# coding=utf-8
# Copyright 2025 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 gc
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDPMScheduler, HunyuanDiT2DModel, HunyuanDiTPipeline

from ...testing_utils import (
    backend_empty_cache,
    enable_full_determinism,
    numpy_cosine_similarity_distance,
    require_torch_accelerator,
    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 (
    PipelineTesterMixin,
    check_qkv_fusion_matches_attn_procs_length,
    check_qkv_fusion_processors_exist,
    to_np,
)


enable_full_determinism()


class HunyuanDiTPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
    pipeline_class = HunyuanDiTPipeline
    params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS

    required_optional_params = PipelineTesterMixin.required_optional_params
    test_layerwise_casting = True

    def get_dummy_components(self):
        torch.manual_seed(0)
        transformer = HunyuanDiT2DModel(
            sample_size=16,
            num_layers=2,
            patch_size=2,
            attention_head_dim=8,
            num_attention_heads=3,
            in_channels=4,
            cross_attention_dim=32,
            cross_attention_dim_t5=32,
            pooled_projection_dim=16,
            hidden_size=24,
            activation_fn="gelu-approximate",
        )
        torch.manual_seed(0)
        vae = AutoencoderKL()

        scheduler = DDPMScheduler()
        text_encoder = BertModel.from_pretrained("hf-internal-testing/tiny-random-BertModel")
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel")
        torch.manual_seed(0)
        config = AutoConfig.from_pretrained("hf-internal-testing/tiny-random-t5")
        text_encoder_2 = T5EncoderModel(config)
        tokenizer_2 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")

        components = {
            "transformer": transformer.eval(),
            "vae": vae.eval(),
            "scheduler": scheduler,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_encoder_2": text_encoder_2,
            "tokenizer_2": tokenizer_2,
            "safety_checker": None,
            "feature_extractor": None,
        }
        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,
            "guidance_scale": 5.0,
            "output_type": "np",
            "use_resolution_binning": False,
        }
        return inputs

    def test_inference(self):
        device = "cpu"

        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.to(device)
        pipe.set_progress_bar_config(disable=None)

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

        self.assertEqual(image.shape, (1, 16, 16, 3))
        expected_slice = np.array(
            [0.56939435, 0.34541583, 0.35915792, 0.46489206, 0.38775963, 0.45004836, 0.5957267, 0.59481275, 0.33287364]
        )
        max_diff = np.abs(image_slice.flatten() - expected_slice).max()
        self.assertLessEqual(max_diff, 1e-3)

    @unittest.skip("The HunyuanDiT Attention pooling layer does not support sequential CPU offloading.")
    def test_sequential_cpu_offload_forward_pass(self):
        # TODO(YiYi) need to fix later
        # This is because it instantiates it's attention layer from torch.nn.MultiheadAttention, which calls to
        # `torch.nn.functional.multi_head_attention_forward` with the weights and bias. Since the hook is never
        # triggered with a forward pass call, the weights stay on the CPU. There are more examples where we skip
        # this test because of MHA (example: HunyuanVideo Framepack)
        pass

    @unittest.skip("The HunyuanDiT Attention pooling layer does not support sequential CPU offloading.")
    def test_sequential_offload_forward_pass_twice(self):
        # TODO(YiYi) need to fix later
        # This is because it instantiates it's attention layer from torch.nn.MultiheadAttention, which calls to
        # `torch.nn.functional.multi_head_attention_forward` with the weights and bias. Since the hook is never
        # triggered with a forward pass call, the weights stay on the CPU. There are more examples where we skip
        # this test because of MHA (example: HunyuanVideo Framepack)
        pass

    def test_inference_batch_single_identical(self):
        self._test_inference_batch_single_identical(
            expected_max_diff=1e-3,
        )

    def test_feed_forward_chunking(self):
        device = "cpu"

        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe.to(device)
        pipe.set_progress_bar_config(disable=None)

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

        pipe.transformer.enable_forward_chunking(chunk_size=1, dim=0)
        inputs = self.get_dummy_inputs(device)
        image = pipe(**inputs).images
        image_slice_chunking = image[0, -3:, -3:, -1]

        max_diff = np.abs(to_np(image_slice_no_chunking) - to_np(image_slice_chunking)).max()
        self.assertLess(max_diff, 1e-4)

    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)
        inputs["return_dict"] = False
        image = pipe(**inputs)[0]
        original_image_slice = image[0, -3:, -3:, -1]

        pipe.transformer.fuse_qkv_projections()
        # 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)
        inputs["return_dict"] = False
        image_fused = pipe(**inputs)[0]
        image_slice_fused = image_fused[0, -3:, -3:, -1]

        pipe.transformer.unfuse_qkv_projections()
        inputs = self.get_dummy_inputs(device)
        inputs["return_dict"] = False
        image_disabled = pipe(**inputs)[0]
        image_slice_disabled = image_disabled[0, -3:, -3:, -1]

        assert np.allclose(original_image_slice, image_slice_fused, atol=1e-2, rtol=1e-2), (
            "Fusion of QKV projections shouldn't affect the outputs."
        )
        assert np.allclose(image_slice_fused, image_slice_disabled, atol=1e-2, rtol=1e-2), (
            "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."
        )

