# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION.  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.
""" ConfigMixinuration base class and utilities."""


import copy
import inspect
import json
import os
import re
from typing import Any, Dict, Tuple, Union

from huggingface_hub import hf_hub_download
from requests import HTTPError

from . import __version__
from .utils import (
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    logging,
)


logger = logging.get_logger(__name__)

_re_configuration_file = re.compile(r"config\.(.*)\.json")


class ConfigMixin:
    r"""
    Base class for all configuration classes. Handles a few parameters common to all models' configurations as well as
    methods for loading/downloading/saving configurations.

    """
    config_name = None

    def register(self, **kwargs):
        if self.config_name is None:
            raise NotImplementedError(f"Make sure that {self.__class__} has defined a class name `config_name`")
        kwargs["_class_name"] = self.__class__.__name__
        kwargs["_diffusers_version"] = __version__

        for key, value in kwargs.items():
            try:
                setattr(self, key, value)
            except AttributeError as err:
                logger.error(f"Can't set {key} with value {value} for {self}")
                raise err

        if not hasattr(self, "_dict_to_save"):
            self._dict_to_save = {}

        self._dict_to_save.update(kwargs)

    def save_config(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
        """
        Save a configuration object to the directory `save_directory`, so that it can be re-loaded using the
        [`~ConfigMixin.from_config`] class method.

        Args:
            save_directory (`str` or `os.PathLike`):
                Directory where the configuration JSON file will be saved (will be created if it does not exist).
            kwargs:
                Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
        """
        if os.path.isfile(save_directory):
            raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")

        os.makedirs(save_directory, exist_ok=True)

        # If we save using the predefined names, we can load using `from_config`
        output_config_file = os.path.join(save_directory, self.config_name)

        self.to_json_file(output_config_file)
        logger.info(f"ConfigMixinuration saved in {output_config_file}")

    @classmethod
    def from_config(cls, pretrained_model_name_or_path: Union[str, os.PathLike], return_unused_kwargs=False, **kwargs):
        config_dict = cls.get_config_dict(pretrained_model_name_or_path=pretrained_model_name_or_path, **kwargs)

        init_dict, unused_kwargs = cls.extract_init_dict(config_dict, **kwargs)

        model = cls(**init_dict)

        if return_unused_kwargs:
            return model, unused_kwargs
        else:
            return model

    @classmethod
    def get_config_dict(
        cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
        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)
        use_auth_token = kwargs.pop("use_auth_token", None)
        local_files_only = kwargs.pop("local_files_only", False)
        revision = kwargs.pop("revision", None)

        user_agent = {"file_type": "config"}

        pretrained_model_name_or_path = str(pretrained_model_name_or_path)

        if cls.config_name is None:
            raise ValueError(
                "`self.config_name` is not defined. Note that one should not load a config from "
                "`ConfigMixin`. Please make sure to define `config_name` in a class inheriting from `ConfigMixin`"
            )

        if os.path.isfile(pretrained_model_name_or_path):
            config_file = pretrained_model_name_or_path
        elif os.path.isdir(pretrained_model_name_or_path):
            if os.path.isfile(os.path.join(pretrained_model_name_or_path, cls.config_name)):
                # Load from a PyTorch checkpoint
                config_file = os.path.join(pretrained_model_name_or_path, cls.config_name)
            else:
                raise EnvironmentError(
                    f"Error no file named {cls.config_name} found in directory {pretrained_model_name_or_path}."
                )
        else:
            try:
                # Load from URL or cache if already cached
                config_file = hf_hub_download(
                    pretrained_model_name_or_path,
                    filename=cls.config_name,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    proxies=proxies,
                    resume_download=resume_download,
                    local_files_only=local_files_only,
                    use_auth_token=use_auth_token,
                    user_agent=user_agent,
                )

            except RepositoryNotFoundError:
                raise EnvironmentError(
                    f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier listed"
                    " on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a token"
                    " having permission to this repo with `use_auth_token` or log in with `huggingface-cli login` and"
                    " pass `use_auth_token=True`."
                )
            except RevisionNotFoundError:
                raise EnvironmentError(
                    f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for"
                    " this model name. Check the model page at"
                    f" 'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
                )
            except EntryNotFoundError:
                raise EnvironmentError(
                    f"{pretrained_model_name_or_path} does not appear to have a file named {cls.config_name}."
                )
            except HTTPError as err:
                raise EnvironmentError(
                    "There was a specific connection error when trying to load"
                    f" {pretrained_model_name_or_path}:\n{err}"
                )
            except ValueError:
                raise EnvironmentError(
                    f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
                    f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
                    f" directory containing a {cls.config_name} file.\nCheckout your internet connection or see how to"
                    " run the library in offline mode at"
                    " 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
                )
            except EnvironmentError:
                raise EnvironmentError(
                    f"Can't load config for '{pretrained_model_name_or_path}'. If you were trying to load it from "
                    "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
                    f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
                    f"containing a {cls.config_name} file"
                )

        try:
            # Load config dict
            config_dict = cls._dict_from_json_file(config_file)
        except (json.JSONDecodeError, UnicodeDecodeError):
            raise EnvironmentError(f"It looks like the config file at '{config_file}' is not a valid JSON file.")

        return config_dict

    @classmethod
    def extract_init_dict(cls, config_dict, **kwargs):
        expected_keys = set(dict(inspect.signature(cls.__init__).parameters).keys())
        expected_keys.remove("self")
        init_dict = {}
        for key in expected_keys:
            if key in kwargs:
                # overwrite key
                init_dict[key] = kwargs.pop(key)
            elif key in config_dict:
                # use value from config dict
                init_dict[key] = config_dict.pop(key)

        unused_kwargs = config_dict.update(kwargs)

        passed_keys = set(init_dict.keys())
        if len(expected_keys - passed_keys) > 0:
            logger.warning(
                f"{expected_keys - passed_keys} was not found in config. Values will be initialized to default values."
            )

        return init_dict, unused_kwargs

    @classmethod
    def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
        with open(json_file, "r", encoding="utf-8") as reader:
            text = reader.read()
        return json.loads(text)

    # def __eq__(self, other):
    #    return self.__dict__ == other.__dict__

    # def __repr__(self):
    #    return f"{self.__class__.__name__} {self.to_json_string()}"

    @property
    def config(self) -> Dict[str, Any]:
        output = copy.deepcopy(self._dict_to_save)
        return output

    def to_json_string(self) -> str:
        """
        Serializes this instance to a JSON string.

        Returns:
            `str`: String containing all the attributes that make up this configuration instance in JSON format.
        """
        config_dict = self._dict_to_save
        return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"

    def to_json_file(self, json_file_path: Union[str, os.PathLike]):
        """
        Save this instance to a JSON file.

        Args:
            json_file_path (`str` or `os.PathLike`):
                Path to the JSON file in which this configuration instance's parameters will be saved.
        """
        with open(json_file_path, "w", encoding="utf-8") as writer:
            writer.write(self.to_json_string())