import gc
import unittest

import numpy as np
import torch
from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel

from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, SD3Transformer2DModel, StableDiffusion3Pipeline
from diffusers.utils.testing_utils import (
    backend_empty_cache,
    numpy_cosine_similarity_distance,
    require_big_accelerator,
    slow,
    torch_device,
)

from ..test_pipelines_common import (
    PipelineTesterMixin,
    check_qkv_fusion_matches_attn_procs_length,
    check_qkv_fusion_processors_exist,
)


class StableDiffusion3PipelineFastTests(unittest.TestCase, PipelineTesterMixin):
    pipeline_class = StableDiffusion3Pipeline
    params = frozenset(
        [
            "prompt",
            "height",
            "width",
            "guidance_scale",
            "negative_prompt",
            "prompt_embeds",
            "negative_prompt_embeds",
        ]
    )
    batch_params = frozenset(["prompt", "negative_prompt"])
    test_layerwise_casting = True
    test_group_offloading = True

    def get_dummy_components(self):
        torch.manual_seed(0)
        transformer = SD3Transformer2DModel(
            sample_size=32,
            patch_size=1,
            in_channels=4,
            num_layers=1,
            attention_head_dim=8,
            num_attention_heads=4,
            caption_projection_dim=32,
            joint_attention_dim=32,
            pooled_projection_dim=64,
            out_channels=4,
        )
        clip_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,
            hidden_act="gelu",
            projection_dim=32,
        )

        torch.manual_seed(0)
        text_encoder = CLIPTextModelWithProjection(clip_text_encoder_config)

        torch.manual_seed(0)
        text_encoder_2 = CLIPTextModelWithProjection(clip_text_encoder_config)

        text_encoder_3 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")

        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
        tokenizer_3 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")

        torch.manual_seed(0)
        vae = AutoencoderKL(
            sample_size=32,
            in_channels=3,
            out_channels=3,
            block_out_channels=(4,),
            layers_per_block=1,
            latent_channels=4,
            norm_num_groups=1,
            use_quant_conv=False,
            use_post_quant_conv=False,
            shift_factor=0.0609,
            scaling_factor=1.5035,
        )

        scheduler = FlowMatchEulerDiscreteScheduler()

        return {
            "scheduler": scheduler,
            "text_encoder": text_encoder,
            "text_encoder_2": text_encoder_2,
            "text_encoder_3": text_encoder_3,
            "tokenizer": tokenizer,
            "tokenizer_2": tokenizer_2,
            "tokenizer_3": tokenizer_3,
            "transformer": transformer,
            "vae": vae,
            "image_encoder": None,
            "feature_extractor": None,
        }

    def get_dummy_inputs(self, device, seed=0):
        if str(device).startswith("mps"):
            generator = torch.manual_seed(seed)
        else:
            generator = torch.Generator(device="cpu").manual_seed(seed)

        inputs = {
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 5.0,
            "output_type": "np",
        }
        return inputs

    def test_stable_diffusion_3_different_prompts(self):
        pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)

        inputs = self.get_dummy_inputs(torch_device)
        output_same_prompt = pipe(**inputs).images[0]

        inputs = self.get_dummy_inputs(torch_device)
        inputs["prompt_2"] = "a different prompt"
        inputs["prompt_3"] = "another different prompt"
        output_different_prompts = pipe(**inputs).images[0]

        max_diff = np.abs(output_same_prompt - output_different_prompts).max()

        # Outputs should be different here
        assert max_diff > 1e-2

    def test_stable_diffusion_3_different_negative_prompts(self):
        pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)

        inputs = self.get_dummy_inputs(torch_device)
        output_same_prompt = pipe(**inputs).images[0]

        inputs = self.get_dummy_inputs(torch_device)
        inputs["negative_prompt_2"] = "deformed"
        inputs["negative_prompt_3"] = "blurry"
        output_different_prompts = pipe(**inputs).images[0]

        max_diff = np.abs(output_same_prompt - output_different_prompts).max()

