# 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 random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    LCMScheduler,
    MultiAdapter,
    StableDiffusionXLAdapterPipeline,
    T2IAdapter,
    UNet2DConditionModel,
)
from diffusers.utils import logging
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    IPAdapterTesterMixin,
    PipelineTesterMixin,
    SDXLOptionalComponentsTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class StableDiffusionXLAdapterPipelineFastTests(
    IPAdapterTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin, unittest.TestCase
):
    pipeline_class = StableDiffusionXLAdapterPipeline
    params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS

    def get_dummy_components(self, adapter_type="full_adapter_xl", time_cond_proj_dim=None):
        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"),
            # SD2-specific config below
            attention_head_dim=(2, 4),
            use_linear_projection=True,
            addition_embed_type="text_time",
            addition_time_embed_dim=8,
            transformer_layers_per_block=(1, 2),
            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
            cross_attention_dim=64,
            time_cond_proj_dim=time_cond_proj_dim,
        )
        scheduler = EulerDiscreteScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            steps_offset=1,
            beta_schedule="scaled_linear",
            timestep_spacing="leading",
        )
        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=32,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
        if adapter_type == "full_adapter_xl":
            adapter = T2IAdapter(
                in_channels=3,
                channels=[32, 64],
                num_res_blocks=2,
                downscale_factor=4,
                adapter_type=adapter_type,
            )
        elif adapter_type == "multi_adapter":
            adapter = MultiAdapter(
                [
                    T2IAdapter(
                        in_channels=3,
                        channels=[32, 64],
                        num_res_blocks=2,
                        downscale_factor=4,
                        adapter_type="full_adapter_xl",
                    ),
                    T2IAdapter(
                        in_channels=3,
                        channels=[32, 64],
                        num_res_blocks=2,
                        downscale_factor=4,
                        adapter_type="full_adapter_xl",
                    ),
                ]
            )
        else:
            raise ValueError(
                f"Unknown adapter type: {adapter_type}, must be one of 'full_adapter_xl', or 'multi_adapter''"
            )

        components = {
            "adapter": adapter,
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_encoder_2": text_encoder_2,
            "tokenizer_2": tokenizer_2,
            # "safety_checker": None,
            "feature_extractor": None,
            "image_encoder": None,
        }
        return components

    def get_dummy_components_with_full_downscaling(self, adapter_type="full_adapter_xl"):
        """Get dummy components with x8 VAE downscaling and 3 UNet down blocks.
        These dummy components are intended to fully-exercise the T2I-Adapter
        downscaling behavior.
        """
        torch.manual_seed(0)
        unet = UNet2DConditionModel(
            block_out_channels=(32, 32, 64),
            layers_per_block=2,
            sample_size=32,
            in_channels=4,
            out_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"),
            # SD2-specific config below
            attention_head_dim=2,
            use_linear_projection=True,
            addition_embed_type="text_time",
            addition_time_embed_dim=8,
            transformer_layers_per_block=1,
            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
            cross_attention_dim=64,
        )
        scheduler = EulerDiscreteScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            steps_offset=1,
            beta_schedule="scaled_linear",
            timestep_spacing="leading",
        )
        torch.manual_seed(0)
        vae = AutoencoderKL(
            block_out_channels=[32, 32, 32, 64],
            in_channels=3,
            out_channels=3,
            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"],
            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "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=32,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
        if adapter_type == "full_adapter_xl":
            adapter = T2IAdapter(
                in_channels=3,
                channels=[32, 32, 64],
                num_res_blocks=2,
                downscale_factor=16,
                adapter_type=adapter_type,
            )
        elif adapter_type == "multi_adapter":
            adapter = MultiAdapter(
                [
                    T2IAdapter(
                        in_channels=3,
                        channels=[32, 32, 64],
                        num_res_blocks=2,
                        downscale_factor=16,
                        adapter_type="full_adapter_xl",
                    ),
                    T2IAdapter(
                        in_channels=3,
                        channels=[32, 32, 64],
                        num_res_blocks=2,
                        downscale_factor=16,
                        adapter_type="full_adapter_xl",
                    ),
                ]
            )
        else:
            raise ValueError(
                f"Unknown adapter type: {adapter_type}, must be one of 'full_adapter_xl', or 'multi_adapter''"
            )

        components = {
            "adapter": adapter,
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_encoder_2": text_encoder_2,
            "tokenizer_2": tokenizer_2,
            # "safety_checker": None,
            "feature_extractor": None,
            "image_encoder": None,
        }
        return components

    def get_dummy_inputs(self, device, seed=0, height=64, width=64, num_images=1):
        if num_images == 1:
            image = floats_tensor((1, 3, height, width), rng=random.Random(seed)).to(device)
        else:
            image = [
                floats_tensor((1, 3, height, width), 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": 5.0,
            "output_type": "np",
        }
        return inputs

    def test_ip_adapter(self, from_multi=False, expected_pipe_slice=None):
        if not from_multi:
            expected_pipe_slice = None
            if torch_device == "cpu":
                expected_pipe_slice = np.array(
                    [0.5752, 0.6155, 0.4826, 0.5111, 0.5741, 0.4678, 0.5199, 0.5231, 0.4794]
                )

        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)

    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 = StableDiffusionXLAdapterPipeline(**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([00.5752, 0.6155, 0.4826, 0.5111, 0.5741, 0.4678, 0.5199, 0.5231, 0.4794])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3

    @parameterized.expand(
        [
            # (dim=144) The internal feature map will be 9x9 after initial pixel unshuffling (downscaled x16).
            (((4 * 2 + 1) * 16),),
            # (dim=160) The internal feature map will be 5x5 after the first T2I down block (downscaled x32).
            (((4 * 1 + 1) * 32),),
        ]
    )
    def test_multiple_image_dimensions(self, dim):
        """Test that the T2I-Adapter pipeline supports any input dimension that
        is divisible by the adapter's `downscale_factor`. This test was added in
        response to an issue where the T2I Adapter's downscaling padding
        behavior did not match the UNet's behavior.

