# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. 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.

from dataclasses import asdict, replace
from unittest import TestCase

import numpy as np
from diffusers import StableDiffusionPipeline
from parameterized import parameterized

from peft import LoHaConfig, LoraConfig, OFTConfig, get_peft_model

from .testing_common import ClassInstantier, PeftCommonTester
from .testing_utils import temp_seed


PEFT_DIFFUSERS_SD_MODELS_TO_TEST = ["hf-internal-testing/tiny-stable-diffusion-torch"]
CONFIG_TESTING_KWARGS = (
    {
        "text_encoder": {
            "r": 8,
            "lora_alpha": 32,
            "target_modules": ["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
            "lora_dropout": 0.0,
            "bias": "none",
        },
        "unet": {
            "r": 8,
            "lora_alpha": 32,
            "target_modules": ["proj_in", "proj_out", "to_k", "to_q", "to_v", "to_out.0", "ff.net.0.proj", "ff.net.2"],
            "lora_dropout": 0.0,
            "bias": "none",
        },
    },
    {
        "text_encoder": {
            "r": 8,
            "alpha": 32,
            "target_modules": ["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
            "rank_dropout": 0.0,
            "module_dropout": 0.0,
        },
        "unet": {
            "r": 8,
            "alpha": 32,
            "target_modules": ["proj_in", "proj_out", "to_k", "to_q", "to_v", "to_out.0", "ff.net.0.proj", "ff.net.2"],
            "rank_dropout": 0.0,
            "module_dropout": 0.0,
        },
    },
    {
        "text_encoder": {
            "r": 8,
            "target_modules": ["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
            "module_dropout": 0.0,
        },
        "unet": {
            "r": 8,
            "target_modules": ["proj_in", "proj_out", "to_k", "to_q", "to_v", "to_out.0", "ff.net.0.proj", "ff.net.2"],
            "module_dropout": 0.0,
        },
    },
)
CLASSES_MAPPING = {
    "lora": (LoraConfig, CONFIG_TESTING_KWARGS[0]),
    "loha": (LoHaConfig, CONFIG_TESTING_KWARGS[1]),
    "lokr": (LoHaConfig, CONFIG_TESTING_KWARGS[1]),
    "oft": (OFTConfig, CONFIG_TESTING_KWARGS[2]),
}


PeftStableDiffusionTestConfigManager = ClassInstantier(CLASSES_MAPPING)


class StableDiffusionModelTester(TestCase, PeftCommonTester):
    r"""
    Tests that diffusers StableDiffusion model works with PEFT as expected.

    """
    transformers_class = StableDiffusionPipeline

    def instantiate_sd_peft(self, model_id, config_cls, config_kwargs):
        # Instantiate StableDiffusionPipeline
        model = self.transformers_class.from_pretrained(model_id)

        config_kwargs = config_kwargs.copy()
        text_encoder_kwargs = config_kwargs.pop("text_encoder")
        unet_kwargs = config_kwargs.pop("unet")
        # the remaining config kwargs should be applied to both configs
        for key, val in config_kwargs.items():
            text_encoder_kwargs[key] = val
            unet_kwargs[key] = val

        # Instantiate text_encoder adapter
        config_text_encoder = config_cls(**text_encoder_kwargs)
        model.text_encoder = get_peft_model(model.text_encoder, config_text_encoder)

        # Instantiate unet adapter
        config_unet = config_cls(**unet_kwargs)
        model.unet = get_peft_model(model.unet, config_unet)

        # Move model to device
        model = model.to(self.torch_device)

        return model

    def prepare_inputs_for_testing(self):
        return {
            "prompt": "a high quality digital photo of a cute corgi",
            "num_inference_steps": 20,
        }

    @parameterized.expand(
        PeftStableDiffusionTestConfigManager.get_grid_parameters(
            {
                "model_ids": PEFT_DIFFUSERS_SD_MODELS_TO_TEST,
                "lora_kwargs": {"init_lora_weights": [False]},
                "loha_kwargs": {"init_weights": [False]},
                "oft_kwargs": {"init_weights": [False]},
            },
        )
    )
    def test_merge_layers(self, test_name, model_id, config_cls, config_kwargs):
        # Instantiate model & adapters
        model = self.instantiate_sd_peft(model_id, config_cls, config_kwargs)

