# coding=utf-8
# Copyright 2024 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""
Processor class for LLaVa-NeXT-Video.
"""

from typing import TYPE_CHECKING, List, Optional, Union

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...image_processing_utils import select_best_resolution
from ...image_utils import ImageInput, VideoInput, get_image_size, to_numpy_array
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType, logging


if TYPE_CHECKING:
    pass

logger = logging.get_logger(__name__)


class LlavaNextVideoProcessor(ProcessorMixin):
    r"""
    Constructs a LLaVa-NeXT-Video processor which wraps a LLaVa-NeXT image processor, LLaVa-NeXT-Video video processor and
    a LLaMa tokenizer into a single processor.

    [`LlavaNextVideoProcessor`] offers all the functionalities of [`LlavaNextImageProcessor`], [`LlavaNextVideoImageProcessor`] and
    [`LlamaTokenizerFast`]. See the [`~LlavaNextVideoProcessor.__call__`] and [`~LlavaNextVideoProcessor.decode`] for more information.

    Args:
        video_processor ([`LlavaNextVideoImageProcessor`], *optional*):
            The video processor is a required input.
        image_processor ([`LlavaNextImageProcessor`], *optional*):
            The image processor is a required input.
        tokenizer ([`LlamaTokenizerFast`], *optional*):
            The tokenizer is a required input.
        chat_template (`str`, *optional*):
            Jinja chat template that will be used in tokenizer's `apply_chat_template`
        patch_size (`int`, *optional*):
            Patch size from the vision tower.
        vision_feature_select_strategy (`str`, *optional*):
            The feature selection strategy used to select the vision feature from the vision backbone.
            Shoudl be same as in model's config
        video_token (`str`, *optional*, defaults to `"<video>"`):
            Special token used to denote video location.
        image_token (`str`, *optional*, defaults to `"<image>"`):
            Special token used to denote image location.
        num_additional_image_tokens (`int`, *optional*, defaults to 0):
            Number of additional tokens added to the image embeddings, such as CLS (+1). If the backbone has no CLS or other
            extra tokens appended, no need to set this arg.
    """

    # video and image processor share same args, but have different processing logic
    # only image processor config is saved in the hub
    attributes = ["video_processor", "image_processor", "tokenizer"]
    valid_kwargs = [
        "chat_template",
        "patch_size",
        "vision_feature_select_strategy",
        "image_token",
        "video_token",
        "num_additional_image_tokens",
    ]
    image_processor_class = "LlavaNextImageProcessor"
    video_processor_class = "LlavaNextVideoImageProcessor"
    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")

    def __init__(
        self,
        video_processor=None,
        image_processor=None,
        tokenizer=None,
        chat_template=None,
        patch_size=None,
        vision_feature_select_strategy=None,
        video_token="<video>",
        image_token="<image>",
        num_additional_image_tokens=0,
        **kwargs,
    ):
        self.patch_size = patch_size
        self.num_additional_image_tokens = num_additional_image_tokens
        self.vision_feature_select_strategy = vision_feature_select_strategy
        self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
        self.video_token = tokenizer.video_token if hasattr(tokenizer, "video_token") else video_token
        super().__init__(video_processor, image_processor, tokenizer, chat_template=chat_template)

    def __call__(
        self,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
        images: ImageInput = None,
        videos: VideoInput = None,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: int = None,
        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
    ) -> BatchFeature:
        """
        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
        and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
        LlavaNextImageProcessor's [`~LlavaNextImageProcessor.__call__`] if `images` is not `None`. To prepare the video(s),
        this method forwards the `videos` and `kwrags` arguments to LlavaNextVideoImageProcessor's
        [`~LlavaNextVideoImageProcessor.__call__`] if `videos` is not `None`. Please refer to the doctsring
        of the above two methods for more information.

