import unittest

import numpy as np
import torch
from transformers import AutoConfig, AutoTokenizer, T5EncoderModel

from diffusers import AutoencoderKL, ChromaPipeline, ChromaTransformer2DModel, FlowMatchEulerDiscreteScheduler

from ...testing_utils import torch_device
from ..test_pipelines_common import FluxIPAdapterTesterMixin, PipelineTesterMixin, check_qkv_fused_layers_exist


class ChromaPipelineFastTests(
    unittest.TestCase,
    PipelineTesterMixin,
    FluxIPAdapterTesterMixin,
):
    pipeline_class = ChromaPipeline
    params = frozenset(["prompt", "height", "width", "guidance_scale", "prompt_embeds"])
    batch_params = frozenset(["prompt"])

    # there is no xformers processor for Flux
    test_xformers_attention = False
    test_layerwise_casting = True
    test_group_offloading = True

    def get_dummy_components(self, num_layers: int = 1, num_single_layers: int = 1):
        torch.manual_seed(0)
        transformer = ChromaTransformer2DModel(
            patch_size=1,
            in_channels=4,
            num_layers=num_layers,
            num_single_layers=num_single_layers,
            attention_head_dim=16,
            num_attention_heads=2,
            joint_attention_dim=32,
            axes_dims_rope=[4, 4, 8],
            approximator_hidden_dim=32,
            approximator_layers=1,
            approximator_num_channels=16,
        )

        torch.manual_seed(0)
        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
        text_encoder = T5EncoderModel(config)

        tokenizer = 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=1,
            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,
            "tokenizer": tokenizer,
            "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",
            "negative_prompt": "bad, ugly",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 5.0,
            "height": 8,
            "width": 8,
            "max_sequence_length": 48,
            "output_type": "np",
        }
        return inputs

    def test_chroma_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"] = "a 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
        # For some reasons, they don't show large differences
        assert max_diff > 1e-6

    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()
        self.assertTrue(
            check_qkv_fused_layers_exist(pipe.transformer, ["to_qkv"]),
            ("Something wrong with the fused attention layers. Expected all the attention projections to be fused."),
        )

        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_chroma_image_output_shape(self):
        pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
        inputs = self.get_dummy_inputs(torch_device)

        height_width_pairs = [(32, 32), (72, 57)]
        for height, width in height_width_pairs:
            expected_height = height - height % (pipe.vae_scale_factor * 2)
            expected_width = width - width % (pipe.vae_scale_factor * 2)

            inputs.update({"height": height, "width": width})
            image = pipe(**inputs).images[0]
            output_height, output_width, _ = image.shape
            assert (output_height, output_width) == (expected_height, expected_width)
