# 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
#
#     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.
"""
Processor class for LLaVa-NeXT-Video.
"""

from typing import TYPE_CHECKING, List, Optional, Union

from ...feature_extraction_utils import BatchFeature
from ...image_utils import ImageInput, VideoInput
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`
    """

    # 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"]
    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, **kwargs):
        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 = {}

        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.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))

    @property
    def default_chat_template(self):
        """
        This default vicuna template formats inputs in the form of a chat history. For each message in the chat history:
        * the template will output the role of the speaker followed by the content of the message.
        * content is a list of strings and images.
        * If the content element is an image, the template will output a sequence of <image> or <video> tokens

        Example:

        ```python
        messages = [{
            "role": "user",
            "content": [
                {"type": "text", "text": "What’s the content of this video?"},
                {"type": "video"},
                ],
        },
        {
            "role": "assistant",
            "content": [{"type": "text", "text": "This picture shows a red stop sign."},]
        }]
        ```

        Will create outputs like:
        ```
        USER: <video>\nWhat is the content of this video?
        ASSITANT: This picture shows a red stop sign
        ```
        """
        # fmt: off
        return (
            "{% for message in messages %}"
                "{% if message['role'] == 'system' %}"
                    "{{ message['content'][0]['text'] }}"
                "{% else %}"
                    "{{ message['role'].upper() + ': '}}"
                "{% endif %}"

                "{# Render all images first #}"
                "{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}"
                    "{{ '<image>\n' }}"
                "{% endfor %}"

                "{# Render all videos next #}"
                "{% for content in message['content'] | selectattr('type', 'equalto', 'video') %}"
                    "{{ '<video>\n' }}"
                "{% endfor %}"

                "{# Render all text finally #}"
                "{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}"
                    "{{ content['text'] + ' '}}"
                "{% endfor %}"
            "{% endfor %}"
            "{% if add_generation_prompt %}"
                "{{ 'ASSISTANT:' }}"
            "{% endif %}"
        )
        # fmt: on
