# coding=utf-8
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2022 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 copy
import json
import os
import tempfile
from typing import Any, Dict, Iterable, Optional, Tuple, Union

import numpy as np
from huggingface_hub import (
    create_repo,
    get_hf_file_metadata,
    hf_hub_download,
    hf_hub_url,
    repo_type_and_id_from_hf_id,
    upload_folder,
)
from huggingface_hub.utils import EntryNotFoundError

from .. import __version__
from ..utils.downloader import COMMUNITY_MODEL_PREFIX, get_path_from_url_with_filelock
from ..utils.log import logger
from .feature_extraction_utils import BatchFeature as BaseBatchFeature
from .utils import resolve_cache_dir

IMAGE_PROCESSOR_NAME = "preprocessor_config.json"


class BatchFeature(BaseBatchFeature):
    r"""
    Holds the output of the image processor specific `__call__` methods.

    This class is derived from a python dictionary and can be used as a dictionary.

    Args:
        data (`dict`):
            Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.).
        tensor_type (`Union[None, str, TensorType]`, *optional*):
            You can give a tensor_type here to convert the lists of integers in Paddle/Numpy Tensors at
            initialization.
    """


class ImageProcessingMixin(object):
    """
    This is an image processor mixin used to provide saving/loading functionality for sequential and image feature
    extractors.
    """

    _auto_class = None

    def __init__(self, **kwargs):
        """Set elements of `kwargs` as attributes."""
        # Pop "processor_class" as it should be saved as private attribute
        self._processor_class = kwargs.pop("processor_class", None)
        # Additional attributes without default values
        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

    def _set_processor_class(self, processor_class: str):
        """Sets processor class as an attribute."""
        self._processor_class = processor_class

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs):
        r"""
        Instantiate a type of [`~image_processing_utils.ImageProcessingMixin`] from an image processor.

        Args:
            pretrained_model_name_or_path (`str` or `os.PathLike`):
                This can be either:

                - a string, the *model id* of a pretrained image_processor hosted inside a model repo on
                  huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or
                  namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
                - a path to a *directory* containing a image processor file saved using the
                  [`~image_processing_utils.ImageProcessingMixin.save_pretrained`] method, e.g.,
                  `./my_model_directory/`.
                - a path or url to a saved image processor JSON *file*, e.g.,
                  `./my_model_directory/preprocessor_config.json`.
            cache_dir (`str` or `os.PathLike`, *optional*):
                Path to a directory in which a downloaded pretrained model image processor should be cached if the
                standard cache should not be used.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force to (re-)download the image processor files and override the cached versions if
                they exist.
            resume_download (`bool`, *optional*, defaults to `False`):
                Whether or not to delete incompletely received file. Attempts 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.
            use_auth_token (`str` or `bool`, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
                the token generated when running `huggingface-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.


                <Tip>

                To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>".

                </Tip>

            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
                If `False`, then this function returns just the final image processor object. If `True`, then this
                functions returns a `Tuple(image_processor, unused_kwargs)` where *unused_kwargs* is a dictionary
                consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of
                `kwargs` which has not been used to update `image_processor` and is otherwise ignored.
            kwargs (`Dict[str, Any]`, *optional*):
                The values in kwargs of any keys which are image processor attributes will be used to override the
                loaded values. Behavior concerning key/value pairs whose keys are *not* image processor attributes is
                controlled by the `return_unused_kwargs` keyword parameter.

        Returns:
            A image processor of type [`~image_processing_utils.ImageProcessingMixin`].

        Examples:

        ```python
        # We can't instantiate directly the base class *ImageProcessingMixin* so let's show the examples on a
        # derived class: *CLIPImageProcessor*
        image_processor = CLIPImageProcessor.from_pretrained(
            "openai/clip-vit-base-patch32"
        )  # Download image_processing_config from huggingface.co and cache.
        image_processor = CLIPImageProcessor.from_pretrained(
            "./test/saved_model/"
        )  # E.g. image processor (or model) was saved using *save_pretrained('./test/saved_model/')*
        image_processor = CLIPImageProcessor.from_pretrained("./test/saved_model/preprocessor_config.json")
        image_processor = CLIPImageProcessor.from_pretrained(
            "openai/clip-vit-base-patch32", do_normalize=False, foo=False
        )
        assert image_processor.do_normalize is False
        image_processor, unused_kwargs = CLIPImageProcessor.from_pretrained(
            "openai/clip-vit-base-patch32", do_normalize=False, foo=False, return_unused_kwargs=True
        )
        assert image_processor.do_normalize is False
        assert unused_kwargs == {"foo": False}
        ```"""
        image_processor_dict, kwargs = cls.get_image_processor_dict(pretrained_model_name_or_path, **kwargs)

        return cls.from_dict(image_processor_dict, **kwargs)

    def save_pretrained(self, save_directory: Union[str, os.PathLike], **kwargs):
        """
        Save an image processor object to the directory `save_directory`, so that it can be re-loaded using the
        [`~image_processing_utils.ImageProcessingMixin.from_pretrained`] class method.

