# 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
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import DDPMWuerstchenScheduler, StableCascadePriorPipeline
from diffusers.loaders import AttnProcsLayers
from diffusers.models import StableCascadeUNet
from diffusers.models.attention_processor import LoRAAttnProcessor, LoRAAttnProcessor2_0
from diffusers.utils.import_utils import is_peft_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    numpy_cosine_similarity_distance,
    require_peft_backend,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)


if is_peft_available():
    from peft import LoraConfig
    from peft.tuners.tuners_utils import BaseTunerLayer

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


def create_prior_lora_layers(unet: nn.Module):
    lora_attn_procs = {}
    for name in unet.attn_processors.keys():
        lora_attn_processor_class = (
            LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor
        )
        lora_attn_procs[name] = lora_attn_processor_class(
            hidden_size=unet.config.c,
        )
    unet_lora_layers = AttnProcsLayers(lora_attn_procs)
    return lora_attn_procs, unet_lora_layers


class StableCascadePriorPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
    pipeline_class = StableCascadePriorPipeline
    params = ["prompt"]
    batch_params = ["prompt", "negative_prompt"]
    required_optional_params = [
        "num_images_per_prompt",
        "generator",
        "num_inference_steps",
        "latents",
        "negative_prompt",
        "guidance_scale",
        "output_type",
        "return_dict",
    ]
    test_xformers_attention = False
    callback_cfg_params = ["text_encoder_hidden_states"]

    @property
    def text_embedder_hidden_size(self):
        return 32

    @property
    def time_input_dim(self):
        return 32

    @property
    def block_out_channels_0(self):
        return self.time_input_dim

    @property
    def time_embed_dim(self):
        return self.time_input_dim * 4

    @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,
            projection_dim=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 CLIPTextModelWithProjection(config).eval()

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

        model_kwargs = {
            "conditioning_dim": 128,
            "block_out_channels": (128, 128),
            "num_attention_heads": (2, 2),
            "down_num_layers_per_block": (1, 1),
            "up_num_layers_per_block": (1, 1),
            "switch_level": (False,),
            "clip_image_in_channels": 768,
            "clip_text_in_channels": self.text_embedder_hidden_size,
            "clip_text_pooled_in_channels": self.text_embedder_hidden_size,
            "dropout": (0.1, 0.1),
        }

        model = StableCascadeUNet(**model_kwargs)
        return model.eval()

    def get_dummy_components(self):
        prior = self.dummy_prior
        text_encoder = self.dummy_text_encoder
        tokenizer = self.dummy_tokenizer

        scheduler = DDPMWuerstchenScheduler()

        components = {
            "prior": prior,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "scheduler": scheduler,
            "feature_extractor": None,
            "image_encoder": 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": "horse",
            "generator": generator,
            "guidance_scale": 4.0,
            "num_inference_steps": 2,
            "output_type": "np",
        }
        return inputs

    def test_wuerstchen_prior(self):
        device = "cpu"

        components = self.get_dummy_components()

        pipe = self.pipeline_class(**components)
        pipe = pipe.to(device)

        pipe.set_progress_bar_config(disable=None)

        output = pipe(**self.get_dummy_inputs(device))
        image = output.image_embeddings

        image_from_tuple = pipe(**self.get_dummy_inputs(device), return_dict=False)[0]

        image_slice = image[0, 0, 0, -10:]
        image_from_tuple_slice = image_from_tuple[0, 0, 0, -10:]
        assert image.shape == (1, 16, 24, 24)

        expected_slice = np.array(
            [
                96.139565,
                -20.213179,
                -116.40341,
                -191.57129,
                39.350136,
                74.80767,
                39.782352,
                -184.67352,
                -46.426907,
                168.41783,
            ]
        )

        assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5e-2

    @skip_mps
    def test_inference_batch_single_identical(self):
        self._test_inference_batch_single_identical(expected_max_diff=2e-1)

