# 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 unittest

import torch
from transformers import AutoTokenizer, Gemma3ForConditionalGeneration

from diffusers import (
    AutoencoderKLLTX2Audio,
    AutoencoderKLLTX2Video,
    FlowMatchEulerDiscreteScheduler,
    LTX2Pipeline,
    LTX2VideoTransformer3DModel,
)
from diffusers.pipelines.ltx2 import LTX2TextConnectors
from diffusers.pipelines.ltx2.vocoder import LTX2Vocoder

from ...testing_utils import enable_full_determinism
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 LTX2PipelineFastTests(PipelineTesterMixin, unittest.TestCase):
    pipeline_class = LTX2Pipeline
    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",
            "audio_latents",
            "output_type",
            "return_dict",
            "callback_on_step_end",
            "callback_on_step_end_tensor_inputs",
        ]
    )
    test_attention_slicing = False
    test_xformers_attention = False
    supports_dduf = False

    base_text_encoder_ckpt_id = "hf-internal-testing/tiny-gemma3"

    def get_dummy_components(self):
        tokenizer = AutoTokenizer.from_pretrained(self.base_text_encoder_ckpt_id)
        text_encoder = Gemma3ForConditionalGeneration.from_pretrained(self.base_text_encoder_ckpt_id)

        torch.manual_seed(0)
        transformer = LTX2VideoTransformer3DModel(
            in_channels=4,
            out_channels=4,
            patch_size=1,
            patch_size_t=1,
            num_attention_heads=2,
            attention_head_dim=8,
            cross_attention_dim=16,
            audio_in_channels=4,
            audio_out_channels=4,
            audio_num_attention_heads=2,
            audio_attention_head_dim=4,
            audio_cross_attention_dim=8,
            num_layers=2,
            qk_norm="rms_norm_across_heads",
            caption_channels=text_encoder.config.text_config.hidden_size,
            rope_double_precision=False,
            rope_type="split",
        )

        torch.manual_seed(0)
        connectors = LTX2TextConnectors(
            caption_channels=text_encoder.config.text_config.hidden_size,
            text_proj_in_factor=text_encoder.config.text_config.num_hidden_layers + 1,
            video_connector_num_attention_heads=4,
            video_connector_attention_head_dim=8,
            video_connector_num_layers=1,
            video_connector_num_learnable_registers=None,
            audio_connector_num_attention_heads=4,
            audio_connector_attention_head_dim=8,
            audio_connector_num_layers=1,
            audio_connector_num_learnable_registers=None,
            connector_rope_base_seq_len=32,
            rope_theta=10000.0,
            rope_double_precision=False,
            causal_temporal_positioning=False,
            rope_type="split",
        )

        torch.manual_seed(0)
        vae = AutoencoderKLLTX2Video(
            in_channels=3,
            out_channels=3,
            latent_channels=4,
            block_out_channels=(8,),
            decoder_block_out_channels=(8,),
            layers_per_block=(1,),
            decoder_layers_per_block=(1, 1),
            spatio_temporal_scaling=(True,),
            decoder_spatio_temporal_scaling=(True,),
            decoder_inject_noise=(False, False),
            downsample_type=("spatial",),
            upsample_residual=(False,),
            upsample_factor=(1,),
            timestep_conditioning=False,
            patch_size=1,
            patch_size_t=1,
            encoder_causal=True,
            decoder_causal=False,
        )
        vae.use_framewise_encoding = False
        vae.use_framewise_decoding = False

        torch.manual_seed(0)
        audio_vae = AutoencoderKLLTX2Audio(
            base_channels=4,
            output_channels=2,
            ch_mult=(1,),
            num_res_blocks=1,
            attn_resolutions=None,
            in_channels=2,
            resolution=32,
            latent_channels=2,
            norm_type="pixel",
            causality_axis="height",
            dropout=0.0,
            mid_block_add_attention=False,
            sample_rate=16000,
            mel_hop_length=160,
            is_causal=True,
            mel_bins=8,
        )

        torch.manual_seed(0)
        vocoder = LTX2Vocoder(
            in_channels=audio_vae.config.output_channels * audio_vae.config.mel_bins,
            hidden_channels=32,
            out_channels=2,
            upsample_kernel_sizes=[4, 4],
            upsample_factors=[2, 2],
            resnet_kernel_sizes=[3],
            resnet_dilations=[[1, 3, 5]],
            leaky_relu_negative_slope=0.1,
            output_sampling_rate=16000,
        )

