# 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 unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    MultiAdapter,
    PNDMScheduler,
    StableDiffusionAdapterPipeline,
    T2IAdapter,
    UNet2DConditionModel,
)
from diffusers.utils import logging
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class AdapterTests:
    pipeline_class = StableDiffusionAdapterPipeline
    params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS

    def get_dummy_components(self, adapter_type):
        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=("CrossAttnDownBlock2D", "DownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
            cross_attention_dim=32,
        )
        scheduler = PNDMScheduler(skip_prk_steps=True)
        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,
        )
        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,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        torch.manual_seed(0)

        if adapter_type == "full_adapter" or adapter_type == "light_adapter":
            adapter = T2IAdapter(
                in_channels=3,
                channels=[32, 64],
                num_res_blocks=2,
                downscale_factor=2,
                adapter_type=adapter_type,
            )
        elif adapter_type == "multi_adapter":
            adapter = MultiAdapter(
                [
                    T2IAdapter(
                        in_channels=3,
                        channels=[32, 64],
                        num_res_blocks=2,
                        downscale_factor=2,
                        adapter_type="full_adapter",
                    ),
                    T2IAdapter(
                        in_channels=3,
                        channels=[32, 64],
                        num_res_blocks=2,
                        downscale_factor=2,
                        adapter_type="full_adapter",
                    ),
                ]
            )
        else:
            raise ValueError(
                f"Unknown adapter type: {adapter_type}, must be one of 'full_adapter', 'light_adapter', or 'multi_adapter''"
            )

        components = {
            "adapter": adapter,
            "unet": unet,
            "scheduler": 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, num_images=1):
        if num_images == 1:
            image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device)
        else:
            image = [floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(device) for _ in range(num_images)]

        if str(device).startswith("mps"):
            generator = torch.manual_seed(seed)
        else:
            generator = torch.Generator(device=device).manual_seed(seed)
        inputs = {
            "prompt": "A painting of a squirrel eating a burger",
            "image": image,
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }
        return inputs

    def test_attention_slicing_forward_pass(self):
        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)

    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available(),
        reason="XFormers attention is only available with CUDA and `xformers` installed",
    )
    def test_xformers_attention_forwardGenerator_pass(self):
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)

    def test_inference_batch_single_identical(self):
        self._test_inference_batch_single_identical(expected_max_diff=2e-3)


class StableDiffusionFullAdapterPipelineFastTests(AdapterTests, PipelineTesterMixin, unittest.TestCase):
    def get_dummy_components(self):
        return super().get_dummy_components("full_adapter")

    def test_stable_diffusion_adapter_default_case(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        sd_pipe = StableDiffusionAdapterPipeline(**components)
        sd_pipe = sd_pipe.to(device)
        sd_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)
        image = sd_pipe(**inputs).images
        image_slice = image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        expected_slice = np.array([0.4858, 0.5500, 0.4278, 0.4669, 0.6184, 0.4322, 0.5010, 0.5033, 0.4746])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3


class StableDiffusionLightAdapterPipelineFastTests(AdapterTests, PipelineTesterMixin, unittest.TestCase):
    def get_dummy_components(self):
        return super().get_dummy_components("light_adapter")

    def test_stable_diffusion_adapter_default_case(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        sd_pipe = StableDiffusionAdapterPipeline(**components)
        sd_pipe = sd_pipe.to(device)
        sd_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)
        image = sd_pipe(**inputs).images
        image_slice = image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        expected_slice = np.array([0.4965, 0.5548, 0.4330, 0.4771, 0.6226, 0.4382, 0.5037, 0.5071, 0.4782])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3


class StableDiffusionMultiAdapterPipelineFastTests(AdapterTests, PipelineTesterMixin, unittest.TestCase):
    def get_dummy_components(self):
        return super().get_dummy_components("multi_adapter")

    def get_dummy_inputs(self, device, seed=0):
        inputs = super().get_dummy_inputs(device, seed, num_images=2)
        inputs["adapter_conditioning_scale"] = [0.5, 0.5]
        return inputs

    def test_stable_diffusion_adapter_default_case(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
        sd_pipe = StableDiffusionAdapterPipeline(**components)
        sd_pipe = sd_pipe.to(device)
        sd_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)
        image = sd_pipe(**inputs).images
        image_slice = image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        expected_slice = np.array([0.4902, 0.5539, 0.4317, 0.4682, 0.6190, 0.4351, 0.5018, 0.5046, 0.4772])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3

    def test_inference_batch_consistent(
        self, batch_sizes=[2, 4, 13], additional_params_copy_to_batched_inputs=["num_inference_steps"]
    ):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(torch_device)

        logger = logging.get_logger(pipe.__module__)
        logger.setLevel(level=diffusers.logging.FATAL)

