# coding=utf-8
# Copyright 2023 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 Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


torch.backends.cuda.matmul.allow_tf32 = False


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

    @property
    def num_embed(self):
        return 12

    @property
    def num_embeds_ada_norm(self):
        return 12

    @property
    def text_embedder_hidden_size(self):
        return 32

    @property
    def dummy_vqvae(self):
        torch.manual_seed(0)
        model = VQModel(
            block_out_channels=[32, 64],
            in_channels=3,
            out_channels=3,
            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
            latent_channels=3,
            num_vq_embeddings=self.num_embed,
            vq_embed_dim=3,
        )
        return model

    @property
    def dummy_tokenizer(self):
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
        return tokenizer

    @property
    def dummy_text_encoder(self):
        torch.manual_seed(0)
        config = CLIPTextConfig(
            bos_token_id=0,
            eos_token_id=2,
            hidden_size=self.text_embedder_hidden_size,
            intermediate_size=37,
            layer_norm_eps=1e-05,
            num_attention_heads=4,
            num_hidden_layers=5,
            pad_token_id=1,
            vocab_size=1000,
        )
        return CLIPTextModel(config)

    @property
    def dummy_transformer(self):
        torch.manual_seed(0)

        height = 12
        width = 12

        model_kwargs = {
            "attention_bias": True,
            "cross_attention_dim": 32,
            "attention_head_dim": height * width,
            "num_attention_heads": 1,
            "num_vector_embeds": self.num_embed,
            "num_embeds_ada_norm": self.num_embeds_ada_norm,
            "norm_num_groups": 32,
            "sample_size": width,
            "activation_fn": "geglu-approximate",
        }

        model = Transformer2DModel(**model_kwargs)
        return model

    def test_vq_diffusion(self):
        device = "cpu"

        vqvae = self.dummy_vqvae
        text_encoder = self.dummy_text_encoder
        tokenizer = self.dummy_tokenizer
        transformer = self.dummy_transformer
        scheduler = VQDiffusionScheduler(self.num_embed)
        learned_classifier_free_sampling_embeddings = LearnedClassifierFreeSamplingEmbeddings(learnable=False)

        pipe = VQDiffusionPipeline(
            vqvae=vqvae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            transformer=transformer,
            scheduler=scheduler,
            learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings,
        )
        pipe = pipe.to(device)
        pipe.set_progress_bar_config(disable=None)

        prompt = "teddy bear playing in the pool"

        generator = torch.Generator(device=device).manual_seed(0)
        output = pipe([prompt], generator=generator, num_inference_steps=2, output_type="np")
        image = output.images

        generator = torch.Generator(device=device).manual_seed(0)
        image_from_tuple = pipe(
            [prompt], generator=generator, output_type="np", return_dict=False, num_inference_steps=2
        )[0]

        image_slice = image[0, -3:, -3:, -1]
        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 24, 24, 3)

        expected_slice = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992])

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

    def test_vq_diffusion_classifier_free_sampling(self):
        device = "cpu"

        vqvae = self.dummy_vqvae
        text_encoder = self.dummy_text_encoder
        tokenizer = self.dummy_tokenizer
        transformer = self.dummy_transformer
        scheduler = VQDiffusionScheduler(self.num_embed)
        learned_classifier_free_sampling_embeddings = LearnedClassifierFreeSamplingEmbeddings(
            learnable=True, hidden_size=self.text_embedder_hidden_size, length=tokenizer.model_max_length
        )

        pipe = VQDiffusionPipeline(
            vqvae=vqvae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            transformer=transformer,
            scheduler=scheduler,
            learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings,
        )
        pipe = pipe.to(device)
        pipe.set_progress_bar_config(disable=None)

        prompt = "teddy bear playing in the pool"

        generator = torch.Generator(device=device).manual_seed(0)
        output = pipe([prompt], generator=generator, num_inference_steps=2, output_type="np")
        image = output.images

        generator = torch.Generator(device=device).manual_seed(0)
        image_from_tuple = pipe(
            [prompt], generator=generator, output_type="np", return_dict=False, num_inference_steps=2
        )[0]

        image_slice = image[0, -3:, -3:, -1]
        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 24, 24, 3)

        expected_slice = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 2.0
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2


@nightly
@require_torch_gpu
class VQDiffusionPipelineIntegrationTests(unittest.TestCase):
    def tearDown(self):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

    def test_vq_diffusion_classifier_free_sampling(self):
        expected_image = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"
        )

        pipeline = VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq")
        pipeline = pipeline.to(torch_device)
        pipeline.set_progress_bar_config(disable=None)

        # requires GPU generator for gumbel softmax
        # don't use GPU generator in tests though
        generator = torch.Generator(device=torch_device).manual_seed(0)
        output = pipeline(
            "teddy bear playing in the pool",
            num_images_per_prompt=1,
            generator=generator,
            output_type="np",
        )

        image = output.images[0]

        assert image.shape == (256, 256, 3)
        assert np.abs(expected_image - image).max() < 2.0
