# coding=utf-8
# Copyright 2022 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 json
import os
import random
import shutil
import tempfile
import unittest
from functools import partial

import numpy as np
import torch

import PIL
from diffusers import (
    AutoencoderKL,
    DDIMPipeline,
    DDIMScheduler,
    DDPMPipeline,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImg2ImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNet2DConditionModel,
    UNet2DModel,
    logging,
)
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir, require_torch_gpu
from parameterized import parameterized
from PIL import Image
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer


torch.backends.cuda.matmul.allow_tf32 = False


def test_progress_bar(capsys):
    model = UNet2DModel(
        block_out_channels=(32, 64),
        layers_per_block=2,
        sample_size=32,
        in_channels=3,
        out_channels=3,
        down_block_types=("DownBlock2D", "AttnDownBlock2D"),
        up_block_types=("AttnUpBlock2D", "UpBlock2D"),
    )
    scheduler = DDPMScheduler(num_train_timesteps=10)

    ddpm = DDPMPipeline(model, scheduler).to(torch_device)
    ddpm(output_type="numpy").images
    captured = capsys.readouterr()
    assert "10/10" in captured.err, "Progress bar has to be displayed"

    ddpm.set_progress_bar_config(disable=True)
    ddpm(output_type="numpy").images
    captured = capsys.readouterr()
    assert captured.err == "", "Progress bar should be disabled"


class DownloadTests(unittest.TestCase):
    def test_download_only_pytorch(self):
        with tempfile.TemporaryDirectory() as tmpdirname:
            # pipeline has Flax weights
            _ = DiffusionPipeline.from_pretrained(
                "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname
            )

            all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname, os.listdir(tmpdirname)[0], "snapshots"))]
            files = [item for sublist in all_root_files for item in sublist]

            # None of the downloaded files should be a flax file even if we have some here:
            # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack
            assert not any(f.endswith(".msgpack") for f in files)

    def test_download_no_safety_checker(self):
        prompt = "hello"
        pipe = StableDiffusionPipeline.from_pretrained(
            "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
        )
        pipe = pipe.to(torch_device)
        if torch_device == "mps":
            # device type MPS is not supported for torch.Generator() api.
            generator = torch.manual_seed(0)
        else:
            generator = torch.Generator(device=torch_device).manual_seed(0)
        out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images

        pipe_2 = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
        pipe_2 = pipe_2.to(torch_device)
        if torch_device == "mps":
            # device type MPS is not supported for torch.Generator() api.
            generator = torch.manual_seed(0)
        else:
            generator = torch.Generator(device=torch_device).manual_seed(0)
        out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images

        assert np.max(np.abs(out - out_2)) < 1e-3

    def test_load_no_safety_checker_explicit_locally(self):
        prompt = "hello"
        pipe = StableDiffusionPipeline.from_pretrained(
            "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None
        )
        pipe = pipe.to(torch_device)
        if torch_device == "mps":
            # device type MPS is not supported for torch.Generator() api.
            generator = torch.manual_seed(0)
        else:
            generator = torch.Generator(device=torch_device).manual_seed(0)
        out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images

        with tempfile.TemporaryDirectory() as tmpdirname:
            pipe.save_pretrained(tmpdirname)
            pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname, safety_checker=None)
            pipe_2 = pipe_2.to(torch_device)

            if torch_device == "mps":
                # device type MPS is not supported for torch.Generator() api.
                generator = torch.manual_seed(0)
            else:
                generator = torch.Generator(device=torch_device).manual_seed(0)

            out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images

        assert np.max(np.abs(out - out_2)) < 1e-3

    def test_load_no_safety_checker_default_locally(self):
        prompt = "hello"
        pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
        pipe = pipe.to(torch_device)
        if torch_device == "mps":
            # device type MPS is not supported for torch.Generator() api.
            generator = torch.manual_seed(0)
        else:
            generator = torch.Generator(device=torch_device).manual_seed(0)
        out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images

        with tempfile.TemporaryDirectory() as tmpdirname:
            pipe.save_pretrained(tmpdirname)
            pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname)
            pipe_2 = pipe_2.to(torch_device)