        # Generate output for peft modified StableDiffusion
        dummy_input = self.prepare_inputs_for_testing()
        with temp_seed(seed=42):
            peft_output = np.array(model(**dummy_input).images[0]).astype(np.float32)

        # Merge adapter and model
        if config_cls not in [LoHaConfig, OFTConfig]:
            # TODO: Merging the text_encoder is leading to issues on CPU with PyTorch 2.1
            model.text_encoder = model.text_encoder.merge_and_unload()
        model.unet = model.unet.merge_and_unload()

        # Generate output for peft merged StableDiffusion
        with temp_seed(seed=42):
            merged_output = np.array(model(**dummy_input).images[0]).astype(np.float32)

        # Images are in uint8 drange, so use large atol
        self.assertTrue(np.allclose(peft_output, merged_output, atol=1.0))

    @parameterized.expand(
        PeftStableDiffusionTestConfigManager.get_grid_parameters(
            {
                "model_ids": PEFT_DIFFUSERS_SD_MODELS_TO_TEST,
                "lora_kwargs": {"init_lora_weights": [False]},
                "loha_kwargs": {"init_weights": [False]},
                "oft_kwargs": {"init_weights": [False]},
            },
        )
    )
    def test_merge_layers_safe_merge(self, test_name, model_id, config_cls, config_kwargs):
        # Instantiate model & adapters
        model = self.instantiate_sd_peft(model_id, config_cls, config_kwargs)

        # Generate output for peft modified StableDiffusion
        dummy_input = self.prepare_inputs_for_testing()
        with temp_seed(seed=42):
            peft_output = np.array(model(**dummy_input).images[0]).astype(np.float32)

        # Merge adapter and model
        if config_cls not in [LoHaConfig, OFTConfig]:
            # TODO: Merging the text_encoder is leading to issues on CPU with PyTorch 2.1
            model.text_encoder = model.text_encoder.merge_and_unload(safe_merge=True)
        model.unet = model.unet.merge_and_unload(safe_merge=True)

        # Generate output for peft merged StableDiffusion
        with temp_seed(seed=42):
            merged_output = np.array(model(**dummy_input).images[0]).astype(np.float32)

        # Images are in uint8 drange, so use large atol
        self.assertTrue(np.allclose(peft_output, merged_output, atol=1.0))

    @parameterized.expand(
        PeftStableDiffusionTestConfigManager.get_grid_parameters(
            {
                "model_ids": PEFT_DIFFUSERS_SD_MODELS_TO_TEST,
                "lora_kwargs": {"init_lora_weights": [False]},
            },
            filter_params_func=lambda tests: [x for x in tests if all(s not in x[0] for s in ["loha", "lokr", "oft"])],
        )
    )
    def test_add_weighted_adapter_base_unchanged(self, test_name, model_id, config_cls, config_kwargs):
        # Instantiate model & adapters
        model = self.instantiate_sd_peft(model_id, config_cls, config_kwargs)

        # Get current available adapter config
        text_encoder_adapter_name = next(iter(model.text_encoder.peft_config.keys()))
        unet_adapter_name = next(iter(model.unet.peft_config.keys()))
        text_encoder_adapter_config = replace(model.text_encoder.peft_config[text_encoder_adapter_name])
        unet_adapter_config = replace(model.unet.peft_config[unet_adapter_name])

        # Create weighted adapters
        model.text_encoder.add_weighted_adapter([unet_adapter_name], [0.5], "weighted_adapter_test")
        model.unet.add_weighted_adapter([unet_adapter_name], [0.5], "weighted_adapter_test")

        # Assert that base adapters config did not change
        self.assertTrue(
            asdict(text_encoder_adapter_config) == asdict(model.text_encoder.peft_config[text_encoder_adapter_name])
        )
        self.assertTrue(asdict(unet_adapter_config) == asdict(model.unet.peft_config[unet_adapter_name]))

    @parameterized.expand(
        PeftStableDiffusionTestConfigManager.get_grid_parameters(
            {
                "model_ids": PEFT_DIFFUSERS_SD_MODELS_TO_TEST,
                "lora_kwargs": {"init_lora_weights": [False]},
                "loha_kwargs": {"init_weights": [False]},
                "lokr_kwargs": {"init_weights": [False]},
                "oft_kwargs": {"init_weights": [False]},
            },
        )
    )
    def test_disable_adapter(self, test_name, model_id, config_cls, config_kwargs):
        self._test_disable_adapter(model_id, config_cls, config_kwargs)
