# Copyright 2025 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 SmolVLM.
"""

from datetime import timedelta

from ...image_utils import make_nested_list_of_images
from ...processing_utils import ProcessingKwargs, ProcessorMixin
from ...utils import auto_docstring, is_num2words_available, logging


# Adapted from transformers.models.smolvlm.video_processing_smolvlm.DEFAULT_VIDEO_INTRO
DEFAULT_VIDEO_INTRO = (
    "You are provided the following series of {frame_count} frames from a {video_duration} [H:MM:SS] video.\n"
)
# Adapted from transformers.models.smolvlm.video_processing_smolvlm.DEFAULT_MEDIA_OUTTRO
DEFAULT_MEDIA_OUTTRO = "\n\n"
# Adapted from transformers.models.smolvlm.video_processing_smolvlm.FRAME_TIMESTAMP_MESSAGE
FRAME_TIMESTAMP_MESSAGE = "\nFrame from {timestamp}:"

logger = logging.get_logger(__name__)


if is_num2words_available():
    from num2words import num2words
else:
    num2words = None


# The correct chat template to be used for videos after #38105
DEFAULT_CHAT_TEMPLATE = "<|im_start|>{% for message in messages %}{{message['role'] | capitalize}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '<image>' }}{% elif line['type'] == 'video' %}{{ '<video>' }}{% endif %}{% endfor %}<end_of_utterance>\n{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}"


def _prompt_split_image(
    image_seq_len, image_rows, image_cols, fake_token_around_image, image_token, global_image_token
):
    """Prompt with expanded image tokens for when the image is split into patches."""
    text_split_images = ""
    for n_h in range(image_rows):
        for n_w in range(image_cols):
            text_split_images += (
                f"{fake_token_around_image}" + f"<row_{n_h + 1}_col_{n_w + 1}>" + f"{image_token}" * image_seq_len
            )
        text_split_images += "\n"

    text_split_images += (
        f"\n{fake_token_around_image}"
        + f"{global_image_token}"
        + f"{image_token}" * image_seq_len
        + f"{fake_token_around_image}"
    )
    return text_split_images


def _prompt_single_image(image_seq_len, fake_token_around_image, image_token, global_image_token):
    """Prompt with expanded image tokens for a single image."""
    return (
        f"{fake_token_around_image}"
        + f"{global_image_token}"
        + f"{image_token}" * image_seq_len
        + f"{fake_token_around_image}"
    )


def get_image_prompt_string(
    image_rows, image_cols, image_seq_len, fake_token_around_image, image_token, global_image_token
):
    if image_rows == 0 and image_cols == 0:
        return _prompt_single_image(
            image_seq_len,
            fake_token_around_image=fake_token_around_image,
            image_token=image_token,
            global_image_token=global_image_token,
        )
    return _prompt_split_image(
        image_seq_len, image_rows, image_cols, fake_token_around_image, image_token, global_image_token
    )


class SmolVLMProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = {
        "text_kwargs": {
            "add_special_tokens": True,
            "padding": False,
            "is_split_into_words": False,
        },
        "images_kwargs": {
            "return_row_col_info": True,
        },
        "videos_kwargs": {
            "return_metadata": True,
        },
    }


@auto_docstring
class SmolVLMProcessor(ProcessorMixin):
    valid_processor_kwargs = SmolVLMProcessorKwargs

    def __init__(
        self,
        image_processor,
        tokenizer,
        video_processor,
        image_seq_len: int = 169,
        chat_template: str | None = None,
        **kwargs,
    ):
        r"""
        image_seq_len (`int`, *optional*, defaults to 169):
            The length of the image sequence i.e. the number of <image> tokens per image in the input.
            This parameter is used to build the string from the input prompt and image tokens and should match the
            value the model used. It is computed as: image_seq_len = int(((image_size // patch_size) ** 2) / (scale_factor**2))
        """
        self.fake_image_token = getattr(tokenizer, "fake_image_token", "<fake_token_around_image>")
        self.image_token = getattr(tokenizer, "image_token", "<image>")
        self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
        self.end_of_utterance_token = getattr(tokenizer, "end_of_utterance_token", "<end_of_utterance>")
        self.global_image_token = getattr(tokenizer, "global_image_token", "<global-img>")
        self.image_seq_len = image_seq_len
        self.video_token = getattr(tokenizer, "video_token", "<video>")

        if not num2words:
            raise ImportError(
                "Package `num2words` is required to run SmolVLM processor. Install it with `pip install num2words`."
            )

        super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template, **kwargs)

    def prepare_inputs_layout(self, images=None, text=None, videos=None, **kwargs):
        if images is not None:
            images = self.image_processor.fetch_images(images)
            images = make_nested_list_of_images(images)
        return super().prepare_inputs_layout(images=images, text=text, videos=videos, **kwargs)

