# 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 sys
import tempfile
import unittest

import numpy as np
import torch
from transformers import AutoTokenizer, GlmModel

from diffusers import AutoencoderKL, CogView4Pipeline, CogView4Transformer2DModel, FlowMatchEulerDiscreteScheduler
from diffusers.utils.testing_utils import floats_tensor, require_peft_backend, skip_mps, torch_device


sys.path.append(".")

from utils import PeftLoraLoaderMixinTests  # noqa: E402


class TokenizerWrapper:
    @staticmethod
    def from_pretrained(*args, **kwargs):
        return AutoTokenizer.from_pretrained(
            "hf-internal-testing/tiny-random-cogview4", subfolder="tokenizer", trust_remote_code=True
        )


@require_peft_backend
@skip_mps
class CogView4LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
    pipeline_class = CogView4Pipeline
    scheduler_cls = FlowMatchEulerDiscreteScheduler
    scheduler_classes = [FlowMatchEulerDiscreteScheduler]
    scheduler_kwargs = {}

    transformer_kwargs = {
        "patch_size": 2,
        "in_channels": 4,
        "num_layers": 2,
        "attention_head_dim": 4,
        "num_attention_heads": 4,
        "out_channels": 4,
        "text_embed_dim": 32,
        "time_embed_dim": 8,
        "condition_dim": 4,
    }
    transformer_cls = CogView4Transformer2DModel
    vae_kwargs = {
        "block_out_channels": [32, 64],
        "in_channels": 3,
        "out_channels": 3,
        "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
        "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
        "latent_channels": 4,
        "sample_size": 128,
    }
    vae_cls = AutoencoderKL
    tokenizer_cls, tokenizer_id, tokenizer_subfolder = (
        TokenizerWrapper,
        "hf-internal-testing/tiny-random-cogview4",
        "tokenizer",
    )
    text_encoder_cls, text_encoder_id, text_encoder_subfolder = (
        GlmModel,
        "hf-internal-testing/tiny-random-cogview4",
        "text_encoder",
    )

    @property
    def output_shape(self):
        return (1, 32, 32, 3)

    def get_dummy_inputs(self, with_generator=True):
        batch_size = 1
        sequence_length = 16
        num_channels = 4
        sizes = (4, 4)

        generator = torch.manual_seed(0)
        noise = floats_tensor((batch_size, num_channels) + sizes)
        input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)

        pipeline_inputs = {
            "prompt": "",
            "num_inference_steps": 1,
            "guidance_scale": 6.0,
            "height": 32,
            "width": 32,
            "max_sequence_length": sequence_length,
            "output_type": "np",
        }
        if with_generator:
            pipeline_inputs.update({"generator": generator})

        return noise, input_ids, pipeline_inputs

    def test_simple_inference_with_text_lora_denoiser_fused_multi(self):
        super().test_simple_inference_with_text_lora_denoiser_fused_multi(expected_atol=9e-3)

    def test_simple_inference_with_text_denoiser_lora_unfused(self):
        super().test_simple_inference_with_text_denoiser_lora_unfused(expected_atol=9e-3)

    def test_simple_inference_save_pretrained(self):
        """
        Tests a simple usecase where users could use saving utilities for LoRA through save_pretrained
        """
        for scheduler_cls in self.scheduler_classes:
            components, _, _ = self.get_dummy_components(scheduler_cls)
            pipe = self.pipeline_class(**components)
            pipe = pipe.to(torch_device)
            pipe.set_progress_bar_config(disable=None)
            _, _, inputs = self.get_dummy_inputs(with_generator=False)

            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
            self.assertTrue(output_no_lora.shape == self.output_shape)

            images_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]

            with tempfile.TemporaryDirectory() as tmpdirname:
                pipe.save_pretrained(tmpdirname)

                pipe_from_pretrained = self.pipeline_class.from_pretrained(tmpdirname)
                pipe_from_pretrained.to(torch_device)

            images_lora_save_pretrained = pipe_from_pretrained(**inputs, generator=torch.manual_seed(0))[0]

            self.assertTrue(
                np.allclose(images_lora, images_lora_save_pretrained, atol=1e-3, rtol=1e-3),
                "Loading from saved checkpoints should give same results.",
            )

    @unittest.skip("Not supported in CogView4.")
    def test_simple_inference_with_text_denoiser_block_scale(self):
        pass

    @unittest.skip("Not supported in CogView4.")
    def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
        pass

    @unittest.skip("Not supported in CogView4.")
    def test_modify_padding_mode(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in CogView4.")
    def test_simple_inference_with_partial_text_lora(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in CogView4.")
    def test_simple_inference_with_text_lora(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in CogView4.")
    def test_simple_inference_with_text_lora_and_scale(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in CogView4.")
    def test_simple_inference_with_text_lora_fused(self):
        pass

    @unittest.skip("Text encoder LoRA is not supported in CogView4.")
    def test_simple_inference_with_text_lora_save_load(self):
        pass
