# 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 random
import tempfile
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer


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

    def test_harm_safe_stable_diffusion(self):
        sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None)
        sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = (
            "portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
            " coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
            " anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
            " children from bahnhof zoo, detailed "
        )
        seed = 4003660346
        guidance_scale = 7

        # without safety guidance (sld_guidance_scale = 0)
        generator = torch.manual_seed(seed)
        output = sd_pipe(
            [prompt],
            generator=generator,
            guidance_scale=guidance_scale,
            num_inference_steps=50,
            output_type="np",
            width=512,
            height=512,
            sld_guidance_scale=0,
        )

        image = output.images
        image_slice = image[0, -3:, -3:, -1]
        expected_slice = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

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

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

        # without safety guidance (strong configuration)
        generator = torch.manual_seed(seed)
        output = sd_pipe(
            [prompt],
            generator=generator,
            guidance_scale=guidance_scale,
            num_inference_steps=50,
            output_type="np",
            width=512,
            height=512,
            sld_guidance_scale=2000,
            sld_warmup_steps=7,
            sld_threshold=0.025,
            sld_momentum_scale=0.5,
            sld_mom_beta=0.7,
        )

        image = output.images
        image_slice = image[0, -3:, -3:, -1]
        expected_slice = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

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

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

    def test_nudity_safe_stable_diffusion(self):
        sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None)
        sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "padme amidala taking a bath artwork, safe for work, no nudity"
        seed = 2734971755
        guidance_scale = 7

        generator = torch.manual_seed(seed)
        output = sd_pipe(
            [prompt],
            generator=generator,
            guidance_scale=guidance_scale,
            num_inference_steps=50,
            output_type="np",
            width=512,
            height=512,
            sld_guidance_scale=0,
        )

        image = output.images
        image_slice = image[0, -3:, -3:, -1]
        expected_slice = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

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

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

        generator = torch.manual_seed(seed)
        output = sd_pipe(
            [prompt],
            generator=generator,
            guidance_scale=guidance_scale,
            num_inference_steps=50,
            output_type="np",
            width=512,
            height=512,
            sld_guidance_scale=2000,
            sld_warmup_steps=7,
            sld_threshold=0.025,
            sld_momentum_scale=0.5,
            sld_mom_beta=0.7,
        )

        image = output.images
        image_slice = image[0, -3:, -3:, -1]
        expected_slice = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

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

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

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

        prompt = (
            "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
            " leyendecker"
        )
        seed = 1044355234
        guidance_scale = 12

        generator = torch.manual_seed(seed)
        output = sd_pipe(
            [prompt],
            generator=generator,
            guidance_scale=guidance_scale,
            num_inference_steps=50,
            output_type="np",
            width=512,
            height=512,
            sld_guidance_scale=0,
        )

        image = output.images
        image_slice = image[0, -3:, -3:, -1]
        expected_slice = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])

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

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-7

        generator = torch.manual_seed(seed)
        output = sd_pipe(
            [prompt],
            generator=generator,
            guidance_scale=guidance_scale,
            num_inference_steps=50,
            output_type="np",
            width=512,
            height=512,
            sld_guidance_scale=2000,
            sld_warmup_steps=7,
            sld_threshold=0.025,
            sld_momentum_scale=0.5,
            sld_mom_beta=0.7,
        )

        image = output.images
        image_slice = image[0, -3:, -3:, -1]
        expected_slice = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561])
        assert image.shape == (1, 512, 512, 3)

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