            if torch_device == "mps":
                # device type MPS is not supported for torch.Generator() api.
                generator = torch.manual_seed(0)
            else:
                generator = torch.Generator(device=torch_device).manual_seed(0)

            out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images

        assert np.max(np.abs(out - out_2)) < 1e-3


class CustomPipelineTests(unittest.TestCase):
    def test_load_custom_pipeline(self):
        pipeline = DiffusionPipeline.from_pretrained(
            "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
        )
        pipeline = pipeline.to(torch_device)
        # NOTE that `"CustomPipeline"` is not a class that is defined in this library, but solely on the Hub
        # under https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L24
        assert pipeline.__class__.__name__ == "CustomPipeline"

    def test_run_custom_pipeline(self):
        pipeline = DiffusionPipeline.from_pretrained(
            "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline"
        )
        pipeline = pipeline.to(torch_device)
        images, output_str = pipeline(num_inference_steps=2, output_type="np")

        assert images[0].shape == (1, 32, 32, 3)

        # compare output to https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L102
        assert output_str == "This is a test"

    def test_local_custom_pipeline_repo(self):
        local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline")
        pipeline = DiffusionPipeline.from_pretrained(
            "google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path
        )
        pipeline = pipeline.to(torch_device)
        images, output_str = pipeline(num_inference_steps=2, output_type="np")

        assert pipeline.__class__.__name__ == "CustomLocalPipeline"
        assert images[0].shape == (1, 32, 32, 3)
        # compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102
        assert output_str == "This is a local test"

    def test_local_custom_pipeline_file(self):
        local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline")
        local_custom_pipeline_path = os.path.join(local_custom_pipeline_path, "what_ever.py")
        pipeline = DiffusionPipeline.from_pretrained(
            "google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path
        )
        pipeline = pipeline.to(torch_device)
        images, output_str = pipeline(num_inference_steps=2, output_type="np")

        assert pipeline.__class__.__name__ == "CustomLocalPipeline"
        assert images[0].shape == (1, 32, 32, 3)
        # compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102
        assert output_str == "This is a local test"

    @slow
    @require_torch_gpu
    def test_load_pipeline_from_git(self):
        clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"

        feature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id)
        clip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16)

        pipeline = DiffusionPipeline.from_pretrained(
            "CompVis/stable-diffusion-v1-4",
            custom_pipeline="clip_guided_stable_diffusion",
            clip_model=clip_model,
            feature_extractor=feature_extractor,
            torch_dtype=torch.float16,
            revision="fp16",
        )
        pipeline.enable_attention_slicing()
        pipeline = pipeline.to(torch_device)

        # NOTE that `"CLIPGuidedStableDiffusion"` is not a class that is defined in the pypi package of th e library, but solely on the community examples folder of GitHub under:
        # https://github.com/huggingface/diffusers/blob/main/examples/community/clip_guided_stable_diffusion.py
        assert pipeline.__class__.__name__ == "CLIPGuidedStableDiffusion"

        image = pipeline("a prompt", num_inference_steps=2, output_type="np").images[0]
        assert image.shape == (512, 512, 3)


class PipelineFastTests(unittest.TestCase):
    def dummy_image(self):
        batch_size = 1
        num_channels = 3
        sizes = (32, 32)

        image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device)
        return image

    def dummy_uncond_unet(self, sample_size=32):
        torch.manual_seed(0)
        model = UNet2DModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            sample_size=sample_size,
            in_channels=3,
            out_channels=3,
            down_block_types=("DownBlock2D", "AttnDownBlock2D"),
            up_block_types=("AttnUpBlock2D", "UpBlock2D"),
        )
        return model

    def dummy_cond_unet(self, sample_size=32):
        torch.manual_seed(0)
        model = UNet2DConditionModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            sample_size=sample_size,
            in_channels=4,
            out_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
            cross_attention_dim=32,
        )
        return model

    @property
    def dummy_vae(self):
        torch.manual_seed(0)
        model = AutoencoderKL(
            block_out_channels=[32, 64],
            in_channels=3,
            out_channels=3,
            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
            latent_channels=4,
        )
        return model

