# 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 gc
import time
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerDiscreteScheduler,
    StableDiffusionPipeline,
    UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
    backend_empty_cache,
    backend_max_memory_allocated,
    backend_reset_max_memory_allocated,
    backend_reset_peak_memory_stats,
    enable_full_determinism,
    load_numpy,
    numpy_cosine_similarity_distance,
    require_accelerator,
    require_torch_accelerator,
    slow,
    torch_device,
)


enable_full_determinism()


class StableDiffusion2VPredictionPipelineFastTests(unittest.TestCase):
    def setUp(self):
        # clean up the VRAM before each test
        super().setUp()
        gc.collect()
        backend_empty_cache(torch_device)

    def tearDown(self):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        backend_empty_cache(torch_device)

    @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,
            # SD2-specific config below
            attention_head_dim=(2, 4),
            use_linear_projection=True,
        )
        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,
            sample_size=128,
        )
        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,
            # SD2-specific config below
            hidden_act="gelu",
            projection_dim=64,
        )
        return CLIPTextModel(config)

    def test_stable_diffusion_v_pred_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,
            prediction_type="v_prediction",
        )

        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=None,
            image_encoder=None,
            requires_safety_checker=False,
        )
        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([0.6569, 0.6525, 0.5142, 0.4968, 0.4923, 0.4601, 0.4996, 0.5041, 0.4544])

        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_v_pred_k_euler(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unet = self.dummy_cond_unet
        scheduler = EulerDiscreteScheduler(
            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", prediction_type="v_prediction"
        )
        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=None,
            image_encoder=None,
            requires_safety_checker=False,
        )
        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, 0.6514, 0.5190, 0.5663, 0.5287, 0.4953, 0.5430, 0.5243, 0.4778])

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

    @require_accelerator
    def test_stable_diffusion_v_pred_fp16(self):
        """Test that stable diffusion v-prediction works with fp16"""
        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,
            prediction_type="v_prediction",
        )
        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=None,
            image_encoder=None,
            requires_safety_checker=False,
        )
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "A painting of a squirrel eating a burger"
        generator = torch.manual_seed(0)
        image = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="np").images

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


@slow
@require_torch_accelerator
class StableDiffusion2VPredictionPipelineIntegrationTests(unittest.TestCase):
    def setUp(self):
        # clean up the VRAM before each test
        super().setUp()
        gc.collect()
        backend_empty_cache(torch_device)

    def tearDown(self):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        backend_empty_cache(torch_device)

    def test_stable_diffusion_v_pred_default(self):
        sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2")
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.enable_attention_slicing()
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "A painting of a squirrel eating a burger"
        generator = torch.manual_seed(0)
        output = sd_pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=20, output_type="np")

        image = output.images
        image_slice = image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 768, 768, 3)
        expected_slice = np.array([0.1868, 0.1922, 0.1527, 0.1921, 0.1908, 0.1624, 0.1779, 0.1652, 0.1734])

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

    def test_stable_diffusion_v_pred_upcast_attention(self):
        sd_pipe = StableDiffusionPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
        )
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.enable_attention_slicing()
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "A painting of a squirrel eating a burger"
        generator = torch.manual_seed(0)
        output = sd_pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=20, output_type="np")

        image = output.images
        image_slice = image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 768, 768, 3)
        expected_slice = np.array([0.4209, 0.4087, 0.4097, 0.4209, 0.3860, 0.4329, 0.4280, 0.4324, 0.4187])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2

    def test_stable_diffusion_v_pred_euler(self):
        scheduler = EulerDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-2", subfolder="scheduler")
        sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", scheduler=scheduler)
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.enable_attention_slicing()
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "A painting of a squirrel eating a burger"
        generator = torch.manual_seed(0)

        output = sd_pipe([prompt], generator=generator, num_inference_steps=5, output_type="np")
        image = output.images

        image_slice = image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 768, 768, 3)
        expected_slice = np.array([0.1781, 0.1695, 0.1661, 0.1705, 0.1588, 0.1699, 0.2005, 0.1589, 0.1677])

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

    def test_stable_diffusion_v_pred_dpm(self):
        """
        TODO: update this test after making DPM compatible with V-prediction!
        """
        scheduler = DPMSolverMultistepScheduler.from_pretrained(
            "stabilityai/stable-diffusion-2",
            subfolder="scheduler",
            final_sigmas_type="sigma_min",
        )
        sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", scheduler=scheduler)
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.enable_attention_slicing()
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "a photograph of an astronaut riding a horse"
        generator = torch.manual_seed(0)
        image = sd_pipe(
            [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=5, output_type="np"
        ).images

        image_slice = image[0, 253:256, 253:256, -1]
        assert image.shape == (1, 768, 768, 3)
        expected_slice = np.array([0.3303, 0.3184, 0.3291, 0.3300, 0.3256, 0.3113, 0.2965, 0.3134, 0.3192])

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

    def test_stable_diffusion_attention_slicing_v_pred(self):
        backend_reset_peak_memory_stats(torch_device)
        model_id = "stabilityai/stable-diffusion-2"
        pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        prompt = "a photograph of an astronaut riding a horse"

        # make attention efficient
        pipe.enable_attention_slicing()
        generator = torch.manual_seed(0)
        output_chunked = pipe(
            [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="np"
        )
        image_chunked = output_chunked.images

        mem_bytes = backend_max_memory_allocated(torch_device)
        backend_reset_peak_memory_stats(torch_device)
        # make sure that less than 5.5 GB is allocated
        assert mem_bytes < 5.5 * 10**9

