# Copyright 2024 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 inspect
import os
import tempfile
import unittest

import numpy as np
import torch
from transformers import AutoTokenizer, T5Config, T5EncoderModel

from diffusers import AllegroPipeline, AllegroTransformer3DModel, AutoencoderKLAllegro, DDIMScheduler
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    numpy_cosine_similarity_distance,
    require_hf_hub_version_greater,
    require_torch_gpu,
    require_transformers_version_greater,
    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, to_np


enable_full_determinism()


class AllegroPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
    pipeline_class = AllegroPipeline
    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)
        transformer = AllegroTransformer3DModel(
            num_attention_heads=2,
            attention_head_dim=12,
            in_channels=4,
            out_channels=4,
            num_layers=1,
            cross_attention_dim=24,
            sample_width=8,
            sample_height=8,
            sample_frames=8,
            caption_channels=24,
        )

        torch.manual_seed(0)
        vae = AutoencoderKLAllegro(
            in_channels=3,
            out_channels=3,
            down_block_types=(
                "AllegroDownBlock3D",
                "AllegroDownBlock3D",
                "AllegroDownBlock3D",
                "AllegroDownBlock3D",
            ),
            up_block_types=(
                "AllegroUpBlock3D",
                "AllegroUpBlock3D",
                "AllegroUpBlock3D",
                "AllegroUpBlock3D",
            ),
            block_out_channels=(8, 8, 8, 8),
            latent_channels=4,
            layers_per_block=1,
            norm_num_groups=2,
            temporal_compression_ratio=4,
        )

        # TODO(aryan): Only for now, since VAE decoding without tiling is not yet implemented here
        vae.enable_tiling()

        torch.manual_seed(0)
        scheduler = DDIMScheduler()

        text_encoder_config = T5Config(
            **{
                "d_ff": 37,
                "d_kv": 8,
                "d_model": 24,
                "num_decoder_layers": 2,
                "num_heads": 4,
                "num_layers": 2,
                "relative_attention_num_buckets": 8,
                "vocab_size": 1103,
            }
        )
        text_encoder = T5EncoderModel(text_encoder_config)
        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")

        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)

        inputs = {
            "prompt": "dance monkey",
            "negative_prompt": "",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "height": 16,
            "width": 16,
            "num_frames": 8,
            "max_sequence_length": 16,
            "output_type": "pt",
        }

        return inputs

    @unittest.skip("Decoding without tiling is not yet implemented")
    def test_save_load_local(self):
        pass

    @unittest.skip("Decoding without tiling is not yet implemented")
    def test_save_load_optional_components(self):
        pass

    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, (8, 3, 16, 16))
        expected_video = torch.randn(8, 3, 16, 16)
        max_diff = np.abs(generated_video - expected_video).max()
        self.assertLessEqual(max_diff, 1e10)

    def test_callback_inputs(self):
        sig = inspect.signature(self.pipeline_class.__call__)
        has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters
        has_callback_step_end = "callback_on_step_end" in sig.parameters

        if not (has_callback_tensor_inputs and has_callback_step_end):
            return

        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        pipe = pipe.to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        self.assertTrue(
            hasattr(pipe, "_callback_tensor_inputs"),
            f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
        )

        def callback_inputs_subset(pipe, i, t, callback_kwargs):
            # iterate over callback args
            for tensor_name, tensor_value in callback_kwargs.items():
                # check that we're only passing in allowed tensor inputs
                assert tensor_name in pipe._callback_tensor_inputs

            return callback_kwargs

        def callback_inputs_all(pipe, i, t, callback_kwargs):
            for tensor_name in pipe._callback_tensor_inputs:
                assert tensor_name in callback_kwargs

            # iterate over callback args
            for tensor_name, tensor_value in callback_kwargs.items():
                # check that we're only passing in allowed tensor inputs
                assert tensor_name in pipe._callback_tensor_inputs

            return callback_kwargs

        inputs = self.get_dummy_inputs(torch_device)

        # Test passing in a subset
        inputs["callback_on_step_end"] = callback_inputs_subset
        inputs["callback_on_step_end_tensor_inputs"] = ["latents"]
        output = pipe(**inputs)[0]

