# 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 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, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNet2DConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    numpy_cosine_similarity_distance,
    require_torch_gpu,
    slow,
    torch_device,
)


enable_full_determinism()


class StableDiffusionUpscalePipelineFastTests(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_upscale(self):
        torch.manual_seed(0)
        model = UNet2DConditionModel(
            block_out_channels=(32, 32, 64),
            layers_per_block=2,
            sample_size=32,
            in_channels=7,
            out_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"),
            cross_attention_dim=32,
            # SD2-specific config below
            attention_head_dim=8,
            use_linear_projection=True,
            only_cross_attention=(True, True, False),
            num_class_embeds=100,
        )
        return model

    @property
    def dummy_vae(self):
        torch.manual_seed(0)
        model = AutoencoderKL(
            block_out_channels=[32, 32, 64],
            in_channels=3,
            out_channels=3,
            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"],
            up_block_types=["UpDecoderBlock2D", "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,
            # SD2-specific config below
            hidden_act="gelu",
            projection_dim=512,
        )
        return CLIPTextModel(config)

    def test_stable_diffusion_upscale(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unet = self.dummy_cond_unet_upscale
        low_res_scheduler = DDPMScheduler()
        scheduler = DDIMScheduler(prediction_type="v_prediction")
        vae = self.dummy_vae
        text_encoder = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
        low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionUpscalePipeline(
            unet=unet,
            low_res_scheduler=low_res_scheduler,
            scheduler=scheduler,
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            max_noise_level=350,
        )
        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],
            image=low_res_image,
            generator=generator,
            guidance_scale=6.0,
            noise_level=20,
            num_inference_steps=2,
            output_type="np",
        )

        image = output.images

        generator = torch.Generator(device=device).manual_seed(0)
        image_from_tuple = sd_pipe(
            [prompt],
            image=low_res_image,
            generator=generator,
            guidance_scale=6.0,
            noise_level=20,
            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]

        expected_height_width = low_res_image.size[0] * 4
        assert image.shape == (1, expected_height_width, expected_height_width, 3)
        expected_slice = np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])

        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_upscale_batch(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unet = self.dummy_cond_unet_upscale
        low_res_scheduler = DDPMScheduler()
        scheduler = DDIMScheduler(prediction_type="v_prediction")
        vae = self.dummy_vae
        text_encoder = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
        low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionUpscalePipeline(
            unet=unet,
            low_res_scheduler=low_res_scheduler,
            scheduler=scheduler,
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            max_noise_level=350,
        )
        sd_pipe = sd_pipe.to(device)
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "A painting of a squirrel eating a burger"
        output = sd_pipe(
            2 * [prompt],
            image=2 * [low_res_image],
            guidance_scale=6.0,
            noise_level=20,
            num_inference_steps=2,
            output_type="np",
        )
        image = output.images
        assert image.shape[0] == 2

        generator = torch.Generator(device=device).manual_seed(0)
        output = sd_pipe(
            [prompt],
            image=low_res_image,
            generator=generator,
            num_images_per_prompt=2,
            guidance_scale=6.0,
            noise_level=20,
            num_inference_steps=2,
            output_type="np",
        )
        image = output.images
        assert image.shape[0] == 2

    def test_stable_diffusion_upscale_prompt_embeds(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        unet = self.dummy_cond_unet_upscale
        low_res_scheduler = DDPMScheduler()
        scheduler = DDIMScheduler(prediction_type="v_prediction")
        vae = self.dummy_vae
        text_encoder = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
        low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionUpscalePipeline(
            unet=unet,
            low_res_scheduler=low_res_scheduler,
            scheduler=scheduler,
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            max_noise_level=350,
        )
        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],
            image=low_res_image,
            generator=generator,
            guidance_scale=6.0,
            noise_level=20,
            num_inference_steps=2,
            output_type="np",
        )

        image = output.images

        generator = torch.Generator(device=device).manual_seed(0)
        prompt_embeds, negative_prompt_embeds = sd_pipe.encode_prompt(prompt, device, 1, False)
        if negative_prompt_embeds is not None:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

        image_from_prompt_embeds = sd_pipe(
            prompt_embeds=prompt_embeds,
            image=[low_res_image],
            generator=generator,
            guidance_scale=6.0,
            noise_level=20,
            num_inference_steps=2,
            output_type="np",
            return_dict=False,
        )[0]

        image_slice = image[0, -3:, -3:, -1]
        image_from_prompt_embeds_slice = image_from_prompt_embeds[0, -3:, -3:, -1]

        expected_height_width = low_res_image.size[0] * 4
        assert image.shape == (1, expected_height_width, expected_height_width, 3)
        expected_slice = np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661])

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

    @unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
    def test_stable_diffusion_upscale_fp16(self):
        """Test that stable diffusion upscale works with fp16"""
        unet = self.dummy_cond_unet_upscale
        low_res_scheduler = DDPMScheduler()
        scheduler = DDIMScheduler(prediction_type="v_prediction")
        vae = self.dummy_vae
        text_encoder = self.dummy_text_encoder
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
        low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))

        # put models in fp16, except vae as it overflows in fp16
        unet = unet.half()
        text_encoder = text_encoder.half()

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionUpscalePipeline(
            unet=unet,
            low_res_scheduler=low_res_scheduler,
            scheduler=scheduler,
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            max_noise_level=350,
        )
        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],
            image=low_res_image,
            generator=generator,
            num_inference_steps=2,
            output_type="np",
        ).images

        expected_height_width = low_res_image.size[0] * 4
        assert image.shape == (1, expected_height_width, expected_height_width, 3)

    def test_stable_diffusion_upscale_from_save_pretrained(self):
        pipes = []

