import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    FlowMatchEulerDiscreteScheduler,
    SD3Transformer2DModel,
    StableDiffusion3Img2ImgPipeline,
)
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
    floats_tensor,
    numpy_cosine_similarity_distance,
    require_big_gpu_with_torch_cuda,
    slow,
    torch_device,
)

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


class StableDiffusion3Img2ImgPipelineFastTests(PipelineLatentTesterMixin, unittest.TestCase, PipelineTesterMixin):
    pipeline_class = StableDiffusion3Img2ImgPipeline
    params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
    required_optional_params = PipelineTesterMixin.required_optional_params
    batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
    image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS

    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,
            joint_attention_dim=32,
            caption_projection_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):
        image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
        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",
            "image": image,
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 5.0,
            "output_type": "np",
            "strength": 0.8,
        }
        return inputs

    def test_stable_diffusion_3_img2img_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_img2img_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_stable_diffusion_3_img2img_prompt_embeds(self):
        pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
        inputs = self.get_dummy_inputs(torch_device)

        output_with_prompt = pipe(**inputs).images[0]

        inputs = self.get_dummy_inputs(torch_device)
        prompt = inputs.pop("prompt")

        do_classifier_free_guidance = inputs["guidance_scale"] > 1
        (
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        ) = pipe.encode_prompt(
            prompt,
            prompt_2=None,
            prompt_3=None,
            do_classifier_free_guidance=do_classifier_free_guidance,
            device=torch_device,
        )
        output_with_embeds = pipe(
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
            **inputs,
        ).images[0]

        max_diff = np.abs(output_with_prompt - output_with_embeds).max()
        assert max_diff < 1e-4

    def test_multi_vae(self):
        pass


@slow
@require_big_gpu_with_torch_cuda
@pytest.mark.big_gpu_with_torch_cuda
class StableDiffusion3Img2ImgPipelineSlowTests(unittest.TestCase):
    pipeline_class = StableDiffusion3Img2ImgPipeline
    repo_id = "stabilityai/stable-diffusion-3-medium-diffusers"

    def setUp(self):
        super().setUp()
        gc.collect()
        torch.cuda.empty_cache()

    def tearDown(self):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

    def get_inputs(self, device, seed=0):
        init_image = load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_img2img/sketch-mountains-input.png"
        )
        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,
            "image": init_image,
        }

    def test_sd3_img2img_inference(self):
        pipe = self.pipeline_class.from_pretrained(self.repo_id, torch_dtype=torch.float16)
        pipe.enable_model_cpu_offload()

        inputs = self.get_inputs(torch_device)

        image = pipe(**inputs).images[0]
        image_slice = image[0, :10, :10]
        expected_slice = np.array(
            [
                0.5435,
                0.4673,
                0.5732,
                0.4438,
                0.3557,
                0.4912,
                0.4331,
                0.3491,
                0.4915,
                0.4287,
                0.3477,
                0.4849,
                0.4355,
                0.3469,
                0.4871,
                0.4431,
                0.3538,
                0.4912,
                0.4521,
                0.3643,
                0.5059,
                0.4587,
                0.3730,
                0.5166,
                0.4685,
                0.3845,
                0.5264,
                0.4746,
                0.3914,
                0.5342,
            ]
        )

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

        assert max_diff < 1e-4, f"Outputs are not close enough, got {max_diff}"
