import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    nightly,
    numpy_cosine_similarity_distance,
    require_torch_gpu,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class StableDiffusionDiffEditPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
    pipeline_class = StableDiffusionDiffEditPipeline
    params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
    batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
    image_params = frozenset(
        []
    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    image_latents_params = frozenset([])

    def get_dummy_components(self):
        torch.manual_seed(0)
        unet = 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,
            # SD2-specific config below
            attention_head_dim=(2, 4),
            use_linear_projection=True,
        )
        scheduler = DDIMScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_one=False,
        )
        inverse_scheduler = DDIMInverseScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_zero=False,
        )
        torch.manual_seed(0)
        vae = 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,
            sample_size=128,
        )
        torch.manual_seed(0)
        text_encoder_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,
            # SD2-specific config below
            hidden_act="gelu",
            projection_dim=512,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        components = {
            "unet": unet,
            "scheduler": scheduler,
            "inverse_scheduler": inverse_scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }

        return components

    def get_dummy_inputs(self, device, seed=0):
        mask = floats_tensor((1, 16, 16), rng=random.Random(seed)).to(device)
        latents = floats_tensor((1, 2, 4, 16, 16), rng=random.Random(seed)).to(device)
        if str(device).startswith("mps"):
            generator = torch.manual_seed(seed)
        else:
            generator = torch.Generator(device=device).manual_seed(seed)
        inputs = {
            "prompt": "a dog and a newt",
            "mask_image": mask,
            "image_latents": latents,
            "generator": generator,
            "num_inference_steps": 2,
            "inpaint_strength": 1.0,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }

        return inputs

    def get_dummy_mask_inputs(self, device, seed=0):
        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
        image = image.cpu().permute(0, 2, 3, 1)[0]
        image = Image.fromarray(np.uint8(image)).convert("RGB")
        if str(device).startswith("mps"):
            generator = torch.manual_seed(seed)
        else:
            generator = torch.Generator(device=device).manual_seed(seed)
        inputs = {
            "image": image,
            "source_prompt": "a cat and a frog",
            "target_prompt": "a dog and a newt",
            "generator": generator,
            "num_inference_steps": 2,
            "num_maps_per_mask": 2,
            "mask_encode_strength": 1.0,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }

        return inputs

    def get_dummy_inversion_inputs(self, device, seed=0):
        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
        image = image.cpu().permute(0, 2, 3, 1)[0]
        image = Image.fromarray(np.uint8(image)).convert("RGB")
        if str(device).startswith("mps"):
            generator = torch.manual_seed(seed)
        else:
            generator = torch.Generator(device=device).manual_seed(seed)
        inputs = {
            "image": image,
            "prompt": "a cat and a frog",
            "generator": generator,
            "num_inference_steps": 2,
            "inpaint_strength": 1.0,
            "guidance_scale": 6.0,
            "decode_latents": True,
            "output_type": "numpy",
        }
        return inputs

    def test_save_load_optional_components(self):
        if not hasattr(self.pipeline_class, "_optional_components"):
            return

        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        # set all optional components to None and update pipeline config accordingly
        for optional_component in pipe._optional_components:
            setattr(pipe, optional_component, None)
        pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components})

        inputs = self.get_dummy_inputs(torch_device)
        output = pipe(**inputs)[0]

        with tempfile.TemporaryDirectory() as tmpdir:
            pipe.save_pretrained(tmpdir)
            pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
            pipe_loaded.to(torch_device)
            pipe_loaded.set_progress_bar_config(disable=None)

        for optional_component in pipe._optional_components:
            self.assertTrue(
                getattr(pipe_loaded, optional_component) is None,
                f"`{optional_component}` did not stay set to None after loading.",
            )

        inputs = self.get_dummy_inputs(torch_device)
        output_loaded = pipe_loaded(**inputs)[0]

        max_diff = np.abs(output - output_loaded).max()
        self.assertLess(max_diff, 1e-4)

    def test_mask(self):
        device = "cpu"

        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.to(device)
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_mask_inputs(device)
        mask = pipe.generate_mask(**inputs)
        mask_slice = mask[0, -3:, -3:]

        self.assertEqual(mask.shape, (1, 16, 16))
        expected_slice = np.array([0] * 9)
        max_diff = np.abs(mask_slice.flatten() - expected_slice).max()
        self.assertLessEqual(max_diff, 1e-3)
        self.assertEqual(mask[0, -3, -4], 0)

    def test_inversion(self):
        device = "cpu"