        scheduler = FlowMatchEulerDiscreteScheduler()

        components = {
            "transformer": transformer,
            "vae": vae,
            "audio_vae": audio_vae,
            "scheduler": scheduler,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "connectors": connectors,
            "vocoder": vocoder,
            "processor": 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 robot dancing",
            "negative_prompt": "",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 1.0,
            "height": 32,
            "width": 32,
            "num_frames": 5,
            "frame_rate": 25.0,
            "max_sequence_length": 16,
            "output_type": "pt",
        }

        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)
        output = pipe(**inputs)
        video = output.frames
        audio = output.audio

        self.assertEqual(video.shape, (1, 5, 3, 32, 32))
        self.assertEqual(audio.shape[0], 1)
        self.assertEqual(audio.shape[1], components["vocoder"].config.out_channels)

        # fmt: off
        expected_video_slice = torch.tensor(
            [
                0.4331, 0.6203, 0.3245, 0.7294, 0.4822, 0.5703, 0.2999, 0.7700, 0.4961, 0.4242, 0.4581, 0.4351, 0.1137, 0.4437, 0.6304, 0.3184
            ]
        )
        expected_audio_slice = torch.tensor(
            [
                0.0263, 0.0528, 0.1217, 0.1104, 0.1632, 0.1072, 0.1789, 0.0949, 0.0672, -0.0069, 0.0688, 0.0097, 0.0808, 0.1231, 0.0986, 0.0739
            ]
        )
        # fmt: on

        video = video.flatten()
        audio = audio.flatten()
        generated_video_slice = torch.cat([video[:8], video[-8:]])
        generated_audio_slice = torch.cat([audio[:8], audio[-8:]])

        assert torch.allclose(expected_video_slice, generated_video_slice, atol=1e-4, rtol=1e-4)
        assert torch.allclose(expected_audio_slice, generated_audio_slice, atol=1e-4, rtol=1e-4)

    def test_two_stages_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)
        inputs["output_type"] = "latent"
        first_stage_output = pipe(**inputs)
        video_latent = first_stage_output.frames
        audio_latent = first_stage_output.audio

        self.assertEqual(video_latent.shape, (1, 4, 3, 16, 16))
        self.assertEqual(audio_latent.shape, (1, 2, 5, 2))
        self.assertEqual(audio_latent.shape[1], components["vocoder"].config.out_channels)

        inputs["latents"] = video_latent
        inputs["audio_latents"] = audio_latent
        inputs["output_type"] = "pt"
        second_stage_output = pipe(**inputs)
        video = second_stage_output.frames
        audio = second_stage_output.audio

        self.assertEqual(video.shape, (1, 5, 3, 32, 32))
        self.assertEqual(audio.shape[0], 1)
        self.assertEqual(audio.shape[1], components["vocoder"].config.out_channels)

        # fmt: off
        expected_video_slice = torch.tensor(
            [
                0.5514, 0.5943, 0.4260, 0.5971, 0.4306, 0.6369, 0.3124, 0.6964, 0.5419, 0.2412, 0.3882, 0.4504, 0.1941, 0.3404, 0.6037, 0.2464
            ]
        )
        expected_audio_slice = torch.tensor(
            [
                0.0252, 0.0526, 0.1211, 0.1119, 0.1638, 0.1042, 0.1776, 0.0948, 0.0672, -0.0069, 0.0688, 0.0097, 0.0808, 0.1231, 0.0986, 0.0739
            ]
        )
        # fmt: on

        video = video.flatten()
        audio = audio.flatten()
        generated_video_slice = torch.cat([video[:8], video[-8:]])
        generated_audio_slice = torch.cat([audio[:8], audio[-8:]])

        assert torch.allclose(expected_video_slice, generated_video_slice, atol=1e-4, rtol=1e-4)
        assert torch.allclose(expected_audio_slice, generated_audio_slice, atol=1e-4, rtol=1e-4)

    def test_inference_batch_single_identical(self):
        self._test_inference_batch_single_identical(batch_size=2, expected_max_diff=2e-2)
