# 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.
import os
import tempfile
from collections import OrderedDict

import torch

from peft import (
    LoraConfig,
    PeftModel,
    PrefixTuningConfig,
    PromptEncoderConfig,
    PromptTuningConfig,
    get_peft_model,
    get_peft_model_state_dict,
    prepare_model_for_int8_training,
)


CONFIG_CLASSES = (
    LoraConfig,
    PrefixTuningConfig,
    PromptEncoderConfig,
    PromptTuningConfig,
)
CONFIG_TESTING_KWARGS = (
    {
        "r": 8,
        "lora_alpha": 32,
        "target_modules": None,
        "lora_dropout": 0.05,
        "bias": "none",
    },
    {
        "num_virtual_tokens": 10,
    },
    {
        "num_virtual_tokens": 10,
        "encoder_hidden_size": 32,
    },
    {
        "num_virtual_tokens": 10,
    },
)

CLASSES_MAPPING = {
    "lora": (LoraConfig, CONFIG_TESTING_KWARGS[0]),
    "prefix_tuning": (PrefixTuningConfig, CONFIG_TESTING_KWARGS[1]),
    "prompt_encoder": (PromptEncoderConfig, CONFIG_TESTING_KWARGS[2]),
    "prompt_tuning": (PromptTuningConfig, CONFIG_TESTING_KWARGS[3]),
}


# Adapted from https://github.com/huggingface/transformers/blob/48327c57182fdade7f7797d1eaad2d166de5c55b/src/transformers/activations.py#LL166C7-L166C22
class ClassInstantier(OrderedDict):
    def __getitem__(self, key, *args, **kwargs):
        # check if any of the kwargs is inside the config class kwargs
        if any(kwarg in self[key][1] for kwarg in kwargs):
            new_config_kwargs = self[key][1].copy()
            new_config_kwargs.update(kwargs)
            return (self[key][0], new_config_kwargs)

        return super().__getitem__(key, *args, **kwargs)

    def get_grid_parameters(self, grid_parameters, filter_params_func=None):
        r"""
        Returns a list of all possible combinations of the parameters in the config classes.

        Args:
            grid_parameters (`dict`):
                A dictionary containing the parameters to be tested. There should be at least the key "model_ids" which
                contains a list of model ids to be tested. The other keys should be the name of the config class
                post-fixed with "_kwargs" and the value should be a dictionary containing the parameters to be tested
                for that config class.
            filter_params_func (`callable`, `optional`):
                A function that takes a list of tuples and returns a list of tuples. This function is used to filter
                out the tests that needs for example to be skipped.

        Returns:
            generated_tests (`list`):
                A list of tuples containing the name of the test, the model id, the config class and the config class
                kwargs.
        """
        generated_tests = []
        model_list = grid_parameters["model_ids"]
        task_type = grid_parameters["task_type"] if "task_type" in grid_parameters else None

        for model_id in model_list:
            for key, value in self.items():
                if "{}_kwargs".format(key) in grid_parameters:
                    peft_configs = []
                    current_peft_config = value[1].copy()
                    for current_key, current_value in grid_parameters[f"{key}_kwargs"].items():
                        for kwarg in current_value:
                            current_peft_config.update({current_key: kwarg})

                            if task_type is not None:
                                current_peft_config.update({"task_type": task_type})

                            peft_configs.append(current_peft_config.copy())
                else:
                    current_peft_config = value[1].copy()
                    if task_type is not None:
                        current_peft_config.update({"task_type": task_type})
                    peft_configs = [current_peft_config]

                for peft_config in peft_configs:
                    generated_tests.append((f"test_{model_id}_{key}", model_id, value[0], peft_config))

        if filter_params_func is not None:
            generated_tests = filter_params_func(generated_tests)

        return generated_tests


PeftTestConfigManager = ClassInstantier(CLASSES_MAPPING)


class PeftCommonTester:
    r"""
    A large testing suite for testing common functionality of the PEFT models.

    Attributes:
        torch_device (`torch.device`):
            The device on which the tests will be run.
        transformers_class (`transformers.PreTrainedModel`):
            The transformers class that is being tested.
    """
    torch_device = "cuda" if torch.cuda.is_available() else "cpu"
    transformers_class = None

    def prepare_inputs_for_common(self):
        raise NotImplementedError

    def _test_model_attr(self, model_id, config_cls, config_kwargs):
        model = self.transformers_class.from_pretrained(model_id)
        config = config_cls(
            base_model_name_or_path=model_id,
            **config_kwargs,
        )
        model = get_peft_model(model, config)

        self.assertTrue(hasattr(model, "save_pretrained"))
        self.assertTrue(hasattr(model, "from_pretrained"))
        self.assertTrue(hasattr(model, "push_to_hub"))

    def _test_prepare_for_training(self, model_id, config_cls, config_kwargs):
        model = self.transformers_class.from_pretrained(model_id).to(self.torch_device)
        config = config_cls(
            base_model_name_or_path=model_id,
            **config_kwargs,
        )
        model = get_peft_model(model, config)

        dummy_input = self.prepare_inputs_for_testing()
        dummy_output = model.get_input_embeddings()(dummy_input["input_ids"])

        self.assertTrue(not dummy_output.requires_grad)

        # load with `prepare_model_for_int8_training`
        model = self.transformers_class.from_pretrained(model_id).to(self.torch_device)
        model = prepare_model_for_int8_training(model)

        for param in model.parameters():
            self.assertTrue(not param.requires_grad)

        config = config_cls(
            base_model_name_or_path=model_id,
            **config_kwargs,
        )
        model = get_peft_model(model, config)