        Args:
            save_directory (`str` or `os.PathLike`):
                Directory where the image processor 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_pretrained`
        output_image_processor_file = os.path.join(save_directory, IMAGE_PROCESSOR_NAME)

        self.to_json_file(output_image_processor_file)
        logger.info(f"Image processor saved in {output_image_processor_file}")

        return [output_image_processor_file]

    def save_to_hf_hub(
        self,
        repo_id: str,
        private: Optional[bool] = None,
        subfolder: Optional[str] = None,
        commit_message: Optional[str] = None,
        revision: Optional[str] = None,
        create_pr: bool = False,
    ):
        """
        Uploads all elements of this processor to a new HuggingFace Hub repository.
        Args:
            repo_id (str): Repository name for your processor in the Hub.
            private (bool, optional): Whether theprocessor is set to private
            subfolder (str, optional): Push to a subfolder of the repo instead of the root
            commit_message (str, optional) — The summary / title / first line of the generated commit. Defaults to: f"Upload {path_in_repo} with huggingface_hub"
            revision (str, optional) — The git revision to commit from. Defaults to the head of the "main" branch.
            create_pr (boolean, optional) — Whether or not to create a Pull Request with that commit. Defaults to False.
                If revision is not set, PR is opened against the "main" branch. If revision is set and is a branch, PR is opened against this branch.
                If revision is set and is not a branch name (example: a commit oid), an RevisionNotFoundError is returned by the server.

        Returns: The url of the commit of your model in the given repository.
        """
        repo_url = create_repo(repo_id, private=private, exist_ok=True)

        # Infer complete repo_id from repo_url
        # Can be different from the input `repo_id` if repo_owner was implicit
        _, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url)

        repo_id = f"{repo_owner}/{repo_name}"

        # Check if README file already exist in repo
        try:
            get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename="README.md", revision=revision))
            has_readme = True
        except EntryNotFoundError:
            has_readme = False

        with tempfile.TemporaryDirectory() as root_dir:
            if subfolder is not None:
                save_dir = os.path.join(root_dir, subfolder)
            else:
                save_dir = root_dir
            # save model
            self.save_pretrained(save_dir)
            # Add readme if does not exist
            logger.info("README.md not found, adding the default README.md")
            if not has_readme:
                with open(os.path.join(root_dir, "README.md"), "w") as f:
                    f.write(f"---\nlibrary_name: paddlenlp\n---\n# {repo_id}")

            # Upload model and return
            logger.info(f"Pushing to the {repo_id}. This might take a while")
            return upload_folder(
                repo_id=repo_id,
                repo_type="model",
                folder_path=root_dir,
                commit_message=commit_message,
                revision=revision,
                create_pr=create_pr,
            )

    @classmethod
    def get_image_processor_dict(
        cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
        """
        From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a
        image processor of type [`~image_processor_utils.ImageProcessingMixin`] using `from_dict`.

        Parameters:
            pretrained_model_name_or_path (`str` or `os.PathLike`):
                The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
            from_hf_hub (bool, optional): whether to load from Huggingface Hub
            subfolder (str, optional) An optional value corresponding to a folder inside the repo.


        Returns:
            `Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the image processor object.
        """
        cache_dir = kwargs.pop("cache_dir", None)
        from_hf_hub = kwargs.pop("from_hf_hub", False)
        subfolder = kwargs.pop("subfolder", None)
        cache_dir = resolve_cache_dir(pretrained_model_name_or_path, from_hf_hub, cache_dir)

