import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


torch.backends.cuda.matmul.allow_tf32 = False


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

    @property
    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

    @property
    def dummy_cond_unet(self):
        torch.manual_seed(0)
        model = UNet2DConditionModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
            sample_size=32,
            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

    def test_semantic_diffusion_ddim(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unet = self.dummy_cond_unet
        scheduler = DDIMScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_one=False,
        )

        vae = self.dummy_vae
        bert = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionPipeline(
            unet=unet,
            scheduler=scheduler,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=None,
            feature_extractor=self.dummy_extractor,
        )
        sd_pipe = sd_pipe.to(device)
        sd_pipe.set_progress_bar_config(disable=None)

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

        generator = torch.Generator(device=device).manual_seed(0)
        output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
        image = output.images

        generator = torch.Generator(device=device).manual_seed(0)
        image_from_tuple = sd_pipe(
            [prompt],
            generator=generator,
            guidance_scale=6.0,
            num_inference_steps=2,
            output_type="np",
            return_dict=False,
        )[0]

        image_slice = image[0, -3:, -3:, -1]
        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        expected_slice = np.array([0.5644, 0.6018, 0.4799, 0.5267, 0.5585, 0.4641, 0.516, 0.4964, 0.4792])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2

    def test_semantic_diffusion_pndm(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        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")

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionPipeline(
            unet=unet,
            scheduler=scheduler,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=None,
            feature_extractor=self.dummy_extractor,
        )
        sd_pipe = sd_pipe.to(device)
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "A painting of a squirrel eating a burger"
        generator = torch.Generator(device=device).manual_seed(0)
        output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")

        image = output.images

        generator = torch.Generator(device=device).manual_seed(0)
        image_from_tuple = sd_pipe(
            [prompt],
            generator=generator,
            guidance_scale=6.0,
            num_inference_steps=2,
            output_type="np",
            return_dict=False,
        )[0]

        image_slice = image[0, -3:, -3:, -1]
        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        expected_slice = np.array([0.5095, 0.5674, 0.4668, 0.5126, 0.5697, 0.4675, 0.5278, 0.4964, 0.4945])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2

    def test_semantic_diffusion_no_safety_checker(self):
        pipe = StableDiffusionPipeline.from_pretrained(
            "hf-internal-testing/tiny-stable-diffusion-lms-pipe", safety_checker=None
        )
        assert isinstance(pipe, StableDiffusionPipeline)
        assert isinstance(pipe.scheduler, LMSDiscreteScheduler)
        assert pipe.safety_checker is None

        image = pipe("example prompt", num_inference_steps=2).images[0]
        assert image is not None

        # check that there's no error when saving a pipeline with one of the models being None
        with tempfile.TemporaryDirectory() as tmpdirname:
            pipe.save_pretrained(tmpdirname)
            pipe = StableDiffusionPipeline.from_pretrained(tmpdirname)

        # sanity check that the pipeline still works
        assert pipe.safety_checker is None
        image = pipe("example prompt", num_inference_steps=2).images[0]
        assert image is not None

    @unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
    def test_semantic_diffusion_fp16(self):
        """Test that stable diffusion works with fp16"""
        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")

        # put models in fp16
        unet = unet.half()
        vae = vae.half()
        bert = bert.half()

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionPipeline(
            unet=unet,
            scheduler=scheduler,
            vae=vae,
            text_encoder=bert,
            tokenizer=tokenizer,
            safety_checker=None,
            feature_extractor=self.dummy_extractor,
        )
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "A painting of a squirrel eating a burger"
        image = sd_pipe([prompt], num_inference_steps=2, output_type="np").images

        assert image.shape == (1, 64, 64, 3)


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

    def test_positive_guidance(self):
        torch_device = "cuda"
        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
        pipe = pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        prompt = "a photo of a cat"
        edit = {
            "editing_prompt": ["sunglasses"],
            "reverse_editing_direction": [False],
            "edit_warmup_steps": 10,
            "edit_guidance_scale": 6,
            "edit_threshold": 0.95,
            "edit_momentum_scale": 0.5,
            "edit_mom_beta": 0.6,
        }

        seed = 3
        guidance_scale = 7

        # no sega enabled
        generator = torch.Generator(torch_device)
        generator.manual_seed(seed)
        output = pipe(
            [prompt],
            generator=generator,
            guidance_scale=guidance_scale,
            num_inference_steps=50,
            output_type="np",
            width=512,
            height=512,
        )

        image = output.images
        image_slice = image[0, -3:, -3:, -1]
        expected_slice = [
            0.34673113,
            0.38492733,
            0.37597352,
            0.34086335,
            0.35650748,
            0.35579205,
            0.3384763,
            0.34340236,
            0.3573271,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