        Args:
            text (`str`, `List[str]`, `List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            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. Both channels-first and channels-last formats are supported.
            videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
                tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
                Select a strategy to pad the returned sequences (according to the model's padding side and padding
                index) among:
                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
                  sequence if provided).
                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
                  acceptable input length for the model if that argument is not provided.
                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
                  lengths).
            max_length (`int`, *optional*):
                Maximum length of the returned list and optionally padding length (see above).
            truncation (`bool`, *optional*):
                Activates truncation to cut input sequences longer than `max_length` to `max_length`.
            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                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:

            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
              `None`).
            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
        """
        if images is not None:
            image_inputs = self.image_processor(images, return_tensors=return_tensors)
        else:
            image_inputs = {}

        if videos is not None:
            videos_inputs = self.video_processor(videos, return_tensors=return_tensors)
        else:
            videos_inputs = {}

        if isinstance(text, str):
            text = [text]
        elif not isinstance(text, list) and not isinstance(text[0], str):
            raise ValueError("Invalid input text. Please provide a string, or a list of strings")

        if image_inputs:
            image_sizes = iter(image_inputs["image_sizes"])
            height, width = get_image_size(to_numpy_array(image_inputs["pixel_values"][0][0]))
            prompt_strings = []
            for sample in text:
                while self.image_token in sample:
                    image_size = next(image_sizes)
                    if not isinstance(image_size, (list, tuple)):
                        # cast to list to avoid numerical precision errors when calculating unpadding
                        image_size = image_size.tolist()
                    orig_height, orig_width = image_size
                    num_image_tokens = self._get_number_of_features(orig_height, orig_width, height, width)
                    if self.vision_feature_select_strategy == "default":
                        num_image_tokens -= 1
                    sample = sample.replace(self.image_token, "<placeholder>" * num_image_tokens, 1)
                prompt_strings.append(sample)
            text = [sample.replace("<placeholder>", self.image_token) for sample in prompt_strings]

        # videos are easier, simply get frames and multiply
        if videos_inputs:
            one_video = videos_inputs.get("pixel_values_videos")[0]
            if isinstance(one_video, (list, tuple)):
                one_video = np.array(one_video)
            else:
                one_video = to_numpy_array(one_video)
            height, width = get_image_size(one_video[0])
            num_frames = one_video.shape[0]  # frame dim is always after batch dim

            # no `self.num_additional_image_tokens` added because video always has a default feature selection strategy
            num_image_tokens = (height // self.patch_size) * (width // self.patch_size)
            num_video_tokens = num_image_tokens // 4 * num_frames  # divide by 4 needed for avg pooling layer
            prompt_strings = []
            for sample in text:
                sample = sample.replace(self.video_token, self.video_token * num_video_tokens)
                prompt_strings.append(sample)
            text = prompt_strings

        text_inputs = self.tokenizer(
            text,
            return_tensors=return_tensors,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
        )
        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs})

    # Copied from transformers.models.llava_next.processing_llava_next.LlavaNextProcessor._get_number_of_features
    def _get_number_of_features(self, orig_height: int, orig_width: int, height: int, width: int) -> int:
        image_grid_pinpoints = self.image_processor.image_grid_pinpoints

        height_best_resolution, width_best_resolution = select_best_resolution(
            [orig_height, orig_width], image_grid_pinpoints
        )
        scale_height, scale_width = height_best_resolution // height, width_best_resolution // width

        patches_height = height // self.patch_size
        patches_width = width // self.patch_size
        unpadded_features, newline_features = self._get_unpadded_features(
            orig_height, orig_width, patches_height, patches_width, scale_height, scale_width
        )
        # The base patch covers the entire image (+1 for the CLS)
        base_features = patches_height * patches_width + self.num_additional_image_tokens
        num_image_tokens = unpadded_features + newline_features + base_features
        return num_image_tokens

    # Copied from transformers.models.llava_next.processing_llava_next.LlavaNextProcessor._get_unpadded_features
    def _get_unpadded_features(self, height, width, patches_height, patches_width, scale_height, scale_width):
        """
        Get number of features for a given image with height/width. LLaVA-NeXT is different from LLaVA
        because it divided each image into patches depending on its resolution. Therefore we need to calculate how many
        patches an image is divided into and get the number of features from that.
        """
        current_height = patches_height * scale_height
        current_width = patches_width * scale_width

        original_aspect_ratio = width / height
        current_aspect_ratio = current_width / current_height
        if original_aspect_ratio > current_aspect_ratio:
            new_height = int(round(height * (current_width / width), 7))
            padding = (current_height - new_height) // 2
            current_height -= padding * 2
        else:
            new_width = int(round(width * (current_height / height), 7))
            padding = (current_width - new_width) // 2
            current_width -= padding * 2

        unpadded_features = current_height * current_width
        newline_features = current_height
        return (unpadded_features, newline_features)

    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
        the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    @property
    # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


__all__ = ["LlavaNextVideoProcessor"]