        Note that we have selected `dim` values to produce odd resolutions at
        each downscaling level.
        """
        components = self.get_dummy_components_with_full_downscaling()
        sd_pipe = StableDiffusionXLAdapterPipeline(**components)
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(torch_device, height=dim, width=dim)
        image = sd_pipe(**inputs).images

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

    @parameterized.expand(["full_adapter", "full_adapter_xl", "light_adapter"])
    def test_total_downscale_factor(self, adapter_type):
        """Test that the T2IAdapter correctly reports its total_downscale_factor."""
        batch_size = 1
        in_channels = 3
        out_channels = [320, 640, 1280, 1280]
        in_image_size = 512

        adapter = T2IAdapter(
            in_channels=in_channels,
            channels=out_channels,
            num_res_blocks=2,
            downscale_factor=8,
            adapter_type=adapter_type,
        )
        adapter.to(torch_device)

        in_image = floats_tensor((batch_size, in_channels, in_image_size, in_image_size)).to(torch_device)

        adapter_state = adapter(in_image)

        # Assume that the last element in `adapter_state` has been downsampled the most, and check
        # that it matches the `total_downscale_factor`.
        expected_out_image_size = in_image_size // adapter.total_downscale_factor
        assert adapter_state[-1].shape == (
            batch_size,
            out_channels[-1],
            expected_out_image_size,
            expected_out_image_size,
        )

    def test_save_load_optional_components(self):
        return self._test_save_load_optional_components()

    def test_adapter_sdxl_lcm(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator

        components = self.get_dummy_components(time_cond_proj_dim=256)
        sd_pipe = StableDiffusionXLAdapterPipeline(**components)
        sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)
        output = sd_pipe(**inputs)
        image = output.images

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

        assert image.shape == (1, 64, 64, 3)
        expected_slice = np.array([0.5425, 0.5385, 0.4964, 0.5045, 0.6149, 0.4974, 0.5469, 0.5332, 0.5426])

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

    def test_adapter_sdxl_lcm_custom_timesteps(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator

        components = self.get_dummy_components(time_cond_proj_dim=256)
        sd_pipe = StableDiffusionXLAdapterPipeline(**components)
        sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)
        del inputs["num_inference_steps"]
        inputs["timesteps"] = [999, 499]
        output = sd_pipe(**inputs)
        image = output.images

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

        assert image.shape == (1, 64, 64, 3)
        expected_slice = np.array([0.5425, 0.5385, 0.4964, 0.5045, 0.6149, 0.4974, 0.5469, 0.5332, 0.5426])

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


class StableDiffusionXLMultiAdapterPipelineFastTests(
    StableDiffusionXLAdapterPipelineFastTests, PipelineTesterMixin, unittest.TestCase
):
    def get_dummy_components(self, time_cond_proj_dim=None):
        return super().get_dummy_components("multi_adapter", time_cond_proj_dim=time_cond_proj_dim)

    def get_dummy_components_with_full_downscaling(self):
        return super().get_dummy_components_with_full_downscaling("multi_adapter")

    def get_dummy_inputs(self, device, seed=0, height=64, width=64):
        inputs = super().get_dummy_inputs(device, seed, height, width, 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 = StableDiffusionXLAdapterPipeline(**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([0.5617, 0.6081, 0.4807, 0.5071, 0.5665, 0.4614, 0.5165, 0.5164, 0.4786])
        assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3

    def test_ip_adapter(self):
        expected_pipe_slice = None
        if torch_device == "cpu":
            expected_pipe_slice = np.array([0.5617, 0.6081, 0.4807, 0.5071, 0.5665, 0.4614, 0.5165, 0.5164, 0.4786])

        return super().test_ip_adapter(from_multi=True, expected_pipe_slice=expected_pipe_slice)

    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"

            output = pipe(**batched_inputs)

            assert len(output[0]) == batch_size

            batched_inputs["output_type"] = "np"

            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 = {}
        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]

        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])

    def test_adapter_sdxl_lcm(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator

        components = self.get_dummy_components(time_cond_proj_dim=256)
        sd_pipe = StableDiffusionXLAdapterPipeline(**components)
        sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)
        output = sd_pipe(**inputs)
        image = output.images

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

        assert image.shape == (1, 64, 64, 3)
        expected_slice = np.array([0.5313, 0.5375, 0.4942, 0.5021, 0.6142, 0.4968, 0.5434, 0.5311, 0.5448])

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

    def test_adapter_sdxl_lcm_custom_timesteps(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator

        components = self.get_dummy_components(time_cond_proj_dim=256)
        sd_pipe = StableDiffusionXLAdapterPipeline(**components)
        sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(device)
        del inputs["num_inference_steps"]
        inputs["timesteps"] = [999, 499]
        output = sd_pipe(**inputs)
        image = output.images

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

        assert image.shape == (1, 64, 64, 3)
        expected_slice = np.array([0.5313, 0.5375, 0.4942, 0.5021, 0.6142, 0.4968, 0.5434, 0.5311, 0.5448])

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