        pretrained_model_name_or_path = str(pretrained_model_name_or_path)
        is_local = os.path.isdir(pretrained_model_name_or_path)
        if os.path.isdir(pretrained_model_name_or_path):
            if subfolder is None:
                resolved_image_processor_file = os.path.join(pretrained_model_name_or_path, IMAGE_PROCESSOR_NAME)
            else:
                resolved_image_processor_file = os.path.join(
                    pretrained_model_name_or_path, subfolder, IMAGE_PROCESSOR_NAME
                )
        elif os.path.isfile(pretrained_model_name_or_path):
            resolved_image_processor_file = pretrained_model_name_or_path
            is_local = True
        elif from_hf_hub:
            image_processor_file = IMAGE_PROCESSOR_NAME
            resolved_image_processor_file = hf_hub_download(
                repo_id=pretrained_model_name_or_path,
                filename=image_processor_file,
                cache_dir=cache_dir,
                subfolder=subfolder,
                library_name="PaddleNLP",
                library_version=__version__,
            )
        else:
            # Assuming from community-contributed pretrained models
            if subfolder is None:
                image_processor_file = "/".join(
                    [COMMUNITY_MODEL_PREFIX, pretrained_model_name_or_path, IMAGE_PROCESSOR_NAME]
                )
            else:
                image_processor_file = "/".join(
                    [COMMUNITY_MODEL_PREFIX, pretrained_model_name_or_path, subfolder, IMAGE_PROCESSOR_NAME]
                )
                # update cache_dir
                cache_dir = os.path.join(cache_dir, subfolder)
            try:
                # Load from local folder or from cache or download from model Hub and cache
                resolved_image_processor_file = get_path_from_url_with_filelock(image_processor_file, cache_dir)
            except EnvironmentError:
                # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted to
                # the original exception.
                raise
            except Exception:
                # For any other exception, we throw a generic error.
                raise EnvironmentError(
                    f"Can't load image processor for '{pretrained_model_name_or_path}'. If you were trying to load"
                    " it from 'BOS', make sure you don't have a local directory with the"
                    f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"
                    f" directory containing a {IMAGE_PROCESSOR_NAME} file"
                )

        try:
            # Load image_processor dict
            with open(resolved_image_processor_file, "r", encoding="utf-8") as reader:
                text = reader.read()
            image_processor_dict = json.loads(text)

        except json.JSONDecodeError:
            raise EnvironmentError(
                f"It looks like the config file at '{resolved_image_processor_file}' is not a valid JSON file."
            )

        if is_local:
            logger.info(f"loading configuration file {resolved_image_processor_file}")
        else:
            logger.info(f"loading configuration file from cache at {resolved_image_processor_file}")

        return image_processor_dict, kwargs

    @classmethod
    def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
        """
        Instantiates a type of [`~image_processing_utils.ImageProcessingMixin`] from a Python dictionary of parameters.

        Args:
            image_processor_dict (`Dict[str, Any]`):
                Dictionary that will be used to instantiate the image processor object. Such a dictionary can be
                retrieved from a pretrained checkpoint by leveraging the
                [`~image_processing_utils.ImageProcessingMixin.to_dict`] method.
            kwargs (`Dict[str, Any]`):
                Additional parameters from which to initialize the image processor object.

        Returns:
            [`~image_processing_utils.ImageProcessingMixin`]: The image processor object instantiated from those
            parameters.
        """
        return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)

        image_processor = cls(**image_processor_dict)

        # Update image_processor with kwargs if needed
        to_remove = []
        for key, value in kwargs.items():
            if hasattr(image_processor, key):
                setattr(image_processor, key, value)
                to_remove.append(key)
        for key in to_remove:
            kwargs.pop(key, None)

        if return_unused_kwargs:
            return image_processor, kwargs
        else:
            return image_processor

    def to_dict(self) -> Dict[str, Any]:
        """
        Serializes this instance to a Python dictionary.

        Returns:
            `Dict[str, Any]`: Dictionary of all the attributes that make up this image processor instance.
        """
        output = copy.deepcopy(self.__dict__)
        output["image_processor_type"] = self.__class__.__name__

        return output

    @classmethod
    def from_json_file(cls, json_file: Union[str, os.PathLike]):
        """
        Instantiates a image processor of type [`~image_processing_utils.ImageProcessingMixin`] from the path to a JSON
        file of parameters.

        Args:
            json_file (`str` or `os.PathLike`):
                Path to the JSON file containing the parameters.