        # batchify inputs
        for batch_size in batch_sizes:
            batched_inputs = {}
            for name, value in inputs.items():
                if name in self.batch_params:
                    # prompt is string
                    if name == "prompt":
                        len_prompt = len(value)
                        # make unequal batch sizes
                        batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]

                        # make last batch super long
                        batched_inputs[name][-1] = 100 * "very long"
                    elif name == "image":
                        batched_images = []

                        for image in value:
                            batched_images.append(batch_size * [image])

                        batched_inputs[name] = batched_images
                    else:
                        batched_inputs[name] = batch_size * [value]

                elif name == "batch_size":
                    batched_inputs[name] = batch_size
                else:
                    batched_inputs[name] = value

            for arg in additional_params_copy_to_batched_inputs:
                batched_inputs[arg] = inputs[arg]

            batched_inputs["output_type"] = "np"

            if self.pipeline_class.__name__ == "DanceDiffusionPipeline":
                batched_inputs.pop("output_type")

            output = pipe(**batched_inputs)

            assert len(output[0]) == batch_size

            batched_inputs["output_type"] = "np"

            if self.pipeline_class.__name__ == "DanceDiffusionPipeline":
                batched_inputs.pop("output_type")

            output = pipe(**batched_inputs)[0]

            assert output.shape[0] == batch_size

        logger.setLevel(level=diffusers.logging.WARNING)

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

        batch_sizes = [1, 2]
        num_images_per_prompts = [1, 2]

        for batch_size in batch_sizes:
            for num_images_per_prompt in num_images_per_prompts:
                inputs = self.get_dummy_inputs(torch_device)

                for key in inputs.keys():
                    if key in self.batch_params:
                        if key == "image":
                            batched_images = []

                            for image in inputs[key]:
                                batched_images.append(batch_size * [image])

                            inputs[key] = batched_images
                        else:
                            inputs[key] = batch_size * [inputs[key]]

                images = pipe(**inputs, num_images_per_prompt=num_images_per_prompt)[0]

                assert images.shape[0] == batch_size * num_images_per_prompt

    def test_inference_batch_single_identical(
        self,
        batch_size=3,
        test_max_difference=None,
        test_mean_pixel_difference=None,
        relax_max_difference=False,
        expected_max_diff=2e-3,
        additional_params_copy_to_batched_inputs=["num_inference_steps"],
    ):
        if test_max_difference is None:
            # TODO(Pedro) - not sure why, but not at all reproducible at the moment it seems
            # make sure that batched and non-batched is identical
            test_max_difference = torch_device != "mps"

        if test_mean_pixel_difference is None:
            # TODO same as above
            test_mean_pixel_difference = torch_device != "mps"

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

        inputs = self.get_dummy_inputs(torch_device)

        logger = logging.get_logger(pipe.__module__)
        logger.setLevel(level=diffusers.logging.FATAL)

        # batchify inputs
        batched_inputs = {}
        batch_size = batch_size
        for name, value in inputs.items():
            if name in self.batch_params:
                # prompt is string
                if name == "prompt":
                    len_prompt = len(value)
                    # make unequal batch sizes
                    batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]

                    # make last batch super long
                    batched_inputs[name][-1] = 100 * "very long"
                elif name == "image":
                    batched_images = []

                    for image in value:
                        batched_images.append(batch_size * [image])

                    batched_inputs[name] = batched_images
                else:
                    batched_inputs[name] = batch_size * [value]
            elif name == "batch_size":
                batched_inputs[name] = batch_size
            elif name == "generator":
                batched_inputs[name] = [self.get_generator(i) for i in range(batch_size)]
            else:
                batched_inputs[name] = value

        for arg in additional_params_copy_to_batched_inputs:
            batched_inputs[arg] = inputs[arg]