    @property
    def dummy_text_encoder(self):
        torch.manual_seed(0)
        config = CLIPTextConfig(
            bos_token_id=0,
            eos_token_id=2,
            hidden_size=32,
            intermediate_size=37,
            layer_norm_eps=1e-05,
            num_attention_heads=4,
            num_hidden_layers=5,
            pad_token_id=1,
            vocab_size=1000,
        )
        return CLIPTextModel(config)

    @property
    def dummy_extractor(self):
        def extract(*args, **kwargs):
            class Out:
                def __init__(self):
                    self.pixel_values = torch.ones([0])

                def to(self, device):
                    self.pixel_values.to(device)
                    return self

            return Out()

        return extract

    @parameterized.expand(
        [
            [DDIMScheduler, DDIMPipeline, 32],
            [partial(DDPMScheduler, predict_epsilon=True), DDPMPipeline, 32],
            [DDIMScheduler, DDIMPipeline, (32, 64)],
            [partial(DDPMScheduler, predict_epsilon=True), DDPMPipeline, (64, 32)],
        ]
    )
    def test_uncond_unet_components(self, scheduler_fn=DDPMScheduler, pipeline_fn=DDPMPipeline, sample_size=32):
        unet = self.dummy_uncond_unet(sample_size)
        # DDIM doesn't take `predict_epsilon`, and DDPM requires it -- so using partial in parameterized decorator
        scheduler = scheduler_fn()
        pipeline = pipeline_fn(unet, scheduler).to(torch_device)

        # Device type MPS is not supported for torch.Generator() api.
        if torch_device == "mps":
            generator = torch.manual_seed(0)
        else:
            generator = torch.Generator(device=torch_device).manual_seed(0)

        out_image = pipeline(
            generator=generator,
            num_inference_steps=2,
            output_type="np",
        ).images
        sample_size = (sample_size, sample_size) if isinstance(sample_size, int) else sample_size
        assert out_image.shape == (1, *sample_size, 3)

    def test_stable_diffusion_components(self):
        """Test that components property works correctly"""
        unet = self.dummy_cond_unet()
        scheduler = PNDMScheduler(skip_prk_steps=True)
        vae = self.dummy_vae
        bert = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        image = self.dummy_image().cpu().permute(0, 2, 3, 1)[0]
        init_image = Image.fromarray(np.uint8(image)).convert("RGB")
        mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))

        # make sure here that pndm scheduler skips prk
        inpaint = StableDiffusionInpaintPipelineLegacy(
            unet=unet,
            scheduler=scheduler,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=None,
            feature_extractor=self.dummy_extractor,
        ).to(torch_device)
        img2img = StableDiffusionImg2ImgPipeline(**inpaint.components).to(torch_device)
        text2img = StableDiffusionPipeline(**inpaint.components).to(torch_device)

        prompt = "A painting of a squirrel eating a burger"

        # Device type MPS is not supported for torch.Generator() api.
        if torch_device == "mps":
            generator = torch.manual_seed(0)
        else:
            generator = torch.Generator(device=torch_device).manual_seed(0)

        image_inpaint = inpaint(
            [prompt],
            generator=generator,
            num_inference_steps=2,
            output_type="np",
            init_image=init_image,
            mask_image=mask_image,
        ).images
        image_img2img = img2img(
            [prompt],
            generator=generator,
            num_inference_steps=2,
            output_type="np",
            init_image=init_image,
        ).images
        image_text2img = text2img(
            [prompt],
            generator=generator,
            num_inference_steps=2,
            output_type="np",
        ).images

        assert image_inpaint.shape == (1, 32, 32, 3)
        assert image_img2img.shape == (1, 32, 32, 3)
        assert image_text2img.shape == (1, 64, 64, 3)

    def test_set_scheduler(self):
        unet = self.dummy_cond_unet()
        scheduler = PNDMScheduler(skip_prk_steps=True)
        vae = self.dummy_vae
        bert = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        sd = StableDiffusionPipeline(
            unet=unet,
            scheduler=scheduler,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=None,
            feature_extractor=self.dummy_extractor,
        )

        sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config)
        assert isinstance(sd.scheduler, DDIMScheduler)
        sd.scheduler = DDPMScheduler.from_config(sd.scheduler.config)
        assert isinstance(sd.scheduler, DDPMScheduler)
        sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config)
        assert isinstance(sd.scheduler, PNDMScheduler)
        sd.scheduler = LMSDiscreteScheduler.from_config(sd.scheduler.config)
        assert isinstance(sd.scheduler, LMSDiscreteScheduler)
        sd.scheduler = EulerDiscreteScheduler.from_config(sd.scheduler.config)
        assert isinstance(sd.scheduler, EulerDiscreteScheduler)
        sd.scheduler = EulerAncestralDiscreteScheduler.from_config(sd.scheduler.config)
        assert isinstance(sd.scheduler, EulerAncestralDiscreteScheduler)
        sd.scheduler = DPMSolverMultistepScheduler.from_config(sd.scheduler.config)
        assert isinstance(sd.scheduler, DPMSolverMultistepScheduler)

    def test_set_scheduler_consistency(self):
        unet = self.dummy_cond_unet()
        pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
        ddim = DDIMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
        vae = self.dummy_vae
        bert = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        sd = StableDiffusionPipeline(
            unet=unet,
            scheduler=pndm,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=None,
            feature_extractor=self.dummy_extractor,
        )

        pndm_config = sd.scheduler.config
        sd.scheduler = DDPMScheduler.from_config(pndm_config)
        sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config)
        pndm_config_2 = sd.scheduler.config
        pndm_config_2 = {k: v for k, v in pndm_config_2.items() if k in pndm_config}

        assert dict(pndm_config) == dict(pndm_config_2)

        sd = StableDiffusionPipeline(
            unet=unet,
            scheduler=ddim,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=None,
            feature_extractor=self.dummy_extractor,
        )

        ddim_config = sd.scheduler.config
        sd.scheduler = LMSDiscreteScheduler.from_config(ddim_config)
        sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config)
        ddim_config_2 = sd.scheduler.config
        ddim_config_2 = {k: v for k, v in ddim_config_2.items() if k in ddim_config}

        assert dict(ddim_config) == dict(ddim_config_2)

    def test_optional_components(self):
        unet = self.dummy_cond_unet()
        pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")
        vae = self.dummy_vae
        bert = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        orig_sd = StableDiffusionPipeline(
            unet=unet,
            scheduler=pndm,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=unet,
            feature_extractor=self.dummy_extractor,
        )
        sd = orig_sd

        assert sd.config.requires_safety_checker is True

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

            # Test that passing None works
            sd = StableDiffusionPipeline.from_pretrained(
                tmpdirname, feature_extractor=None, safety_checker=None, requires_safety_checker=False
            )

            assert sd.config.requires_safety_checker is False
            assert sd.config.safety_checker == (None, None)
            assert sd.config.feature_extractor == (None, None)

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

            # Test that loading previous None works
            sd = StableDiffusionPipeline.from_pretrained(tmpdirname)

            assert sd.config.requires_safety_checker is False
            assert sd.config.safety_checker == (None, None)
            assert sd.config.feature_extractor == (None, None)

            orig_sd.save_pretrained(tmpdirname)

            # Test that loading without any directory works
            shutil.rmtree(os.path.join(tmpdirname, "safety_checker"))
            with open(os.path.join(tmpdirname, sd.config_name)) as f:
                config = json.load(f)
                config["safety_checker"] = [None, None]
            with open(os.path.join(tmpdirname, sd.config_name), "w") as f:
                json.dump(config, f)

            sd = StableDiffusionPipeline.from_pretrained(tmpdirname, requires_safety_checker=False)
            sd.save_pretrained(tmpdirname)
            sd = StableDiffusionPipeline.from_pretrained(tmpdirname)

            assert sd.config.requires_safety_checker is False
            assert sd.config.safety_checker == (None, None)
            assert sd.config.feature_extractor == (None, None)