        Returns:
            A image processor of type [`~image_processing_utils.ImageProcessingMixin`]: The image_processor object
            instantiated from that JSON file.
        """
        with open(json_file, "r", encoding="utf-8") as reader:
            text = reader.read()
        image_processor_dict = json.loads(text)
        return cls(**image_processor_dict)

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

        Returns:
            `str`: String containing all the attributes that make up this feature_extractor instance in JSON format.
        """
        dictionary = self.to_dict()

        for key, value in dictionary.items():
            if isinstance(value, np.ndarray):
                dictionary[key] = value.tolist()

        # make sure private name "_processor_class" is correctly
        # saved as "processor_class"
        _processor_class = dictionary.pop("_processor_class", None)
        if _processor_class is not None:
            dictionary["processor_class"] = _processor_class

        return json.dumps(dictionary, 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 image_processor instance's parameters will be saved.
        """
        with open(json_file_path, "w", encoding="utf-8") as writer:
            writer.write(self.to_json_string())

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


class BaseImageProcessor(ImageProcessingMixin):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def __call__(self, images, **kwargs) -> BatchFeature:
        """Preprocess an image or a batch of images."""
        return self.preprocess(images, **kwargs)

    def preprocess(self, images, **kwargs) -> BatchFeature:
        raise NotImplementedError("Each image processor must implement its own preprocess method")


VALID_SIZE_DICT_KEYS = ({"height", "width"}, {"shortest_edge"}, {"shortest_edge", "longest_edge"})


def is_valid_size_dict(size_dict):
    if not isinstance(size_dict, dict):
        return False

    size_dict_keys = set(size_dict.keys())
    for allowed_keys in VALID_SIZE_DICT_KEYS:
        if size_dict_keys == allowed_keys:
            return True
    return False


def convert_to_size_dict(
    size, max_size: Optional[int] = None, default_to_square: bool = True, height_width_order: bool = True
):
    # By default, if size is an int we assume it represents a tuple of (size, size).
    if isinstance(size, int) and default_to_square:
        if max_size is not None:
            raise ValueError("Cannot specify both size as an int, with default_to_square=True and max_size")
        return {"height": size, "width": size}
    # In other configs, if size is an int and default_to_square is False, size represents the length of
    # the shortest edge after resizing.
    elif isinstance(size, int) and not default_to_square:
        size_dict = {"shortest_edge": size}
        if max_size is not None:
            size_dict["longest_edge"] = max_size
        return size_dict
    # Otherwise, if size is a tuple it's either (height, width) or (width, height)
    elif isinstance(size, (tuple, list)) and height_width_order:
        return {"height": size[0], "width": size[1]}
    elif isinstance(size, (tuple, list)) and not height_width_order:
        return {"height": size[1], "width": size[0]}

    raise ValueError(f"Could not convert size input to size dict: {size}")


def get_size_dict(
    size: Union[int, Iterable[int], Dict[str, int]] = None,
    max_size: Optional[int] = None,
    height_width_order: bool = True,
    default_to_square: bool = True,
    param_name="size",
) -> dict:
    """
    Converts the old size parameter in the config into the new dict expected in the config. This is to ensure backwards
    compatibility with the old image processor configs and removes ambiguity over whether the tuple is in (height,
    width) or (width, height) format.

    - If `size` is tuple, it is converted to `{"height": size[0], "width": size[1]}` or `{"height": size[1], "width":
    size[0]}` if `height_width_order` is `False`.
    - If `size` is an int, and `default_to_square` is `True`, it is converted to `{"height": size, "width": size}`.
    - If `size` is an int and `default_to_square` is False, it is converted to `{"shortest_edge": size}`. If `max_size`
      is set, it is added to the dict as `{"longest_edge": max_size}`.

    Args:
        size (`Union[int, Iterable[int], Dict[str, int]]`, *optional*):
            The `size` parameter to be cast into a size dictionary.
        max_size (`Optional[int]`, *optional*):
            The `max_size` parameter to be cast into a size dictionary.
        height_width_order (`bool`, *optional*, defaults to `True`):
            If `size` is a tuple, whether it's in (height, width) or (width, height) order.
        default_to_square (`bool`, *optional*, defaults to `True`):
            If `size` is an int, whether to default to a square image or not.
    """
    if not isinstance(size, dict):
        size_dict = convert_to_size_dict(size, max_size, default_to_square, height_width_order)
        logger.info(
            f"{param_name} should be a dictionary on of the following set of keys: {VALID_SIZE_DICT_KEYS}, got {size}."
            f" Converted to {size_dict}.",
        )
    else:
        size_dict = size

    if not is_valid_size_dict(size_dict):
        raise ValueError(
            f"{param_name} must have one of the following set of keys: {VALID_SIZE_DICT_KEYS}, got {size_dict.keys()}"
        )
    return size_dict
