# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# 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
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# 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.
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import os
from collections import defaultdict
from typing import Callable, Dict, Union

import torch

from .models.cross_attention import LoRACrossAttnProcessor
from .models.modeling_utils import _get_model_file
from .utils import DIFFUSERS_CACHE, HF_HUB_OFFLINE, logging


logger = logging.get_logger(__name__)


LORA_WEIGHT_NAME = "pytorch_lora_weights.bin"


class AttnProcsLayers(torch.nn.Module):
    def __init__(self, state_dict: Dict[str, torch.Tensor]):
        super().__init__()
        self.layers = torch.nn.ModuleList(state_dict.values())
        self.mapping = {k: v for k, v in enumerate(state_dict.keys())}
        self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys())}

        # we add a hook to state_dict() and load_state_dict() so that the
        # naming fits with `unet.attn_processors`
        def map_to(module, state_dict, *args, **kwargs):
            new_state_dict = {}
            for key, value in state_dict.items():
                num = int(key.split(".")[1])  # 0 is always "layers"
                new_key = key.replace(f"layers.{num}", module.mapping[num])
                new_state_dict[new_key] = value

            return new_state_dict

        def map_from(module, state_dict, *args, **kwargs):
            all_keys = list(state_dict.keys())
            for key in all_keys:
                replace_key = key.split(".processor")[0] + ".processor"
                new_key = key.replace(replace_key, f"layers.{module.rev_mapping[replace_key]}")
                state_dict[new_key] = state_dict[key]
                del state_dict[key]

        self._register_state_dict_hook(map_to)
        self._register_load_state_dict_pre_hook(map_from, with_module=True)


class UNet2DConditionLoadersMixin:
    def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
        r"""
        Load pretrained attention processor layers into `UNet2DConditionModel`. Attention processor layers have to be
        defined in
        [cross_attention.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py)
        and be a `torch.nn.Module` class.

        <Tip warning={true}>

            This function is experimental and might change in the future.

        </Tip>

        Parameters:
            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
                Can be either:

                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
                      Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.
                    - A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g.,
                      `./my_model_directory/`.
                    - A [torch state
                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).

            cache_dir (`Union[str, os.PathLike]`, *optional*):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            resume_download (`bool`, *optional*, defaults to `False`):
                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
                file exists.
            proxies (`Dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            local_files_only(`bool`, *optional*, defaults to `False`):
                Whether or not to only look at local files (i.e., do not try to download the model).
            use_auth_token (`str` or *bool*, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
                when running `diffusers-cli login` (stored in `~/.huggingface`).
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
                identifier allowed by git.
            subfolder (`str`, *optional*, defaults to `""`):
                In case the relevant files are located inside a subfolder of the model repo (either remote in
                huggingface.co or downloaded locally), you can specify the folder name here.

            mirror (`str`, *optional*):
                Mirror source to accelerate downloads in China. If you are from China and have an accessibility
                problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
                Please refer to the mirror site for more information.

        <Tip>

         It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
         models](https://huggingface.co/docs/hub/models-gated#gated-models).

        </Tip>

        <Tip>

        Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use
        this method in a firewalled environment.

        </Tip>
        """

        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
        force_download = kwargs.pop("force_download", False)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
        use_auth_token = kwargs.pop("use_auth_token", None)
        revision = kwargs.pop("revision", None)
        subfolder = kwargs.pop("subfolder", None)
        weight_name = kwargs.pop("weight_name", LORA_WEIGHT_NAME)

        user_agent = {
            "file_type": "attn_procs_weights",
            "framework": "pytorch",
        }

        if not isinstance(pretrained_model_name_or_path_or_dict, dict):
            model_file = _get_model_file(
                pretrained_model_name_or_path_or_dict,
                weights_name=weight_name,
                cache_dir=cache_dir,
                force_download=force_download,
                resume_download=resume_download,
                proxies=proxies,
                local_files_only=local_files_only,
                use_auth_token=use_auth_token,
                revision=revision,
                subfolder=subfolder,
                user_agent=user_agent,
            )
            state_dict = torch.load(model_file, map_location="cpu")
        else:
            state_dict = pretrained_model_name_or_path_or_dict

        # fill attn processors
        attn_processors = {}

        is_lora = all("lora" in k for k in state_dict.keys())

        if is_lora:
            lora_grouped_dict = defaultdict(dict)
            for key, value in state_dict.items():
                attn_processor_key, sub_key = ".".join(key.split(".")[:-3]), ".".join(key.split(".")[-3:])
                lora_grouped_dict[attn_processor_key][sub_key] = value

            for key, value_dict in lora_grouped_dict.items():
                rank = value_dict["to_k_lora.down.weight"].shape[0]
                cross_attention_dim = value_dict["to_k_lora.down.weight"].shape[1]
                hidden_size = value_dict["to_k_lora.up.weight"].shape[0]

                attn_processors[key] = LoRACrossAttnProcessor(
                    hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=rank
                )
                attn_processors[key].load_state_dict(value_dict)

        else:
            raise ValueError(f"{model_file} does not seem to be in the correct format expected by LoRA training.")

        # set correct dtype & device
        attn_processors = {k: v.to(device=self.device, dtype=self.dtype) for k, v in attn_processors.items()}

        # set layers
        self.set_attn_processor(attn_processors)

    def save_attn_procs(
        self,
        save_directory: Union[str, os.PathLike],
        is_main_process: bool = True,
        weights_name: str = LORA_WEIGHT_NAME,
        save_function: Callable = None,
    ):
        r"""
        Save an attention processor to a directory, so that it can be re-loaded using the
        `[`~loaders.UNet2DConditionLoadersMixin.load_attn_procs`]` method.

        Arguments:
            save_directory (`str` or `os.PathLike`):
                Directory to which to save. Will be created if it doesn't exist.
            is_main_process (`bool`, *optional*, defaults to `True`):
                Whether the process calling this is the main process or not. Useful when in distributed training like
                TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on
                the main process to avoid race conditions.
            save_function (`Callable`):
                The function to use to save the state dictionary. Useful on distributed training like TPUs when one
                need to replace `torch.save` by another method. Can be configured with the environment variable
                `DIFFUSERS_SAVE_MODE`.
        """
        if os.path.isfile(save_directory):
            logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
            return

        if save_function is None:
            save_function = torch.save

        os.makedirs(save_directory, exist_ok=True)

        model_to_save = AttnProcsLayers(self.attn_processors)

        # Save the model
        state_dict = model_to_save.state_dict()

        # Clean the folder from a previous save
        for filename in os.listdir(save_directory):
            full_filename = os.path.join(save_directory, filename)
            # If we have a shard file that is not going to be replaced, we delete it, but only from the main process
            # in distributed settings to avoid race conditions.
            weights_no_suffix = weights_name.replace(".bin", "")
            if filename.startswith(weights_no_suffix) and os.path.isfile(full_filename) and is_main_process:
                os.remove(full_filename)

        # Save the model
        save_function(state_dict, os.path.join(save_directory, weights_name))

        logger.info(f"Model weights saved in {os.path.join(save_directory, weights_name)}")