        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.to(device)
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inversion_inputs(device)
        image = pipe.invert(**inputs).images
        image_slice = image[0, -1, -3:, -3:]

        self.assertEqual(image.shape, (2, 32, 32, 3))
        expected_slice = np.array(
            [0.5160, 0.5115, 0.5060, 0.5456, 0.4704, 0.5060, 0.5019, 0.4405, 0.4726],
        )
        max_diff = np.abs(image_slice.flatten() - expected_slice).max()
        self.assertLessEqual(max_diff, 1e-3)

    def test_inference_batch_single_identical(self):
        super().test_inference_batch_single_identical(expected_max_diff=5e-3)

    def test_inversion_dpm(self):
        device = "cpu"

        components = self.get_dummy_components()

        scheduler_args = {"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
        components["scheduler"] = DPMSolverMultistepScheduler(**scheduler_args)
        components["inverse_scheduler"] = DPMSolverMultistepInverseScheduler(**scheduler_args)

        pipe = self.pipeline_class(**components)
        pipe.to(device)
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inversion_inputs(device)
        image = pipe.invert(**inputs).images
        image_slice = image[0, -1, -3:, -3:]

        self.assertEqual(image.shape, (2, 32, 32, 3))
        expected_slice = np.array(
            [0.5305, 0.4673, 0.5314, 0.5308, 0.4886, 0.5279, 0.5142, 0.4724, 0.4892],
        )
        max_diff = np.abs(image_slice.flatten() - expected_slice).max()
        self.assertLessEqual(max_diff, 1e-3)


@require_torch_gpu
@nightly
class StableDiffusionDiffEditPipelineIntegrationTests(unittest.TestCase):
    def tearDown(self):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

    @classmethod
    def setUpClass(cls):
        raw_image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"
        )
        raw_image = raw_image.convert("RGB").resize((256, 256))

        cls.raw_image = raw_image

    def test_stable_diffusion_diffedit_full(self):
        generator = torch.manual_seed(0)

        pipe = StableDiffusionDiffEditPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2-1-base", safety_checker=None, torch_dtype=torch.float16
        )
        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
        pipe.scheduler.clip_sample = True

        pipe.inverse_scheduler = DDIMInverseScheduler.from_config(pipe.scheduler.config)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=None)

        source_prompt = "a bowl of fruit"
        target_prompt = "a bowl of pears"

        mask_image = pipe.generate_mask(
            image=self.raw_image,
            source_prompt=source_prompt,
            target_prompt=target_prompt,
            generator=generator,
        )

        inv_latents = pipe.invert(
            prompt=source_prompt,
            image=self.raw_image,
            inpaint_strength=0.7,
            generator=generator,
            num_inference_steps=5,
        ).latents

        image = pipe(
            prompt=target_prompt,
            mask_image=mask_image,
            image_latents=inv_latents,
            generator=generator,
            negative_prompt=source_prompt,
            inpaint_strength=0.7,
            num_inference_steps=5,
            output_type="np",
        ).images[0]

        expected_image = (
            np.array(
                load_image(
                    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                    "/diffedit/pears.png"
                ).resize((256, 256))
            )
            / 255
        )

        assert numpy_cosine_similarity_distance(expected_image.flatten(), image.flatten()) < 2e-1


@nightly
@require_torch_gpu
class StableDiffusionDiffEditPipelineNightlyTests(unittest.TestCase):
    def tearDown(self):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

    @classmethod
    def setUpClass(cls):
        raw_image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"
        )

        raw_image = raw_image.convert("RGB").resize((768, 768))

        cls.raw_image = raw_image

    def test_stable_diffusion_diffedit_dpm(self):
        generator = torch.manual_seed(0)

        pipe = StableDiffusionDiffEditPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2-1", safety_checker=None, torch_dtype=torch.float16
        )
        pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
        pipe.inverse_scheduler = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=None)

        source_prompt = "a bowl of fruit"
        target_prompt = "a bowl of pears"

        mask_image = pipe.generate_mask(
            image=self.raw_image,
            source_prompt=source_prompt,
            target_prompt=target_prompt,
            generator=generator,
        )

        inv_latents = pipe.invert(
            prompt=source_prompt,
            image=self.raw_image,
            inpaint_strength=0.7,
            generator=generator,
            num_inference_steps=25,
        ).latents

        image = pipe(
            prompt=target_prompt,
            mask_image=mask_image,
            image_latents=inv_latents,
            generator=generator,
            negative_prompt=source_prompt,
            inpaint_strength=0.7,
            num_inference_steps=25,
            output_type="numpy",
        ).images[0]

        expected_image = (
            np.array(
                load_image(
                    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                    "/diffedit/pears.png"
                ).resize((768, 768))
            )
            / 255
        )
        assert np.abs((expected_image - image).max()) < 5e-1
