# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""
Processor class for LLaVa-NeXT.
"""


from typing import List, Optional, Union

from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from transformers.utils import TensorType
from transformers import AutoImageProcessor
from PIL import Image

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

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

    Args:
        image_processor ([`LlavaNextImageProcessor`], *optional*):
            The image processor is a required input.
        tokenizer ([`LlamaTokenizerFast`], *optional*):
            The tokenizer is a required input.
    """

    attributes = ["tokenizer"]
    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")

    def __init__(self, image_processor=None, tokenizer=None):            
        self.image_processor = image_processor
        self.tokenizer = tokenizer

    def __call__(
        self,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
        images: ImageInput = None,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length=None,
        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
        image_aspect_ratio='pad',
    ) -> 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`. 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.
            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.process_images(images, return_tensors, image_aspect_ratio)
        else:
            image_inputs = {}
        text_inputs = self.tokenizer(
            text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length, add_special_tokens=True,
        )

        return BatchFeature(data={**text_inputs, **image_inputs})

    def process_images(self, image_path, return_tensors, image_aspect_ratio='pad'):
        if isinstance(image_path, str):
            image = Image.open(image_path).convert('RGB')
        else:
            image = image_path
        if image_aspect_ratio == 'keep':
            max_hw, min_hw = max(image.size), min(image.size)
            aspect_ratio = max_hw / min_hw
            max_len, min_len = 448, 224
            shortest_edge = int(min(max_len / aspect_ratio, min_len))
            image = self.image_processor.preprocess(
                image, return_tensors=return_tensors, size={"shortest_edge": shortest_edge}
            )['pixel_values']
        elif image_aspect_ratio == 'pad':
            def expand2square(pil_img, background_color):
                width, height = pil_img.size
                if width == height:
                    return pil_img
                elif width > height:
                    result = Image.new(pil_img.mode, (width, width), background_color)
                    result.paste(pil_img, (0, (width - height) // 2))
                    return result
                else:
                    result = Image.new(pil_img.mode, (height, height), background_color)
                    result.paste(pil_img, ((height - width) // 2, 0))
                    return result
            image = expand2square(image, tuple(0 for x in self.image_processor.image_mean))
            image = self.image_processor.preprocess(image, return_tensors='pt')['pixel_values']
        else:
            image = self.image_processor.preprocess(image, return_tensors='pt')['pixel_values']
        output = {
            'pixel_values' : image.unsqueeze(dim=0).unsqueeze(dim=0)
        }
        return output
        
    # 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))
    
    @classmethod
    def _get_arguments_from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
        args = [AutoImageProcessor.from_pretrained(pretrained_model_name_or_path, **kwargs)]
        args.extend(super()._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs))
        return args