# Copyright 2025 The HuggingFace Team.
#
# 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 PIL import Image
from transformers import Gemma2Config, Gemma2Model, GemmaTokenizer

from diffusers import (
    AutoencoderKLWan,
    FlowMatchEulerDiscreteScheduler,
    SanaImageToVideoPipeline,
    SanaVideoTransformer3DModel,
)

from ...testing_utils import (
    backend_empty_cache,
    enable_full_determinism,
    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


enable_full_determinism()


class SanaImageToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
    pipeline_class = SanaImageToVideoPipeline
    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 = frozenset(
        [
            "num_inference_steps",
            "generator",
            "latents",
            "return_dict",
            "callback_on_step_end",
            "callback_on_step_end_tensor_inputs",
        ]
    )
    test_xformers_attention = False

    def get_dummy_components(self):
        torch.manual_seed(0)
        vae = AutoencoderKLWan(
            base_dim=3,
            z_dim=16,
            dim_mult=[1, 1, 1, 1],
            num_res_blocks=1,
            temperal_downsample=[False, True, True],
        )

        torch.manual_seed(0)
        scheduler = FlowMatchEulerDiscreteScheduler()

        torch.manual_seed(0)
        text_encoder_config = Gemma2Config(
            head_dim=16,
            hidden_size=8,
            initializer_range=0.02,
            intermediate_size=64,
            max_position_embeddings=8192,
            model_type="gemma2",
            num_attention_heads=2,
            num_hidden_layers=1,
            num_key_value_heads=2,
            vocab_size=8,
            attn_implementation="eager",
        )
        text_encoder = Gemma2Model(text_encoder_config)
        tokenizer = GemmaTokenizer.from_pretrained("hf-internal-testing/dummy-gemma")

        torch.manual_seed(0)
        transformer = SanaVideoTransformer3DModel(
            in_channels=16,
            out_channels=16,
            num_attention_heads=2,
            attention_head_dim=12,
            num_layers=2,
            num_cross_attention_heads=2,
            cross_attention_head_dim=12,
            cross_attention_dim=24,
            caption_channels=8,
            mlp_ratio=2.5,
            dropout=0.0,
            attention_bias=False,
            sample_size=8,
            patch_size=(1, 2, 2),
            norm_elementwise_affine=False,
            norm_eps=1e-6,
            qk_norm="rms_norm_across_heads",
            rope_max_seq_len=32,
        )

        components = {
            "transformer": transformer,
            "vae": vae,
            "scheduler": scheduler,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
        }
        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)

        # Create a dummy image input (PIL Image)
        image = Image.new("RGB", (32, 32))

        inputs = {
            "image": image,
            "prompt": "",
            "negative_prompt": "",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "height": 32,
            "width": 32,
            "frames": 9,
            "max_sequence_length": 16,
            "output_type": "pt",
            "complex_human_instruction": [],
            "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)
        video = pipe(**inputs).frames
        generated_video = video[0]
        self.assertEqual(generated_video.shape, (9, 3, 32, 32))

    @unittest.skip("Test not supported")
    def test_attention_slicing_forward_pass(self):
        pass

    def test_save_load_local(self, expected_max_difference=5e-4):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        for component in pipe.components.values():
            if hasattr(component, "set_default_attn_processor"):
                component.set_default_attn_processor()
        pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(torch_device)
        torch.manual_seed(0)
        output = pipe(**inputs)[0]

        with tempfile.TemporaryDirectory() as tmpdir:
            pipe.save_pretrained(tmpdir, safe_serialization=False)
            pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
            for component in pipe_loaded.components.values():
                if hasattr(component, "set_default_attn_processor"):
                    component.set_default_attn_processor()
            pipe_loaded.to(torch_device)
            pipe_loaded.set_progress_bar_config(disable=None)

        inputs = self.get_dummy_inputs(torch_device)
        torch.manual_seed(0)
        output_loaded = pipe_loaded(**inputs)[0]

        max_diff = np.abs(output.detach().cpu().numpy() - output_loaded.detach().cpu().numpy()).max()
        self.assertLess(max_diff, expected_max_difference)

    # TODO(aryan): Create a dummy gemma model with smol vocab size
    @unittest.skip(
        "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error."
    )
    def test_inference_batch_consistent(self):
        pass

    @unittest.skip(
        "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error."
    )
    def test_inference_batch_single_identical(self):
        pass

    @unittest.skip("Skipping fp16 test as model is trained with bf16")
    def test_float16_inference(self):
        # Requires higher tolerance as model seems very sensitive to dtype
        super().test_float16_inference(expected_max_diff=0.08)

    @unittest.skip("Skipping fp16 test as model is trained with bf16")
    def test_save_load_float16(self):
        # Requires higher tolerance as model seems very sensitive to dtype
        super().test_save_load_float16(expected_max_diff=0.2)


@slow
@require_torch_accelerator
class SanaVideoPipelineIntegrationTests(unittest.TestCase):
    prompt = "Evening, backlight, side lighting, soft light, high contrast, mid-shot, centered composition, clean solo shot, warm color. A young Caucasian man stands in a forest."

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

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

    @unittest.skip("TODO: test needs to be implemented")
    def test_sana_video_480p(self):
        pass