# 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 time
import unittest

import numpy as np
import torch

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerDiscreteScheduler,
    StableDiffusionPipeline,
    UNet2DConditionModel,
)
from diffusers.utils import load_numpy, slow, 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 StableDiffusion2VPredictionPipelineFastTests(unittest.TestCase):
    def tearDown(self):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

    @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, 8, 8),
            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,
            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([0.6424, 0.6109, 0.494, 0.5088, 0.4984, 0.4525, 0.5059, 0.5068, 0.4474])

        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,
            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([0.4616, 0.5184, 0.4887, 0.5111, 0.4839, 0.48, 0.5119, 0.5263, 0.4776])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2

    @unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
    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,
            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.Generator(device=torch_device).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_gpu
class StableDiffusion2VPredictionPipelineIntegrationTests(unittest.TestCase):
    def tearDown(self):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

    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.Generator(device=torch_device).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([0.0567, 0.057, 0.0416, 0.0463, 0.0433, 0.06, 0.0517, 0.0526, 0.0866])
        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.Generator(device=torch_device).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([0.0461, 0.0483, 0.0566, 0.0512, 0.0446, 0.0751, 0.0664, 0.0551, 0.0488])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-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.Generator(device=torch_device).manual_seed(0)

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

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

        assert image.shape == (1, 768, 768, 3)
        expected_slice = np.array([0.0351, 0.0376, 0.0505, 0.0424, 0.0551, 0.0656, 0.0471, 0.0276, 0.0596])
        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"
        )
        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.Generator(device=torch_device).manual_seed(0)
        image = sd_pipe(
            [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=5, output_type="numpy"
        ).images

        image_slice = image[0, 253:256, 253:256, -1]
        assert image.shape == (1, 768, 768, 3)
        expected_slice = np.array([0.2049, 0.2115, 0.2323, 0.2416, 0.256, 0.2484, 0.2517, 0.2358, 0.236])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    def test_stable_diffusion_attention_slicing_v_pred(self):
        torch.cuda.reset_peak_memory_stats()
        model_id = "stabilityai/stable-diffusion-2"
        pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", 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.Generator(device=torch_device).manual_seed(0)
        with torch.autocast(torch_device):
            output_chunked = pipe(
                [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
            )
            image_chunked = output_chunked.images

        mem_bytes = torch.cuda.max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()
        # 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.Generator(device=torch_device).manual_seed(0)
        with torch.autocast(torch_device):
            output = pipe(
                [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy"
            )
            image = output.images

        # make sure that more than 5.5 GB is allocated
        mem_bytes = torch.cuda.max_memory_allocated()
        assert mem_bytes > 5.5 * 10**9
        assert np.abs(image_chunked.flatten() - image.flatten()).max() < 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.Generator(device=torch_device).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)
        assert np.abs(expected_image - image).max() < 5e-3

    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", revision="fp16", torch_dtype=torch.float16
        )
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        prompt = "astronaut riding a horse"

        generator = torch.Generator(device=torch_device).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)
        assert np.abs(expected_image - image).max() < 5e-1

    def test_stable_diffusion_text2img_intermediate_state_v_pred(self):
        number_of_steps = 0

        def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> 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(
                    [-0.2543, -1.2755, 0.4261, -0.9555, -1.173, -0.5892, 2.4159, 0.1554, -1.2098]
                )
                assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-3
            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(
                    [-0.9572, -0.967, -0.6152, 0.0894, -0.699, -0.2344, 1.5465, -0.0357, -0.1141]
                )
                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-2

        test_callback_fn.has_been_called = False

        pipe = StableDiffusionPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2", revision="fp16", 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.Generator(device=torch_device).manual_seed(0)
        with torch.autocast(torch_device):
            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, revision="fp16", 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, revision="fp16", 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):
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

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

        pipeline = StableDiffusionPipeline.from_pretrained(pipeline_id, revision="fp16", torch_dtype=torch.float16)
        pipeline = pipeline.to(torch_device)
        pipeline.enable_attention_slicing(1)
        pipeline.enable_sequential_cpu_offload()

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

        mem_bytes = torch.cuda.max_memory_allocated()
        # make sure that less than 2.8 GB is allocated
        assert mem_bytes < 2.8 * 10**9