    @unittest.skip(
        "Test not supported as `encode_prompt` is called two times separately which deivates from about 99% of the pipelines we have."
    )
    def test_encode_prompt_works_in_isolation(self):
        pass

    def test_save_load_optional_components(self):
        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)

        prompt = inputs["prompt"]
        generator = inputs["generator"]
        num_inference_steps = inputs["num_inference_steps"]
        output_type = inputs["output_type"]

        (
            prompt_embeds,
            negative_prompt_embeds,
            prompt_attention_mask,
            negative_prompt_attention_mask,
        ) = pipe.encode_prompt(prompt, device=torch_device, dtype=torch.float32, text_encoder_index=0)

        (
            prompt_embeds_2,
            negative_prompt_embeds_2,
            prompt_attention_mask_2,
            negative_prompt_attention_mask_2,
        ) = pipe.encode_prompt(
            prompt,
            device=torch_device,
            dtype=torch.float32,
            text_encoder_index=1,
        )

        # inputs with prompt converted to embeddings
        inputs = {
            "prompt_embeds": prompt_embeds,
            "prompt_attention_mask": prompt_attention_mask,
            "negative_prompt_embeds": negative_prompt_embeds,
            "negative_prompt_attention_mask": negative_prompt_attention_mask,
            "prompt_embeds_2": prompt_embeds_2,
            "prompt_attention_mask_2": prompt_attention_mask_2,
            "negative_prompt_embeds_2": negative_prompt_embeds_2,
            "negative_prompt_attention_mask_2": negative_prompt_attention_mask_2,
            "generator": generator,
            "num_inference_steps": num_inference_steps,
            "output_type": output_type,
            "use_resolution_binning": False,
        }

        # set all optional components to None
        for optional_component in pipe._optional_components:
            setattr(pipe, optional_component, None)

        output = pipe(**inputs)[0]

        with tempfile.TemporaryDirectory() as tmpdir:
            pipe.save_pretrained(tmpdir)
            pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
            pipe_loaded.to(torch_device)
            pipe_loaded.set_progress_bar_config(disable=None)

        for optional_component in pipe._optional_components:
            self.assertTrue(
                getattr(pipe_loaded, optional_component) is None,
                f"`{optional_component}` did not stay set to None after loading.",
            )

        inputs = self.get_dummy_inputs(torch_device)

        generator = inputs["generator"]
        num_inference_steps = inputs["num_inference_steps"]
        output_type = inputs["output_type"]

        # inputs with prompt converted to embeddings
        inputs = {
            "prompt_embeds": prompt_embeds,
            "prompt_attention_mask": prompt_attention_mask,
            "negative_prompt_embeds": negative_prompt_embeds,
            "negative_prompt_attention_mask": negative_prompt_attention_mask,
            "prompt_embeds_2": prompt_embeds_2,
            "prompt_attention_mask_2": prompt_attention_mask_2,
            "negative_prompt_embeds_2": negative_prompt_embeds_2,
            "negative_prompt_attention_mask_2": negative_prompt_attention_mask_2,
            "generator": generator,
            "num_inference_steps": num_inference_steps,
            "output_type": output_type,
            "use_resolution_binning": False,
        }

        output_loaded = pipe_loaded(**inputs)[0]

        max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
        self.assertLess(max_diff, 1e-4)


@slow
@require_torch_accelerator
class HunyuanDiTPipelineIntegrationTests(unittest.TestCase):
    prompt = "一个宇航员在骑马"

    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 test_hunyuan_dit_1024(self):
        generator = torch.Generator("cpu").manual_seed(0)

        pipe = HunyuanDiTPipeline.from_pretrained(
            "XCLiu/HunyuanDiT-0523", revision="refs/pr/2", torch_dtype=torch.float16
        )
        pipe.enable_model_cpu_offload(device=torch_device)
        prompt = self.prompt

        image = pipe(
            prompt=prompt, height=1024, width=1024, generator=generator, num_inference_steps=2, output_type="np"
        ).images

        image_slice = image[0, -3:, -3:, -1]
        expected_slice = np.array(
            [0.48388672, 0.33789062, 0.30737305, 0.47875977, 0.25097656, 0.30029297, 0.4440918, 0.26953125, 0.30078125]
        )

        max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice)
        assert max_diff < 1e-3, f"Max diff is too high. got {image_slice.flatten()}"