        if self.pipeline_class.__name__ != "DanceDiffusionPipeline":
            batched_inputs["output_type"] = "np"

        output_batch = pipe(**batched_inputs)
        assert output_batch[0].shape[0] == batch_size

        inputs["generator"] = self.get_generator(0)

        output = pipe(**inputs)

        logger.setLevel(level=diffusers.logging.WARNING)
        if test_max_difference:
            if relax_max_difference:
                # Taking the median of the largest <n> differences
                # is resilient to outliers
                diff = np.abs(output_batch[0][0] - output[0][0])
                diff = diff.flatten()
                diff.sort()
                max_diff = np.median(diff[-5:])
            else:
                max_diff = np.abs(output_batch[0][0] - output[0][0]).max()
            assert max_diff < expected_max_diff

        if test_mean_pixel_difference:
            assert_mean_pixel_difference(output_batch[0][0], output[0][0])


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

    def test_stable_diffusion_adapter(self):
        test_cases = [
            (
                "TencentARC/t2iadapter_color_sd14v1",
                "CompVis/stable-diffusion-v1-4",
                "snail",
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/color.png",
                3,
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_color_sd14v1.npy",
            ),
            (
                "TencentARC/t2iadapter_depth_sd14v1",
                "CompVis/stable-diffusion-v1-4",
                "desk",
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/desk_depth.png",
                3,
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_depth_sd14v1.npy",
            ),
            (
                "TencentARC/t2iadapter_depth_sd15v2",
                "runwayml/stable-diffusion-v1-5",
                "desk",
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/desk_depth.png",
                3,
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_depth_sd15v2.npy",
            ),
            (
                "TencentARC/t2iadapter_keypose_sd14v1",
                "CompVis/stable-diffusion-v1-4",
                "person",
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/person_keypose.png",
                3,
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_keypose_sd14v1.npy",
            ),
            (
                "TencentARC/t2iadapter_openpose_sd14v1",
                "CompVis/stable-diffusion-v1-4",
                "person",
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/iron_man_pose.png",
                3,
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_openpose_sd14v1.npy",
            ),
            (
                "TencentARC/t2iadapter_seg_sd14v1",
                "CompVis/stable-diffusion-v1-4",
                "motorcycle",
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motor.png",
                3,
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_seg_sd14v1.npy",
            ),
            (
                "TencentARC/t2iadapter_zoedepth_sd15v1",
                "runwayml/stable-diffusion-v1-5",
                "motorcycle",
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motorcycle.png",
                3,
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_zoedepth_sd15v1.npy",
            ),
            (
                "TencentARC/t2iadapter_canny_sd14v1",
                "CompVis/stable-diffusion-v1-4",
                "toy",
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png",
                1,
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_canny_sd14v1.npy",
            ),
            (
                "TencentARC/t2iadapter_canny_sd15v2",
                "runwayml/stable-diffusion-v1-5",
                "toy",
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png",
                1,
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_canny_sd15v2.npy",
            ),
            (
                "TencentARC/t2iadapter_sketch_sd14v1",
                "CompVis/stable-diffusion-v1-4",
                "cat",
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/edge.png",
                1,
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_sketch_sd14v1.npy",
            ),
            (
                "TencentARC/t2iadapter_sketch_sd15v2",
                "runwayml/stable-diffusion-v1-5",
                "cat",
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/edge.png",
                1,
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_sketch_sd15v2.npy",
            ),
        ]

        for adapter_model, sd_model, prompt, image_url, input_channels, out_url in test_cases:
            image = load_image(image_url)
            expected_out = load_numpy(out_url)

            if input_channels == 1:
                image = image.convert("L")

            adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)

            pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
            pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            pipe.enable_attention_slicing()

            generator = torch.Generator(device="cpu").manual_seed(0)

            out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images

            self.assertTrue(np.allclose(out, expected_out))

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

        adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_seg_sd14v1")
        pipe = StableDiffusionAdapterPipeline.from_pretrained(
            "CompVis/stable-diffusion-v1-4", adapter=adapter, safety_checker=None
        )
        pipe = pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing(1)
        pipe.enable_sequential_cpu_offload()

        image = load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motor.png"
        )

        pipe(prompt="foo", image=image, num_inference_steps=2)

        mem_bytes = torch.cuda.max_memory_allocated()
        assert mem_bytes < 5 * 10**9