            # Test that loading from deleted model index works
            with open(os.path.join(tmpdirname, sd.config_name)) as f:
                config = json.load(f)
                del config["safety_checker"]
                del config["feature_extractor"]
            with open(os.path.join(tmpdirname, sd.config_name), "w") as f:
                json.dump(config, f)

            sd = StableDiffusionPipeline.from_pretrained(tmpdirname)

            assert sd.config.requires_safety_checker is False
            assert sd.config.safety_checker == (None, None)
            assert sd.config.feature_extractor == (None, None)

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

            # Test that partially loading works
            sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor)

            assert sd.config.requires_safety_checker is False
            assert sd.config.safety_checker == (None, None)
            assert sd.config.feature_extractor != (None, None)

            # Test that partially loading works
            sd = StableDiffusionPipeline.from_pretrained(
                tmpdirname,
                feature_extractor=self.dummy_extractor,
                safety_checker=unet,
                requires_safety_checker=[True, True],
            )

            assert sd.config.requires_safety_checker == [True, True]
            assert sd.config.safety_checker != (None, None)
            assert sd.config.feature_extractor != (None, None)

        with tempfile.TemporaryDirectory() as tmpdirname:
            sd.save_pretrained(tmpdirname)
            sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor)

            assert sd.config.requires_safety_checker == [True, True]
            assert sd.config.safety_checker != (None, None)
            assert sd.config.feature_extractor != (None, None)


@slow
class PipelineSlowTests(unittest.TestCase):
    def tearDown(self):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

    def test_smart_download(self):
        model_id = "hf-internal-testing/unet-pipeline-dummy"
        with tempfile.TemporaryDirectory() as tmpdirname:
            _ = DiffusionPipeline.from_pretrained(model_id, cache_dir=tmpdirname, force_download=True)
            local_repo_name = "--".join(["models"] + model_id.split("/"))
            snapshot_dir = os.path.join(tmpdirname, local_repo_name, "snapshots")
            snapshot_dir = os.path.join(snapshot_dir, os.listdir(snapshot_dir)[0])

            # inspect all downloaded files to make sure that everything is included
            assert os.path.isfile(os.path.join(snapshot_dir, DiffusionPipeline.config_name))
            assert os.path.isfile(os.path.join(snapshot_dir, CONFIG_NAME))
            assert os.path.isfile(os.path.join(snapshot_dir, SCHEDULER_CONFIG_NAME))
            assert os.path.isfile(os.path.join(snapshot_dir, WEIGHTS_NAME))
            assert os.path.isfile(os.path.join(snapshot_dir, "scheduler", SCHEDULER_CONFIG_NAME))
            assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME))
            assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME))
            # let's make sure the super large numpy file:
            # https://huggingface.co/hf-internal-testing/unet-pipeline-dummy/blob/main/big_array.npy
            # is not downloaded, but all the expected ones
            assert not os.path.isfile(os.path.join(snapshot_dir, "big_array.npy"))

    def test_warning_unused_kwargs(self):
        model_id = "hf-internal-testing/unet-pipeline-dummy"
        logger = logging.get_logger("diffusers.pipeline_utils")
        with tempfile.TemporaryDirectory() as tmpdirname:
            with CaptureLogger(logger) as cap_logger:
                DiffusionPipeline.from_pretrained(
                    model_id,
                    not_used=True,
                    cache_dir=tmpdirname,
                    force_download=True,
                )

        assert cap_logger.out == "Keyword arguments {'not_used': True} not recognized.\n"

    def test_from_pretrained_save_pretrained(self):
        # 1. Load models
        model = UNet2DModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            sample_size=32,
            in_channels=3,
            out_channels=3,
            down_block_types=("DownBlock2D", "AttnDownBlock2D"),
            up_block_types=("AttnUpBlock2D", "UpBlock2D"),
        )
        schedular = DDPMScheduler(num_train_timesteps=10)

        ddpm = DDPMPipeline(model, schedular)
        ddpm.to(torch_device)
        ddpm.set_progress_bar_config(disable=None)

