=True)

            # without we should not see an error, but every image will be black
            pipe.fuse_lora(safe_fusing=False)

            out = pipe("test", num_inference_steps=2, output_type="np").images

            self.assertTrue(np.isnan(out).all())

    def test_get_adapters(self):
        """
        Tests a simple usecase where we attach multiple adapters and check if the results
        are the expected results
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
            pipe.unet.add_adapter(unet_lora_config, "adapter-1")

            adapter_names = pipe.get_active_adapters()
            self.assertListEqual(adapter_names, ["adapter-1"])

            pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
            pipe.unet.add_adapter(unet_lora_config, "adapter-2")

            adapter_names = pipe.get_active_adapters()
            self.assertListEqual(adapter_names, ["adapter-2"])

            pipe.set_adapters(["adapter-1", "adapter-2"])
            self.assertListEqual(pipe.get_active_adapters(), ["adapter-1", "adapter-2"])

    def test_get_list_adapters(self):
        """
        Tests a simple usecase where we attach multiple adapters and check if the results
        are the expected results
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)

            pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
            pipe.unet.add_adapter(unet_lora_config, "adapter-1")

            adapter_names = pipe.get_list_adapters()
            self.assertDictEqual(adapter_names, {"text_encoder": ["adapter-1"], "unet": ["adapter-1"]})

            pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
            pipe.unet.add_adapter(unet_lora_config, "adapter-2")

            adapter_names = pipe.get_list_adapters()
            self.assertDictEqual(
                adapter_names, {"text_encoder": ["adapter-1", "adapter-2"], "unet": ["adapter-1", "adapter-2"]}
            )

            pipe.set_adapters(["adapter-1", "adapter-2"])
            self.assertDictEqual(
                pipe.get_list_adapters(),
                {"unet": ["adapter-1", "adapter-2"], "text_encoder": ["adapter-1", "adapter-2"]},
            )

            pipe.unet.add_adapter(unet_lora_config, "adapter-3")
            self.assertDictEqual(
                pipe.get_list_adapters(),
                {"unet": ["adapter-1", "adapter-2", "adapter-3"], "text_encoder": ["adapter-1", "adapter-2"]},
            )

    @require_peft_version_greater(peft_version="0.6.2")
    def test_simple_inference_with_text_lora_unet_fused_multi(self):
        """
        Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model
        and makes sure it works as expected - with unet and multi-adapter case
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(output_no_lora.shape == (1, 64, 64, 3))

            pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
            pipe.unet.add_adapter(unet_lora_config, "adapter-1")

            # Attach a second adapter
            pipe.text_encoder.add_adapter(text_lora_config, "adapter-2")
            pipe.unet.add_adapter(unet_lora_config, "adapter-2")

            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-1")
                pipe.text_encoder_2.add_adapter(text_lora_config, "adapter-2")
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            # set them to multi-adapter inference mode
            pipe.set_adapters(["adapter-1", "adapter-2"])
            ouputs_all_lora = pipe(**inputs, generator=torch.manual_seed(0)).images

            pipe.set_adapters(["adapter-1"])
            ouputs_lora_1 = pipe(**inputs, generator=torch.manual_seed(0)).images

            pipe.fuse_lora(adapter_names=["adapter-1"])

            # Fusing should still keep the LoRA layers so outpout should remain the same
            outputs_lora_1_fused = pipe(**inputs, generator=torch.manual_seed(0)).images

            self.assertTrue(
                np.allclose(ouputs_lora_1, outputs_lora_1_fused, atol=1e-3, rtol=1e-3),
                "Fused lora should not change the output",
            )

            pipe.unfuse_lora()
            pipe.fuse_lora(adapter_names=["adapter-2", "adapter-1"])

            # Fusing should still keep the LoRA layers
            output_all_lora_fused = pipe(**inputs, generator=torch.manual_seed(0)).images
            self.assertTrue(
                np.allclose(output_all_lora_fused, ouputs_all_lora, atol=1e-3, rtol=1e-3),
                "Fused lora should not change the output",
            )

    @unittest.skip("This is failing for now - need to investigate")
    def test_simple_inference_with_text_unet_lora_unfused_torch_compile(self):
        """
        Tests a simple inference with lora attached to text encoder and unet, then unloads the lora weights
        and makes sure it works as expected
        """
        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, text_lora_config, unet_lora_config = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            pipe.text_encoder.add_adapter(text_lora_config)
            pipe.unet.add_adapter(unet_lora_config)

            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
            self.assertTrue(check_if_lora_correctly_set(pipe.unet), "Lora not correctly set in Unet")

            if self.has_two_text_encoders:
                pipe.text_encoder_2.add_adapter(text_lora_config)
                self.assertTrue(
                    check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                )

            pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
            pipe.text_encoder = torch.compile(pipe.text_encoder, mode="reduce-overhead", fullgraph=True)

            if self.has_two_text_encoders:
                pipe.text_encoder_2 = torch.compile(pipe.text_encoder_2, mode="reduce-overhead", fullgraph=True)

            # Just makes sure it works..
            _ = pipe(**inputs, generator=torch.manual_seed(0)).images

    def test_modify_padding_mode(self):
        def set_pad_mode(network, mode="circular"):
            for _, module in network.named_modules():
                if isinstance(module, torch.nn.Conv2d):
                    module.padding_mode = mode

        for scheduler_cls in [DDIMScheduler, LCMScheduler]:
            components, _, _ = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _pad_mode = "circular"
            set_pad_mode(pipe.vae, _pad_mode)
            set_pad_mode(pipe.unet, _pad_mode)

            _, _, inputs = self.get_dummy_inputs()
            _ = pipe(**inputs).images