        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        low_res_scheduler = DDPMScheduler()
        scheduler = DDIMScheduler(prediction_type="v_prediction")
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        # make sure here that pndm scheduler skips prk
        sd_pipe = StableDiffusionUpscalePipeline(
            unet=self.dummy_cond_unet_upscale,
            low_res_scheduler=low_res_scheduler,
            scheduler=scheduler,
            vae=self.dummy_vae,
            text_encoder=self.dummy_text_encoder,
            tokenizer=tokenizer,
            max_noise_level=350,
        )
        sd_pipe = sd_pipe.to(device)
        pipes.append(sd_pipe)

        with tempfile.TemporaryDirectory() as tmpdirname:
            sd_pipe.save_pretrained(tmpdirname)
            sd_pipe = StableDiffusionUpscalePipeline.from_pretrained(tmpdirname).to(device)
        pipes.append(sd_pipe)

        prompt = "A painting of a squirrel eating a burger"
        image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
        low_res_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64))

        image_slices = []
        for pipe in pipes:
            generator = torch.Generator(device=device).manual_seed(0)
            image = pipe(
                [prompt],
                image=low_res_image,
                generator=generator,
                guidance_scale=6.0,
                noise_level=20,
                num_inference_steps=2,
                output_type="np",
            ).images
            image_slices.append(image[0, -3:, -3:, -1].flatten())

        assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3


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

    def test_stable_diffusion_upscale_pipeline(self):
        image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-upscale/low_res_cat.png"
        )
        expected_image = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
            "/upsampled_cat.npy"
        )

        model_id = "stabilityai/stable-diffusion-x4-upscaler"
        pipe = StableDiffusionUpscalePipeline.from_pretrained(model_id)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        prompt = "a cat sitting on a park bench"

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

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image).max() < 1e-3

    def test_stable_diffusion_upscale_pipeline_fp16(self):
        image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-upscale/low_res_cat.png"
        )
        expected_image = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
            "/upsampled_cat_fp16.npy"
        )

        model_id = "stabilityai/stable-diffusion-x4-upscaler"
        pipe = StableDiffusionUpscalePipeline.from_pretrained(
            model_id,
            torch_dtype=torch.float16,
        )
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()

        prompt = "a cat sitting on a park bench"

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

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image).max() < 5e-1

    def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-upscale/low_res_cat.png"
        )

        model_id = "stabilityai/stable-diffusion-x4-upscaler"
        pipe = StableDiffusionUpscalePipeline.from_pretrained(
            model_id,
            torch_dtype=torch.float16,
        )
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing(1)
        pipe.enable_sequential_cpu_offload()

        prompt = "a cat sitting on a park bench"

        generator = torch.manual_seed(0)
        _ = pipe(
            prompt=prompt,
            image=image,
            generator=generator,
            num_inference_steps=5,
            output_type="np",
        )

        mem_bytes = torch.cuda.max_memory_allocated()
        # make sure that less than 2.9 GB is allocated
        assert mem_bytes < 2.9 * 10**9

    def test_download_ckpt_diff_format_is_same(self):
        image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-upscale/low_res_cat.png"
        )

        prompt = "a cat sitting on a park bench"
        model_id = "stabilityai/stable-diffusion-x4-upscaler"
        pipe = StableDiffusionUpscalePipeline.from_pretrained(model_id)
        pipe.enable_model_cpu_offload()

        generator = torch.Generator("cpu").manual_seed(0)
        output = pipe(prompt=prompt, image=image, generator=generator, output_type="np", num_inference_steps=3)
        image_from_pretrained = output.images[0]

        single_file_path = (
            "https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/blob/main/x4-upscaler-ema.safetensors"
        )
        pipe_from_single_file = StableDiffusionUpscalePipeline.from_single_file(single_file_path)
        pipe_from_single_file.enable_model_cpu_offload()

        generator = torch.Generator("cpu").manual_seed(0)
        output_from_single_file = pipe_from_single_file(
            prompt=prompt, image=image, generator=generator, output_type="np", num_inference_steps=3
        )
        image_from_single_file = output_from_single_file.images[0]

        assert image_from_pretrained.shape == (512, 512, 3)
        assert image_from_single_file.shape == (512, 512, 3)
        assert (
            numpy_cosine_similarity_distance(image_from_pretrained.flatten(), image_from_single_file.flatten()) < 1e-3
        )

    def test_single_file_component_configs(self):
        pipe = StableDiffusionUpscalePipeline.from_pretrained(
            "stabilityai/stable-diffusion-x4-upscaler", variant="fp16"
        )

        ckpt_path = (
            "https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/blob/main/x4-upscaler-ema.safetensors"
        )
        single_file_pipe = StableDiffusionUpscalePipeline.from_single_file(ckpt_path, load_safety_checker=True)

        for param_name, param_value in single_file_pipe.text_encoder.config.to_dict().items():
            if param_name in ["torch_dtype", "architectures", "_name_or_path"]:
                continue
            assert pipe.text_encoder.config.to_dict()[param_name] == param_value

        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "architectures", "_use_default_values"]
        for param_name, param_value in single_file_pipe.unet.config.items():
            if param_name in PARAMS_TO_IGNORE:
                continue
            assert (
                pipe.unet.config[param_name] == param_value
            ), f"{param_name} differs between single file loading and pretrained loading"

        for param_name, param_value in single_file_pipe.vae.config.items():
            if param_name in PARAMS_TO_IGNORE:
                continue
            assert (
                pipe.vae.config[param_name] == param_value
            ), f"{param_name} differs between single file loading and pretrained loading"

        for param_name, param_value in single_file_pipe.safety_checker.config.to_dict().items():
            if param_name in PARAMS_TO_IGNORE:
                continue
            assert (
                pipe.safety_checker.config.to_dict()[param_name] == param_value
            ), f"{param_name} differs between single file loading and pretrained loading"