        with tempfile.TemporaryDirectory() as tmpdirname:
            ddpm.save_pretrained(tmpdirname)
            new_ddpm = DDPMPipeline.from_pretrained(tmpdirname)
            new_ddpm.to(torch_device)

        generator = torch.Generator(device=torch_device).manual_seed(0)
        image = ddpm(generator=generator, output_type="numpy").images

        generator = generator.manual_seed(0)
        new_image = new_ddpm(generator=generator, output_type="numpy").images

        assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"

    def test_from_pretrained_hub(self):
        model_path = "google/ddpm-cifar10-32"

        scheduler = DDPMScheduler(num_train_timesteps=10)

        ddpm = DDPMPipeline.from_pretrained(model_path, scheduler=scheduler)
        ddpm = ddpm.to(torch_device)
        ddpm.set_progress_bar_config(disable=None)

        ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)
        ddpm_from_hub = ddpm_from_hub.to(torch_device)
        ddpm_from_hub.set_progress_bar_config(disable=None)

        generator = torch.Generator(device=torch_device).manual_seed(0)
        image = ddpm(generator=generator, output_type="numpy").images

        generator = generator.manual_seed(0)
        new_image = ddpm_from_hub(generator=generator, output_type="numpy").images

        assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"

    def test_from_pretrained_hub_pass_model(self):
        model_path = "google/ddpm-cifar10-32"

        scheduler = DDPMScheduler(num_train_timesteps=10)

        # pass unet into DiffusionPipeline
        unet = UNet2DModel.from_pretrained(model_path)
        ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(model_path, unet=unet, scheduler=scheduler)
        ddpm_from_hub_custom_model = ddpm_from_hub_custom_model.to(torch_device)
        ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)

        ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler)
        ddpm_from_hub = ddpm_from_hub.to(torch_device)
        ddpm_from_hub_custom_model.set_progress_bar_config(disable=None)

        generator = torch.Generator(device=torch_device).manual_seed(0)
        image = ddpm_from_hub_custom_model(generator=generator, output_type="numpy").images

        generator = generator.manual_seed(0)
        new_image = ddpm_from_hub(generator=generator, output_type="numpy").images

        assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"

    def test_output_format(self):
        model_path = "google/ddpm-cifar10-32"

        scheduler = DDIMScheduler.from_pretrained(model_path)
        pipe = DDIMPipeline.from_pretrained(model_path, scheduler=scheduler)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        generator = torch.Generator(device=torch_device).manual_seed(0)
        images = pipe(generator=generator, output_type="numpy").images
        assert images.shape == (1, 32, 32, 3)
        assert isinstance(images, np.ndarray)

        images = pipe(generator=generator, output_type="pil", num_inference_steps=4).images
        assert isinstance(images, list)
        assert len(images) == 1
        assert isinstance(images[0], PIL.Image.Image)

        # use PIL by default
        images = pipe(generator=generator, num_inference_steps=4).images
        assert isinstance(images, list)
        assert isinstance(images[0], PIL.Image.Image)

    def test_ddpm_ddim_equality_batched(self):
        seed = 0
        model_id = "google/ddpm-cifar10-32"

        unet = UNet2DModel.from_pretrained(model_id)
        ddpm_scheduler = DDPMScheduler()
        ddim_scheduler = DDIMScheduler()

        ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
        ddpm.to(torch_device)
        ddpm.set_progress_bar_config(disable=None)

        ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
        ddim.to(torch_device)
        ddim.set_progress_bar_config(disable=None)

        generator = torch.Generator(device=torch_device).manual_seed(seed)
        ddpm_images = ddpm(batch_size=2, generator=generator, output_type="numpy").images

        generator = torch.Generator(device=torch_device).manual_seed(seed)
        ddim_images = ddim(
            batch_size=2,
            generator=generator,
            num_inference_steps=1000,
            eta=1.0,
            output_type="numpy",
            use_clipped_model_output=True,  # Need this to make DDIM match DDPM
        ).images

        # the values aren't exactly equal, but the images look the same visually
        assert np.abs(ddpm_images - ddim_images).max() < 1e-1
