# coding=utf-8
# Copyright 2021 The HuggingFace Inc. 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
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"""Feature extractor class for CLIP."""

from typing import List, Optional, Union

import numpy as np
from PIL import Image

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
from ...utils import TensorType, logging


logger = logging.get_logger(__name__)


class CLIPFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
    r"""
    Constructs a CLIP feature extractor.

    This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
    should refer to this superclass for more information regarding those methods.

    Args:
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the input to a certain `size`.
        size (`int`, *optional*, defaults to 224):
            Resize the input to the given size. Only has an effect if `do_resize` is set to `True`.
        resample (`int`, *optional*, defaults to `PIL.Image.BICUBIC`):
            An optional resampling filter. This can be one of `PIL.Image.NEAREST`, `PIL.Image.BOX`,
            `PIL.Image.BILINEAR`, `PIL.Image.HAMMING`, `PIL.Image.BICUBIC` or `PIL.Image.LANCZOS`. Only has an effect
            if `do_resize` is set to `True`.
        do_center_crop (`bool`, *optional*, defaults to `True`):
            Whether to crop the input at the center. If the input size is smaller than `crop_size` along any edge, the
            image is padded with 0's and then center cropped.
        crop_size (`int`, *optional*, defaults to 224):
            Desired output size when applying center-cropping. Only has an effect if `do_center_crop` is set to `True`.
        do_normalize (`bool`, *optional*, defaults to `True`):
            Whether or not to normalize the input with `image_mean` and `image_std`.
        image_mean (`List[int]`, defaults to `[0.485, 0.456, 0.406]`):
            The sequence of means for each channel, to be used when normalizing images.
        image_std (`List[int]`, defaults to `[0.229, 0.224, 0.225]`):
            The sequence of standard deviations for each channel, to be used when normalizing images.
        convert_rgb (`bool`, defaults to `True`):
            Whether or not to convert `PIL.Image.Image` into `RGB` format
    """

    model_input_names = ["pixel_values"]

    def __init__(
        self,
        do_resize=True,
        size=224,
        resample=Image.BICUBIC,
        do_center_crop=True,
        crop_size=224,
        do_normalize=True,
        image_mean=None,
        image_std=None,
        do_convert_rgb=True,
        **kwargs
    ):
        super().__init__(**kwargs)
        self.do_resize = do_resize
        self.size = size
        self.resample = resample
        self.do_center_crop = do_center_crop
        self.crop_size = crop_size
        self.do_normalize = do_normalize
        self.image_mean = image_mean if image_mean is not None else [0.48145466, 0.4578275, 0.40821073]
        self.image_std = image_std if image_std is not None else [0.26862954, 0.26130258, 0.27577711]
        self.do_convert_rgb = do_convert_rgb

    def __call__(
        self,
        images: Union[
            Image.Image, np.ndarray, "torch.Tensor", List[Image.Image], List[np.ndarray], List["torch.Tensor"]  # noqa
        ],
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs
    ) -> BatchFeature:
        """
        Main method to prepare for the model one or several image(s).

        <Tip warning={true}>

        NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
        PIL images.

        </Tip>

        Args:
            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
                number of channels, H and W are image height and width.

            return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `'np'`):
                If set, will return tensors of a particular framework. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return NumPy `np.ndarray` objects.
                - `'jax'`: Return JAX `jnp.ndarray` objects.

        Returns:
            [`BatchFeature`]: A [`BatchFeature`] with the following fields:

            - **pixel_values** -- Pixel values to be fed to a model.
        """
        # Input type checking for clearer error
        valid_images = False

        # Check that images has a valid type
        if isinstance(images, (Image.Image, np.ndarray)) or is_torch_tensor(images):
            valid_images = True
        elif isinstance(images, (list, tuple)):
            if len(images) == 0 or isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]):
                valid_images = True

        if not valid_images:
            raise ValueError(
                "Images must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single example), "
                "`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of examples)."
            )

        is_batched = bool(
            isinstance(images, (list, tuple))
            and (isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]))
        )

        if not is_batched:
            images = [images]

        # transformations (convert rgb + resizing + center cropping + normalization)
        if self.do_convert_rgb:
            images = [self.convert_rgb(image) for image in images]
        if self.do_resize and self.size is not None and self.resample is not None:
            images = [self.resize(image=image, size=self.size, resample=self.resample) for image in images]
        if self.do_center_crop and self.crop_size is not None:
            images = [self.center_crop(image, self.crop_size) for image in images]
        if self.do_normalize:
            images = [self.normalize(image=image, mean=self.image_mean, std=self.image_std) for image in images]

        # return as BatchFeature
        data = {"pixel_values": images}
        encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)

        return encoded_inputs

    def convert_rgb(self, image):
        """
        Converts `image` to RGB format. Note that this will trigger a conversion of `image` to a PIL Image.

        Args:
            image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
                The image to convert.
        """
        self._ensure_format_supported(image)
        if not isinstance(image, Image.Image):
            return image

        return image.convert("RGB")

    def center_crop(self, image, size):
        """
        Crops `image` to the given size using a center crop. Note that if the image is too small to be cropped to the
        size is given, it will be padded (so the returned result has the size asked).

        Args:
            image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
                The image to resize.
            size (`int` or `Tuple[int, int]`):
                The size to which crop the image.
        """
        self._ensure_format_supported(image)
        if not isinstance(size, tuple):
            size = (size, size)

        if not isinstance(image, Image.Image):
            image = self.to_pil_image(image)

        image_width, image_height = image.size
        crop_height, crop_width = size

        crop_top = int((image_height - crop_height + 1) * 0.5)
        crop_left = int((image_width - crop_width + 1) * 0.5)

        return image.crop((crop_left, crop_top, crop_left + crop_width, crop_top + crop_height))

    def resize(self, image, size, resample=Image.BICUBIC):
        """
        Resizes `image`. Note that this will trigger a conversion of `image` to a PIL Image.

        Args:
            image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
                The image to resize.
            size (`int` or `Tuple[int, int]`):
                The size to use for resizing the image. If `int` it will be resized to match the shorter side
            resample (`int`, *optional*, defaults to `PIL.Image.BILINEAR`):
                The filter to user for resampling.
        """
        self._ensure_format_supported(image)

        if not isinstance(image, Image.Image):
            image = self.to_pil_image(image)
        if isinstance(size, tuple):
            new_w, new_h = size
        else:
            width, height = image.size
            short, long = (width, height) if width <= height else (height, width)
            if short == size:
                return image
            new_short, new_long = size, int(size * long / short)
            new_w, new_h = (new_short, new_long) if width <= height else (new_long, new_short)
        return image.resize((new_w, new_h), resample)