    @skip_mps
    def test_attention_slicing_forward_pass(self):
        test_max_difference = torch_device == "cpu"
        test_mean_pixel_difference = False

        self._test_attention_slicing_forward_pass(
            test_max_difference=test_max_difference,
            test_mean_pixel_difference=test_mean_pixel_difference,
        )

    @unittest.skip(reason="fp16 not supported")
    def test_float16_inference(self):
        super().test_float16_inference()

    def check_if_lora_correctly_set(self, model) -> bool:
        """
        Checks if the LoRA layers are correctly set with peft
        """
        for module in model.modules():
            if isinstance(module, BaseTunerLayer):
                return True
        return False

    def get_lora_components(self):
        prior = self.dummy_prior

        prior_lora_config = LoraConfig(
            r=4, lora_alpha=4, target_modules=["to_q", "to_k", "to_v", "to_out.0"], init_lora_weights=False
        )

        prior_lora_attn_procs, prior_lora_layers = create_prior_lora_layers(prior)

        lora_components = {
            "prior_lora_layers": prior_lora_layers,
            "prior_lora_attn_procs": prior_lora_attn_procs,
        }

        return prior, prior_lora_config, lora_components

    @require_peft_backend
    @unittest.skip(reason="no lora support for now")
    def test_inference_with_prior_lora(self):
        _, prior_lora_config, _ = self.get_lora_components()
        device = "cpu"

        components = self.get_dummy_components()

        pipe = self.pipeline_class(**components)
        pipe = pipe.to(device)

        pipe.set_progress_bar_config(disable=None)

        output_no_lora = pipe(**self.get_dummy_inputs(device))
        image_embed = output_no_lora.image_embeddings
        self.assertTrue(image_embed.shape == (1, 16, 24, 24))

        pipe.prior.add_adapter(prior_lora_config)
        self.assertTrue(self.check_if_lora_correctly_set(pipe.prior), "Lora not correctly set in prior")

        output_lora = pipe(**self.get_dummy_inputs(device))
        lora_image_embed = output_lora.image_embeddings

        self.assertTrue(image_embed.shape == lora_image_embed.shape)

    def test_stable_cascade_decoder_prompt_embeds(self):
        device = "cpu"
        components = self.get_dummy_components()

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

        prompt = "A photograph of a shiba inu, wearing a hat"
        (
            prompt_embeds,
            prompt_embeds_pooled,
            negative_prompt_embeds,
            negative_prompt_embeds_pooled,
        ) = pipe.encode_prompt(device, 1, 1, False, prompt=prompt)
        generator = torch.Generator(device=device)

        output_prompt = pipe(
            prompt=prompt,
            num_inference_steps=1,
            output_type="np",
            generator=generator.manual_seed(0),
        )
        output_prompt_embeds = pipe(
            prompt=None,
            prompt_embeds=prompt_embeds,
            prompt_embeds_pooled=prompt_embeds_pooled,
            negative_prompt_embeds=negative_prompt_embeds,
            negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,
            num_inference_steps=1,
            output_type="np",
            generator=generator.manual_seed(0),
        )

        assert np.abs(output_prompt.image_embeddings - output_prompt_embeds.image_embeddings).max() < 1e-5


@slow
@require_torch_gpu
class StableCascadePriorPipelineIntegrationTests(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_stable_cascade_prior(self):
        pipe = StableCascadePriorPipeline.from_pretrained(
            "stabilityai/stable-cascade-prior", variant="bf16", torch_dtype=torch.bfloat16
        )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=None)

        prompt = "A photograph of the inside of a subway train. There are raccoons sitting on the seats. One of them is reading a newspaper. The window shows the city in the background."

        generator = torch.Generator(device="cpu").manual_seed(0)

        output = pipe(prompt, num_inference_steps=2, output_type="np", generator=generator)
        image_embedding = output.image_embeddings
        expected_image_embedding = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_cascade/stable_cascade_prior_image_embeddings.npy"
        )
        assert image_embedding.shape == (1, 16, 24, 24)

        max_diff = numpy_cosine_similarity_distance(image_embedding.flatten(), expected_image_embedding.flatten())
        assert max_diff < 1e-4