# Copyright 2023 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
#
# 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 pathlib import Path
from typing import Dict, Union

import torch
from huggingface_hub.utils import validate_hf_hub_args
from safetensors import safe_open

from ..utils import (
    _get_model_file,
    is_transformers_available,
    logging,
)


if is_transformers_available():
    from transformers import (
        CLIPImageProcessor,
        CLIPVisionModelWithProjection,
    )

    from ..models.attention_processor import (
        IPAdapterAttnProcessor,
        IPAdapterAttnProcessor2_0,
    )

logger = logging.get_logger(__name__)


class IPAdapterMixin:
    """Mixin for handling IP Adapters."""

    @validate_hf_hub_args
    def load_ip_adapter(
        self,
        pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
        subfolder: str,
        weight_name: str,
        **kwargs,
    ):
        """
        Parameters:
            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
                Can be either:

                    - A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
                      the Hub.
                    - A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
                      with [`ModelMixin.save_pretrained`].
                    - 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 where a downloaded pretrained model configuration is cached if the standard cache
                is not 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 resume downloading the model weights and configuration files. If set to `False`, any
                incompletely downloaded files are deleted.
            proxies (`Dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, for example, `{'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 to only load local model weights and configuration files or not. If set to `True`, the model
                won't be downloaded from the Hub.
            token (`str` or *bool*, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
                `diffusers-cli login` (stored in `~/.huggingface`) is used.
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
                allowed by Git.
            subfolder (`str`, *optional*, defaults to `""`):
                The subfolder location of a model file within a larger model repository on the Hub or locally.
        """

        # Load the main state dict first.
        cache_dir = kwargs.pop("cache_dir", None)
        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", None)
        token = kwargs.pop("token", None)
        revision = kwargs.pop("revision", None)

        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,
                token=token,
                revision=revision,
                subfolder=subfolder,
                user_agent=user_agent,
            )
            if weight_name.endswith(".safetensors"):
                state_dict = {"image_proj": {}, "ip_adapter": {}}
                with safe_open(model_file, framework="pt", device="cpu") as f:
                    for key in f.keys():
                        if key.startswith("image_proj."):
                            state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
                        elif key.startswith("ip_adapter."):
                            state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
            else:
                state_dict = torch.load(model_file, map_location="cpu")
        else:
            state_dict = pretrained_model_name_or_path_or_dict

        keys = list(state_dict.keys())
        if keys != ["image_proj", "ip_adapter"]:
            raise ValueError("Required keys are (`image_proj` and `ip_adapter`) missing from the state dict.")

        # load CLIP image encoder here if it has not been registered to the pipeline yet
        if hasattr(self, "image_encoder") and getattr(self, "image_encoder", None) is None:
            if not isinstance(pretrained_model_name_or_path_or_dict, dict):
                logger.info(f"loading image_encoder from {pretrained_model_name_or_path_or_dict}")
                image_encoder = CLIPVisionModelWithProjection.from_pretrained(
                    pretrained_model_name_or_path_or_dict,
                    subfolder=Path(subfolder, "image_encoder").as_posix(),
                ).to(self.device, dtype=self.dtype)
                self.image_encoder = image_encoder
                self.register_to_config(image_encoder=["transformers", "CLIPVisionModelWithProjection"])
            else:
                raise ValueError("`image_encoder` cannot be None when using IP Adapters.")

        # create feature extractor if it has not been registered to the pipeline yet
        if hasattr(self, "feature_extractor") and getattr(self, "feature_extractor", None) is None:
            self.feature_extractor = CLIPImageProcessor()
            self.register_to_config(feature_extractor=["transformers", "CLIPImageProcessor"])

        # load ip-adapter into unet
        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
        unet._load_ip_adapter_weights(state_dict)

    def set_ip_adapter_scale(self, scale):
        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
        for attn_processor in unet.attn_processors.values():
            if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)):
                attn_processor.scale = scale

    def unload_ip_adapter(self):
        """
        Unloads the IP Adapter weights

        Examples:

        ```python
        >>> # Assuming `pipeline` is already loaded with the IP Adapter weights.
        >>> pipeline.unload_ip_adapter()
        >>> ...
        ```
        """
        # remove CLIP image encoder
        if hasattr(self, "image_encoder") and getattr(self, "image_encoder", None) is not None:
            self.image_encoder = None
            self.register_to_config(image_encoder=[None, None])

        # remove feature extractor
        if hasattr(self, "feature_extractor") and getattr(self, "feature_extractor", None) is not None:
            self.feature_extractor = None
            self.register_to_config(feature_extractor=[None, None])

        # remove hidden encoder
        self.unet.encoder_hid_proj = None
        self.config.encoder_hid_dim_type = None

        # restore original Unet attention processors layers
        self.unet.set_default_attn_processor()