        # with sega enabled
        # generator = torch.manual_seed(seed)
        generator.manual_seed(seed)
        output = pipe(
            [prompt],
            generator=generator,
            guidance_scale=guidance_scale,
            num_inference_steps=50,
            output_type="np",
            width=512,
            height=512,
            **edit,
        )

        image = output.images
        image_slice = image[0, -3:, -3:, -1]
        expected_slice = [
            0.41887826,
            0.37728766,
            0.30138272,
            0.41416335,
            0.41664985,
            0.36283392,
            0.36191246,
            0.43364465,
            0.43001732,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    def test_negative_guidance(self):
        torch_device = "cuda"
        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
        pipe = pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        prompt = "an image of a crowded boulevard, realistic, 4k"
        edit = {
            "editing_prompt": "crowd, crowded, people",
            "reverse_editing_direction": True,
            "edit_warmup_steps": 10,
            "edit_guidance_scale": 8.3,
            "edit_threshold": 0.9,
            "edit_momentum_scale": 0.5,
            "edit_mom_beta": 0.6,
        }

        seed = 9
        guidance_scale = 7

        # no sega enabled
        generator = torch.Generator(torch_device)
        generator.manual_seed(seed)
        output = pipe(
            [prompt],
            generator=generator,
            guidance_scale=guidance_scale,
            num_inference_steps=50,
            output_type="np",
            width=512,
            height=512,
        )

        image = output.images
        image_slice = image[0, -3:, -3:, -1]
        expected_slice = [
            0.43497998,
            0.91814065,
            0.7540739,
            0.55580205,
            0.8467265,
            0.5389691,
            0.62574506,
            0.58897763,
            0.50926757,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

        # with sega enabled
        # generator = torch.manual_seed(seed)
        generator.manual_seed(seed)
        output = pipe(
            [prompt],
            generator=generator,
            guidance_scale=guidance_scale,
            num_inference_steps=50,
            output_type="np",
            width=512,
            height=512,
            **edit,
        )

        image = output.images
        image_slice = image[0, -3:, -3:, -1]
        expected_slice = [
            0.3089719,
            0.30500144,
            0.29016042,
            0.30630964,
            0.325687,
            0.29419225,
            0.2908091,
            0.28723598,
            0.27696294,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    def test_multi_cond_guidance(self):
        torch_device = "cuda"
        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
        pipe = pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        prompt = "a castle next to a river"
        edit = {
            "editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
            "reverse_editing_direction": False,
            "edit_warmup_steps": [15, 18],
            "edit_guidance_scale": 6,
            "edit_threshold": [0.9, 0.8],
            "edit_momentum_scale": 0.5,
            "edit_mom_beta": 0.6,
        }

        seed = 48
        guidance_scale = 7

        # no sega enabled
        generator = torch.Generator(torch_device)
        generator.manual_seed(seed)
        output = pipe(
            [prompt],
            generator=generator,
            guidance_scale=guidance_scale,
            num_inference_steps=50,
            output_type="np",
            width=512,
            height=512,
        )

        image = output.images
        image_slice = image[0, -3:, -3:, -1]
        expected_slice = [
            0.75163555,
            0.76037145,
            0.61785,
            0.9189673,
            0.8627701,
            0.85189694,
            0.8512813,
            0.87012076,
            0.8312857,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

        # with sega enabled
        # generator = torch.manual_seed(seed)
        generator.manual_seed(seed)
        output = pipe(
            [prompt],
            generator=generator,
            guidance_scale=guidance_scale,
            num_inference_steps=50,
            output_type="np",
            width=512,
            height=512,
            **edit,
        )

        image = output.images
        image_slice = image[0, -3:, -3:, -1]
        expected_slice = [
            0.73553365,
            0.7537271,
            0.74341905,
            0.66480356,
            0.6472925,
            0.63039416,
            0.64812905,
            0.6749717,
            0.6517102,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    def test_guidance_fp16(self):
        torch_device = "cuda"
        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
        pipe = pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        prompt = "a photo of a cat"
        edit = {
            "editing_prompt": ["sunglasses"],
            "reverse_editing_direction": [False],
            "edit_warmup_steps": 10,
            "edit_guidance_scale": 6,
            "edit_threshold": 0.95,
            "edit_momentum_scale": 0.5,
            "edit_mom_beta": 0.6,
        }

        seed = 3
        guidance_scale = 7

        # no sega enabled
        generator = torch.Generator(torch_device)
        generator.manual_seed(seed)
        output = pipe(
            [prompt],
            generator=generator,
            guidance_scale=guidance_scale,
            num_inference_steps=50,
            output_type="np",
            width=512,
            height=512,
        )

        image = output.images
        image_slice = image[0, -3:, -3:, -1]
        expected_slice = [
            0.34887695,
            0.3876953,
            0.375,
            0.34423828,
            0.3581543,
            0.35717773,
            0.3383789,
            0.34570312,
            0.359375,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

        # with sega enabled
        # generator = torch.manual_seed(seed)
        generator.manual_seed(seed)
        output = pipe(
            [prompt],
            generator=generator,
            guidance_scale=guidance_scale,
            num_inference_steps=50,
            output_type="np",
            width=512,
            height=512,
            **edit,
        )

        image = output.images
        image_slice = image[0, -3:, -3:, -1]
        expected_slice = [
            0.42285156,
            0.36914062,
            0.29077148,
            0.42041016,
            0.41918945,
            0.35498047,
            0.3618164,
            0.4423828,
            0.43115234,
        ]

        assert image.shape == (1, 512, 512, 3)

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
