import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class StableUnCLIPPipelineFastTests(PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase):
    pipeline_class = StableUnCLIPPipeline
    params = TEXT_TO_IMAGE_PARAMS
    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS

    # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
    test_xformers_attention = False

    def get_dummy_components(self):
        embedder_hidden_size = 32
        embedder_projection_dim = embedder_hidden_size

        # prior components

        torch.manual_seed(0)
        prior_tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        torch.manual_seed(0)
        prior_text_encoder = CLIPTextModelWithProjection(
            CLIPTextConfig(
                bos_token_id=0,
                eos_token_id=2,
                hidden_size=embedder_hidden_size,
                projection_dim=embedder_projection_dim,
                intermediate_size=37,
                layer_norm_eps=1e-05,
                num_attention_heads=4,
                num_hidden_layers=5,
                pad_token_id=1,
                vocab_size=1000,
            )
        )

        torch.manual_seed(0)
        prior = PriorTransformer(
            num_attention_heads=2,
            attention_head_dim=12,
            embedding_dim=embedder_projection_dim,
            num_layers=1,
        )

        torch.manual_seed(0)
        prior_scheduler = DDPMScheduler(
            variance_type="fixed_small_log",
            prediction_type="sample",
            num_train_timesteps=1000,
            clip_sample=True,
            clip_sample_range=5.0,
            beta_schedule="squaredcos_cap_v2",
        )

        # regular denoising components

        torch.manual_seed(0)
        image_normalizer = StableUnCLIPImageNormalizer(embedding_dim=embedder_hidden_size)
        image_noising_scheduler = DDPMScheduler(beta_schedule="squaredcos_cap_v2")

        torch.manual_seed(0)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        torch.manual_seed(0)
        text_encoder = CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0,
                eos_token_id=2,
                hidden_size=embedder_hidden_size,
                projection_dim=32,
                intermediate_size=37,
                layer_norm_eps=1e-05,
                num_attention_heads=4,
                num_hidden_layers=5,
                pad_token_id=1,
                vocab_size=1000,
            )
        )

        torch.manual_seed(0)
        unet = UNet2DConditionModel(
            sample_size=32,
            in_channels=4,
            out_channels=4,
            down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"),
            up_block_types=("UpBlock2D", "CrossAttnUpBlock2D"),
            block_out_channels=(32, 64),
            attention_head_dim=(2, 4),
            class_embed_type="projection",
            # The class embeddings are the noise augmented image embeddings.
            # I.e. the image embeddings concated with the noised embeddings of the same dimension
            projection_class_embeddings_input_dim=embedder_projection_dim * 2,
            cross_attention_dim=embedder_hidden_size,
            layers_per_block=1,
            upcast_attention=True,
            use_linear_projection=True,
        )

        torch.manual_seed(0)
        scheduler = DDIMScheduler(
            beta_schedule="scaled_linear",
            beta_start=0.00085,
            beta_end=0.012,
            prediction_type="v_prediction",
            set_alpha_to_one=False,
            steps_offset=1,
        )

        torch.manual_seed(0)
        vae = AutoencoderKL()

        components = {
            # prior components
            "prior_tokenizer": prior_tokenizer,
            "prior_text_encoder": prior_text_encoder,
            "prior": prior,
            "prior_scheduler": prior_scheduler,
            # image noising components
            "image_normalizer": image_normalizer,
            "image_noising_scheduler": image_noising_scheduler,
            # regular denoising components
            "tokenizer": tokenizer,
            "text_encoder": text_encoder,
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
        }

        return components

    def get_dummy_inputs(self, device, seed=0):
        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",
            "generator": generator,
            "num_inference_steps": 2,
            "prior_num_inference_steps": 2,
            "output_type": "numpy",
        }
        return inputs

    # Overriding PipelineTesterMixin::test_attention_slicing_forward_pass
    # because UnCLIP GPU undeterminism requires a looser check.
    def test_attention_slicing_forward_pass(self):
        test_max_difference = torch_device == "cpu"

        self._test_attention_slicing_forward_pass(test_max_difference=test_max_difference)

    # Overriding PipelineTesterMixin::test_inference_batch_single_identical
    # because UnCLIP undeterminism requires a looser check.
    def test_inference_batch_single_identical(self):
        test_max_difference = torch_device in ["cpu", "mps"]

        self._test_inference_batch_single_identical(test_max_difference=test_max_difference)


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

    def test_stable_unclip(self):
        expected_image = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"
        )

        pipe = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l", torch_dtype=torch.float16)
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        # stable unclip will oom when integration tests are run on a V100,
        # so turn on memory savings
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        generator = torch.Generator(device="cpu").manual_seed(0)
        output = pipe("anime turle", generator=generator, output_type="np")

        image = output.images[0]

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

        assert_mean_pixel_difference(image, expected_image)

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

        pipe = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l", torch_dtype=torch.float16)
        pipe = pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        _ = pipe(
            "anime turtle",
            prior_num_inference_steps=2,
            num_inference_steps=2,
            output_type="np",
        )

        mem_bytes = torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9
