# coding=utf-8
# Copyright 2024 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 unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    TextToVideoSDPipeline,
    UNet3DConditionModel,
)
from diffusers.utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    numpy_cosine_similarity_distance,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, SDFunctionTesterMixin


enable_full_determinism()


@skip_mps
class TextToVideoSDPipelineFastTests(PipelineTesterMixin, SDFunctionTesterMixin, unittest.TestCase):
    pipeline_class = TextToVideoSDPipeline
    params = TEXT_TO_IMAGE_PARAMS
    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
    # No `output_type`.
    required_optional_params = frozenset(
        [
            "num_inference_steps",
            "generator",
            "latents",
            "return_dict",
            "callback",
            "callback_steps",
        ]
    )

    def get_dummy_components(self):
        torch.manual_seed(0)
        unet = UNet3DConditionModel(
            block_out_channels=(4, 8),
            layers_per_block=1,
            sample_size=32,
            in_channels=4,
            out_channels=4,
            down_block_types=("CrossAttnDownBlock3D", "DownBlock3D"),
            up_block_types=("UpBlock3D", "CrossAttnUpBlock3D"),
            cross_attention_dim=4,
            attention_head_dim=4,
            norm_num_groups=2,
        )
        scheduler = DDIMScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_one=False,
        )
        torch.manual_seed(0)
        vae = AutoencoderKL(
            block_out_channels=(8,),
            in_channels=3,
            out_channels=3,
            down_block_types=["DownEncoderBlock2D"],
            up_block_types=["UpDecoderBlock2D"],
            latent_channels=4,
            sample_size=32,
            norm_num_groups=2,
        )
        torch.manual_seed(0)
        text_encoder_config = CLIPTextConfig(
            bos_token_id=0,
            eos_token_id=2,
            hidden_size=4,
            intermediate_size=16,
            layer_norm_eps=1e-05,
            num_attention_heads=2,
            num_hidden_layers=2,
            pad_token_id=1,
            vocab_size=1000,
            hidden_act="gelu",
            projection_dim=32,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        components = {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "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)
        inputs = {
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "pt",
        }
        return inputs

    def test_dict_tuple_outputs_equivalent(self):
        expected_slice = None
        if torch_device == "cpu":
            expected_slice = np.array([0.4903, 0.5649, 0.5504, 0.5179, 0.4821, 0.5466, 0.4131, 0.5052, 0.5077])
        return super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)

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

        inputs = self.get_dummy_inputs(device)
        inputs["output_type"] = "np"
        frames = sd_pipe(**inputs).frames

        image_slice = frames[0][0][-3:, -3:, -1]

        assert frames[0][0].shape == (32, 32, 3)
        expected_slice = np.array([0.7537, 0.1752, 0.6157, 0.5508, 0.4240, 0.4110, 0.4838, 0.5648, 0.5094])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

    @unittest.skipIf(torch_device != "cuda", reason="Feature isn't heavily used. Test in CUDA environment only.")
    def test_attention_slicing_forward_pass(self):
        self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False, expected_max_diff=3e-3)

    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available(),
        reason="XFormers attention is only available with CUDA and `xformers` installed",
    )
    def test_xformers_attention_forwardGenerator_pass(self):
        self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False, expected_max_diff=1e-2)

    # (todo): sayakpaul
    @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline.")
    def test_inference_batch_consistent(self):
        pass

    # (todo): sayakpaul
    @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline.")
    def test_inference_batch_single_identical(self):
        pass

    @unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline.")
    def test_num_images_per_prompt(self):
        pass

    def test_progress_bar(self):
        return super().test_progress_bar()


@slow
@skip_mps
@require_torch_gpu
class TextToVideoSDPipelineSlowTests(unittest.TestCase):
    def setUp(self):
        # clean up the VRAM before each test
        super().setUp()
        gc.collect()
        torch.cuda.empty_cache()

    def tearDown(self):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

    def test_two_step_model(self):
        expected_video = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/video_2step.npy"
        )

        pipe = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b")
        pipe = pipe.to(torch_device)

        prompt = "Spiderman is surfing"
        generator = torch.Generator(device="cpu").manual_seed(0)

        video_frames = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").frames
        assert numpy_cosine_similarity_distance(expected_video.flatten(), video_frames.flatten()) < 1e-4

    def test_two_step_model_with_freeu(self):
        expected_video = []

        pipe = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b")
        pipe = pipe.to(torch_device)

        prompt = "Spiderman is surfing"
        generator = torch.Generator(device="cpu").manual_seed(0)

        pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)
        video_frames = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").frames
        video = video_frames[0, 0, -3:, -3:, -1].flatten()

        expected_video = [0.3643, 0.3455, 0.3831, 0.3923, 0.2978, 0.3247, 0.3278, 0.3201, 0.3475]

        assert np.abs(expected_video - video).mean() < 5e-2