        # disable slicing
        pipe.disable_attention_slicing()
        generator = torch.manual_seed(0)
        output = pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="np")
        image = output.images

        # make sure that more than 3.0 GB is allocated
        mem_bytes = backend_max_memory_allocated(torch_device)
        assert mem_bytes > 3 * 10**9
        max_diff = numpy_cosine_similarity_distance(image.flatten(), image_chunked.flatten())
        assert max_diff < 1e-3

    def test_stable_diffusion_text2img_pipeline_v_pred_default(self):
        expected_image = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
            "sd2-text2img/astronaut_riding_a_horse_v_pred.npy"
        )

        pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2")
        pipe.to(torch_device)
        pipe.enable_attention_slicing()
        pipe.set_progress_bar_config(disable=None)

        prompt = "astronaut riding a horse"

        generator = torch.manual_seed(0)
        output = pipe(prompt=prompt, guidance_scale=7.5, generator=generator, output_type="np")
        image = output.images[0]

        assert image.shape == (768, 768, 3)
        max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())
        assert max_diff < 1e-3

    def test_stable_diffusion_text2img_pipeline_unflawed(self):
        expected_image = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
            "sd2-text2img/lion_galaxy.npy"
        )

        pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")
        pipe.scheduler = DDIMScheduler.from_config(
            pipe.scheduler.config, timestep_spacing="trailing", rescale_betas_zero_snr=True
        )
        pipe.enable_model_cpu_offload(device=torch_device)
        pipe.set_progress_bar_config(disable=None)

        prompt = "A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k"

        generator = torch.Generator("cpu").manual_seed(0)
        output = pipe(
            prompt=prompt,
            guidance_scale=7.5,
            num_inference_steps=10,
            guidance_rescale=0.7,
            generator=generator,
            output_type="np",
        )
        image = output.images[0]

        assert image.shape == (768, 768, 3)
        max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())
        assert max_diff < 5e-2

    def test_stable_diffusion_text2img_pipeline_v_pred_fp16(self):
        expected_image = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
            "sd2-text2img/astronaut_riding_a_horse_v_pred_fp16.npy"
        )

        pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", torch_dtype=torch.float16)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        prompt = "astronaut riding a horse"

        generator = torch.manual_seed(0)
        output = pipe(prompt=prompt, guidance_scale=7.5, generator=generator, output_type="np")
        image = output.images[0]

        assert image.shape == (768, 768, 3)
        max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())
        assert max_diff < 1e-3

    def test_download_local(self):
        filename = hf_hub_download("stabilityai/stable-diffusion-2-1", filename="v2-1_768-ema-pruned.safetensors")

        pipe = StableDiffusionPipeline.from_single_file(filename, torch_dtype=torch.float16)
        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
        pipe.enable_model_cpu_offload(device=torch_device)

        image_out = pipe("test", num_inference_steps=1, output_type="np").images[0]

        assert image_out.shape == (768, 768, 3)

    def test_stable_diffusion_text2img_intermediate_state_v_pred(self):
        number_of_steps = 0

        def test_callback_fn(step: int, timestep: int, latents: torch.Tensor) -> None:
            test_callback_fn.has_been_called = True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 0:
                latents = latents.detach().cpu().numpy()
                assert latents.shape == (1, 4, 96, 96)
                latents_slice = latents[0, -3:, -3:, -1]
                expected_slice = np.array([0.7749, 0.0325, 0.5088, 0.1619, 0.3372, 0.3667, -0.5186, 0.6860, 1.4326])

                assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
            elif step == 19:
                latents = latents.detach().cpu().numpy()
                assert latents.shape == (1, 4, 96, 96)
                latents_slice = latents[0, -3:, -3:, -1]
                expected_slice = np.array([1.3887, 1.0273, 1.7266, 0.0726, 0.6611, 0.1598, -1.0547, 0.1522, 0.0227])

                assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2

        test_callback_fn.has_been_called = False

        pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", torch_dtype=torch.float16)
        pipe = pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        prompt = "Andromeda galaxy in a bottle"

        generator = torch.manual_seed(0)
        pipe(
            prompt=prompt,
            num_inference_steps=20,
            guidance_scale=7.5,
            generator=generator,
            callback=test_callback_fn,
            callback_steps=1,
        )
        assert test_callback_fn.has_been_called
        assert number_of_steps == 20

    def test_stable_diffusion_low_cpu_mem_usage_v_pred(self):
        pipeline_id = "stabilityai/stable-diffusion-2"

        start_time = time.time()
        pipeline_low_cpu_mem_usage = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16)
        pipeline_low_cpu_mem_usage.to(torch_device)
        low_cpu_mem_usage_time = time.time() - start_time

        start_time = time.time()
        _ = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16, low_cpu_mem_usage=False)
        normal_load_time = time.time() - start_time

        assert 2 * low_cpu_mem_usage_time < normal_load_time

    def test_stable_diffusion_pipeline_with_sequential_cpu_offloading_v_pred(self):
        backend_empty_cache(torch_device)
        backend_reset_max_memory_allocated(torch_device)
        backend_reset_peak_memory_stats(torch_device)

        pipeline_id = "stabilityai/stable-diffusion-2"
        prompt = "Andromeda galaxy in a bottle"

        pipeline = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16)
        pipeline.enable_attention_slicing(1)
        pipeline.enable_sequential_cpu_offload(device=torch_device)

        generator = torch.manual_seed(0)
        _ = pipeline(prompt, generator=generator, num_inference_steps=5)

        mem_bytes = backend_max_memory_allocated(torch_device)
        # make sure that less than 2.8 GB is allocated
        assert mem_bytes < 2.8 * 10**9