        # Test passing in a everything
        inputs["callback_on_step_end"] = callback_inputs_all
        inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
        output = pipe(**inputs)[0]

        def callback_inputs_change_tensor(pipe, i, t, callback_kwargs):
            is_last = i == (pipe.num_timesteps - 1)
            if is_last:
                callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"])
            return callback_kwargs

        inputs["callback_on_step_end"] = callback_inputs_change_tensor
        inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
        output = pipe(**inputs)[0]
        assert output.abs().sum() < 1e10

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

    def test_attention_slicing_forward_pass(
        self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3
    ):
        if not self.test_attention_slicing:
            return

        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)

        generator_device = "cpu"
        inputs = self.get_dummy_inputs(generator_device)
        output_without_slicing = pipe(**inputs)[0]

        pipe.enable_attention_slicing(slice_size=1)
        inputs = self.get_dummy_inputs(generator_device)
        output_with_slicing1 = pipe(**inputs)[0]

        pipe.enable_attention_slicing(slice_size=2)
        inputs = self.get_dummy_inputs(generator_device)
        output_with_slicing2 = pipe(**inputs)[0]

        if test_max_difference:
            max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max()
            max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max()
            self.assertLess(
                max(max_diff1, max_diff2),
                expected_max_diff,
                "Attention slicing should not affect the inference results",
            )

    # TODO(aryan)
    @unittest.skip("Decoding without tiling is not yet implemented.")
    def test_vae_tiling(self, expected_diff_max: float = 0.2):
        generator_device = "cpu"
        components = self.get_dummy_components()

        pipe = self.pipeline_class(**components)
        pipe.to("cpu")
        pipe.set_progress_bar_config(disable=None)

        # Without tiling
        inputs = self.get_dummy_inputs(generator_device)
        inputs["height"] = inputs["width"] = 128
        output_without_tiling = pipe(**inputs)[0]

        # With tiling
        pipe.vae.enable_tiling(
            tile_sample_min_height=96,
            tile_sample_min_width=96,
            tile_overlap_factor_height=1 / 12,
            tile_overlap_factor_width=1 / 12,
        )
        inputs = self.get_dummy_inputs(generator_device)
        inputs["height"] = inputs["width"] = 128
        output_with_tiling = pipe(**inputs)[0]

        self.assertLess(
            (to_np(output_without_tiling) - to_np(output_with_tiling)).max(),
            expected_diff_max,
            "VAE tiling should not affect the inference results",
        )

    @require_hf_hub_version_greater("0.26.5")
    @require_transformers_version_greater("4.47.1")
    def test_save_load_dduf(self):
        # reimplement because it needs `enable_tiling()` on the loaded pipe.
        from huggingface_hub import export_folder_as_dduf

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

        inputs = self.get_dummy_inputs(device="cpu")
        inputs.pop("generator")
        inputs["generator"] = torch.manual_seed(0)

        pipeline_out = pipe(**inputs)[0].cpu()

        with tempfile.TemporaryDirectory() as tmpdir:
            dduf_filename = os.path.join(tmpdir, f"{pipe.__class__.__name__.lower()}.dduf")
            pipe.save_pretrained(tmpdir, safe_serialization=True)
            export_folder_as_dduf(dduf_filename, folder_path=tmpdir)
            loaded_pipe = self.pipeline_class.from_pretrained(tmpdir, dduf_file=dduf_filename).to(torch_device)

        loaded_pipe.vae.enable_tiling()
        inputs["generator"] = torch.manual_seed(0)
        loaded_pipeline_out = loaded_pipe(**inputs)[0].cpu()

        assert np.allclose(pipeline_out, loaded_pipeline_out)


@slow
@require_torch_gpu
class AllegroPipelineIntegrationTests(unittest.TestCase):
    prompt = "A painting of a squirrel eating a burger."

    def setUp(self):
        super().setUp()
        gc.collect()
        torch.cuda.empty_cache()

    def tearDown(self):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

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

        pipe = AllegroPipeline.from_pretrained("rhymes-ai/Allegro", torch_dtype=torch.float16)
        pipe.enable_model_cpu_offload()
        prompt = self.prompt

        videos = pipe(
            prompt=prompt,
            height=720,
            width=1280,
            num_frames=88,
            generator=generator,
            num_inference_steps=2,
            output_type="pt",
        ).frames

        video = videos[0]
        expected_video = torch.randn(1, 88, 720, 1280, 3).numpy()

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