        # For backward compatibility
        if hasattr(model, "enable_input_require_grads"):
            model.enable_input_require_grads()
        else:

            def make_inputs_require_grad(module, input, output):
                output.requires_grad_(True)

            model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)

        dummy_input = self.prepare_inputs_for_testing()
        dummy_output = model.get_input_embeddings()(dummy_input["input_ids"])

        self.assertTrue(dummy_output.requires_grad)

    def _test_save_pretrained(self, model_id, config_cls, config_kwargs):
        model = self.transformers_class.from_pretrained(model_id)
        config = config_cls(
            base_model_name_or_path=model_id,
            **config_kwargs,
        )
        model = get_peft_model(model, config)
        model = model.to(self.torch_device)

        with tempfile.TemporaryDirectory() as tmp_dirname:
            model.save_pretrained(tmp_dirname)

            model_from_pretrained = self.transformers_class.from_pretrained(model_id)
            model_from_pretrained = PeftModel.from_pretrained(model_from_pretrained, tmp_dirname)

            # check if the state dicts are equal
            state_dict = get_peft_model_state_dict(model)
            state_dict_from_pretrained = get_peft_model_state_dict(model_from_pretrained)

            # check if same keys
            self.assertEqual(state_dict.keys(), state_dict_from_pretrained.keys())

            # check if tensors equal
            for key in state_dict.keys():
                self.assertTrue(
                    torch.allclose(
                        state_dict[key].to(self.torch_device), state_dict_from_pretrained[key].to(self.torch_device)
                    )
                )

            # check if `adapter_model.bin` is present
            self.assertTrue(os.path.exists(os.path.join(tmp_dirname, "adapter_model.bin")))

            # check if `adapter_config.json` is present
            self.assertTrue(os.path.exists(os.path.join(tmp_dirname, "adapter_config.json")))

            # check if `pytorch_model.bin` is not present
            self.assertFalse(os.path.exists(os.path.join(tmp_dirname, "pytorch_model.bin")))

            # check if `config.json` is not present
            self.assertFalse(os.path.exists(os.path.join(tmp_dirname, "config.json")))

    def _test_merge_layers(self, model_id, config_cls, config_kwargs):
        model = self.transformers_class.from_pretrained(model_id)
        config = config_cls(
            base_model_name_or_path=model_id,
            **config_kwargs,
        )
        model = get_peft_model(model, config)
        model = model.to(self.torch_device)

        if config.peft_type != "LORA":
            with self.assertRaises(AttributeError):
                model = model.merge_and_unload()
        elif model.config.model_type == "gpt2":
            with self.assertRaises(ValueError):
                model = model.merge_and_unload()
        else:
            dummy_input = self.prepare_inputs_for_testing()
            model.eval()
            logits_lora = model(**dummy_input)[0]

            model = model.merge_and_unload()

            logits_merged = model(**dummy_input)[0]

            transformers_model = self.transformers_class.from_pretrained(model_id).to(self.torch_device)

            logits_transformers = transformers_model(**dummy_input)[0]

            self.assertTrue(torch.allclose(logits_lora, logits_merged, atol=1e-4, rtol=1e-4))
            self.assertFalse(torch.allclose(logits_merged, logits_transformers, atol=1e-10, rtol=1e-10))

            with tempfile.TemporaryDirectory() as tmp_dirname:
                model.save_pretrained(tmp_dirname)

                model_from_pretrained = self.transformers_class.from_pretrained(tmp_dirname).to(self.torch_device)

                logits_merged_from_pretrained = model_from_pretrained(**dummy_input)[0]

                self.assertTrue(torch.allclose(logits_merged, logits_merged_from_pretrained, atol=1e-4, rtol=1e-4))

    def _test_generate(self, model_id, config_cls, config_kwargs):
        model = self.transformers_class.from_pretrained(model_id)
        config = config_cls(
            base_model_name_or_path=model_id,
            **config_kwargs,
        )
        model = get_peft_model(model, config)
        model = model.to(self.torch_device)

        inputs = self.prepare_inputs_for_testing()

        # check if `generate` works
        _ = model.generate(**inputs)

        with self.assertRaises(TypeError):
            # check if `generate` raises an error if no positional arguments are passed
            _ = model.generate(inputs["input_ids"])

    def _test_generate_half_prec(self, model_id, config_cls, config_kwargs):
        if config_cls not in (LoraConfig, PrefixTuningConfig):
            return

        model = self.transformers_class.from_pretrained(model_id, torch_dtype=torch.bfloat16)
        config = config_cls(
            base_model_name_or_path=model_id,
            **config_kwargs,
        )
        model = get_peft_model(model, config)
        model = model.to(self.torch_device)

        input_ids = torch.LongTensor([[1, 1, 1], [2, 1, 2]]).to(self.torch_device)
        attention_mask = torch.LongTensor([[1, 1, 1], [1, 0, 1]]).to(self.torch_device)

        # check if `generate` works
        _ = model.generate(input_ids=input_ids, attention_mask=attention_mask)

        with self.assertRaises(TypeError):
            # check if `generate` raises an error if no positional arguments are passed
            _ = model.generate(input_ids, attention_mask=attention_mask)

    def _test_training(self, model_id, config_cls, config_kwargs):
        if config_cls not in (LoraConfig,):
            return

        model = self.transformers_class.from_pretrained(model_id)
        config = config_cls(
            base_model_name_or_path=model_id,
            **config_kwargs,
        )
        model = get_peft_model(model, config)
        model = model.to(self.torch_device)

        inputs = self.prepare_inputs_for_testing()

        # check if `training` works
        output = model(**inputs)[0]
        loss = output.sum()
        loss.backward()

        for n, param in model.named_parameters():
            if "lora" in n:
                self.assertIsNotNone(param.grad)
            else:
                self.assertIsNone(param.grad)