        # Outputs should be different here
        assert max_diff > 1e-2

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

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

        # TODO (sayakpaul): will refactor this once `fuse_qkv_projections()` has been added
        # to the pipeline level.
        pipe.transformer.fuse_qkv_projections()
        assert check_qkv_fusion_processors_exist(pipe.transformer), (
            "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
        )
        assert check_qkv_fusion_matches_attn_procs_length(
            pipe.transformer, pipe.transformer.original_attn_processors
        ), "Something wrong with the attention processors concerning the fused QKV projections."

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

        pipe.transformer.unfuse_qkv_projections()
        inputs = self.get_dummy_inputs(device)
        image = pipe(**inputs).images
        image_slice_disabled = image[0, -3:, -3:, -1]

        assert np.allclose(original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3), (
            "Fusion of QKV projections shouldn't affect the outputs."
        )
        assert np.allclose(image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3), (
            "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
        )
        assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2), (
            "Original outputs should match when fused QKV projections are disabled."
        )

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

        inputs = self.get_dummy_inputs(torch_device)

        output_full = pipe(**inputs)[0]

        inputs_with_skip = inputs.copy()
        inputs_with_skip["skip_guidance_layers"] = [0]
        output_skip = pipe(**inputs_with_skip)[0]

        self.assertFalse(
            np.allclose(output_full, output_skip, atol=1e-5), "Outputs should differ when layers are skipped"
        )

        self.assertEqual(output_full.shape, output_skip.shape, "Outputs should have the same shape")

        inputs["num_images_per_prompt"] = 2
        output_full = pipe(**inputs)[0]

        inputs_with_skip = inputs.copy()
        inputs_with_skip["skip_guidance_layers"] = [0]
        output_skip = pipe(**inputs_with_skip)[0]

        self.assertFalse(
            np.allclose(output_full, output_skip, atol=1e-5), "Outputs should differ when layers are skipped"
        )

        self.assertEqual(output_full.shape, output_skip.shape, "Outputs should have the same shape")


@slow
@require_big_accelerator
class StableDiffusion3PipelineSlowTests(unittest.TestCase):
    pipeline_class = StableDiffusion3Pipeline
    repo_id = "stabilityai/stable-diffusion-3-medium-diffusers"

    def setUp(self):
        super().setUp()
        gc.collect()
        backend_empty_cache(torch_device)

    def tearDown(self):
        super().tearDown()
        gc.collect()
        backend_empty_cache(torch_device)

    def get_inputs(self, device, seed=0):
        if str(device).startswith("mps"):
            generator = torch.manual_seed(seed)
        else:
            generator = torch.Generator(device="cpu").manual_seed(seed)

        return {
            "prompt": "A photo of a cat",
            "num_inference_steps": 2,
            "guidance_scale": 5.0,
            "output_type": "np",
            "generator": generator,
        }

    def test_sd3_inference(self):
        pipe = self.pipeline_class.from_pretrained(self.repo_id, torch_dtype=torch.float16)
        pipe.enable_model_cpu_offload(device=torch_device)

        inputs = self.get_inputs(torch_device)

        image = pipe(**inputs).images[0]
        image_slice = image[0, :10, :10]
        expected_slice = np.array(
            [
                0.4648,
                0.4404,
                0.4177,
                0.5063,
                0.4800,
                0.4287,
                0.5425,
                0.5190,
                0.4717,
                0.5430,
                0.5195,
                0.4766,
                0.5361,
                0.5122,
                0.4612,
                0.4871,
                0.4749,
                0.4058,
                0.4756,
                0.4678,
                0.3804,
                0.4832,
                0.4822,
                0.3799,
                0.5103,
                0.5034,
                0.3953,
                0.5073,
                0.4839,
                0.3884,
            ]
        )

        max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten())

        assert max_diff < 1e-4