    def validate_inputs(self, images=None, text=None, videos=None, **kwargs):
        super().validate_inputs(images=images, text=text, videos=videos, **kwargs)
        if text is None and images is None and videos is None:
            raise ValueError("You must provide one of `text`, `images` or `videos`.")
        if text is None and ((images is None) ^ (videos is not None)):
            raise ValueError("You must specify exactly one of `images` or `videos`")
        if text is not None:
            n_images_in_text = sum(sample.count(self.image_token) for sample in text)
            if n_images_in_text > 0 and images is None and videos is None:
                raise ValueError(f"We detected {n_images_in_text} tokens in the text but no images/videos were passed")
            if images is not None:
                n_images_per_sample = [sample.count(self.image_token) for sample in text]
                n_images_in_images = [len(sublist) for sublist in images]
                if n_images_in_images != n_images_per_sample:
                    raise ValueError(
                        f"The number of images in the text {n_images_per_sample} and images {n_images_in_images} should be the same."
                    )
            if videos is not None:
                n_videos_per_sample = [sample.count(self.video_token) for sample in text]
                n_videos_in_videos = [len(sublist) for sublist in videos]
                if n_videos_in_videos != n_videos_per_sample:
                    raise ValueError(
                        f"The number of videos in the text {n_videos_per_sample} and videos {n_videos_in_videos} should be the same."
                    )

    def replace_image_token(self, image_inputs: dict, image_idx: int, **kwargs) -> str:
        rows = [row for row_list in image_inputs["rows"] for row in row_list]
        cols = [col for col_list in image_inputs["cols"] for col in col_list]
        return get_image_prompt_string(
            rows[image_idx],
            cols[image_idx],
            self.image_seq_len,
            fake_token_around_image=self.fake_image_token,
            image_token=self.image_token,
            global_image_token=self.global_image_token,
        )

    def replace_video_token(self, video_inputs: dict, video_idx: int, **kwargs) -> str:
        num_frames = video_inputs["pixel_values"].shape[1]
        metadata = video_inputs["video_metadata"][video_idx]
        if metadata.fps is None:
            logger.warning_once(
                "SmolVLM requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
                "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
                "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
            )
            metadata.fps = 24
        timestamps = [(int(second // 60), int(second % 60)) for second in metadata.timestamps]
        duration = int(metadata.duration) if metadata.duration is not None else int(metadata.timestamps[-1])
        duration_td = timedelta(seconds=int(duration))
        prompt = DEFAULT_VIDEO_INTRO.format(frame_count=num2words(num_frames), video_duration=str(duration_td))
        for timestamp in timestamps:
            image_prompt_string = _prompt_single_image(
                self.image_seq_len,
                image_token=self.image_token,
                fake_token_around_image=self.fake_image_token,
                global_image_token=self.global_image_token,
            )
            timestamp_str = f"{timestamp[0]:02d}:{timestamp[1]:02d}"
            prompt += FRAME_TIMESTAMP_MESSAGE.format(timestamp=timestamp_str) + image_prompt_string
        return prompt + DEFAULT_MEDIA_OUTTRO

    def apply_chat_template(
        self,
        conversation: list[dict[str, str]] | list[list[dict[str, str]]],
        chat_template: str | None = None,
        processor_kwargs: dict | None = None,
        **kwargs,
    ) -> str:
        """
        Similar to the `apply_chat_template` method on tokenizers, this method applies a Jinja template to input
        conversations to turn them into a single tokenizable string.

        The input is expected to be in the following format, where each message content is a list consisting of text and
        optionally image or video inputs. One can also provide an image, video, URL or local path which will be used to form
        `pixel_values` when `return_dict=True`. If not provided, one will get only the formatted text, optionally tokenized text.

        conversation = [
            {
                "role": "user",
                "content": [
                    {"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
                    {"type": "text", "text": "Please describe this image in detail."},
                ],
            },
        ]

        Args:
            conversation (`Union[list[Dict, [str, str]], list[list[dict[str, str]]]]`):
                The conversation to format.
            chat_template (`Optional[str]`, *optional*):
                The Jinja template to use for formatting the conversation. If not provided, the tokenizer's
                chat template is used.
        """
        if isinstance(conversation, (list, tuple)) and (
            isinstance(conversation[0], (list, tuple)) or hasattr(conversation[0], "content")
        ):
            conversations = conversation
        else:
            conversations = [conversation]

        has_video = any(
            (isinstance(content, dict) and content["type"] == "video")
            for conversation in conversations
            for message in conversation
            for content in (message.get("content") or [])
        )
        if chat_template is None and has_video:
            # re-assign to the correct default template for BC, if user is not requesting their own template
            chat_template = DEFAULT_CHAT_TEMPLATE

        # Users might be passing processor kwargs simply as `**kwargs`
        if processor_kwargs:
            processor_kwargs.setdefault("num_frames", self.video_processor.num_frames)
            processor_kwargs.setdefault("fps", self.video_processor.fps)
        else:
            kwargs.setdefault("num_frames", self.video_processor.num_frames)
            kwargs.setdefault("fps", self.video_processor.fps)

        return super().apply_chat_template(conversation, chat_template, processor_kwargs=processor_kwargs, **kwargs)

    @property
    def unused_input_names(self) -> list[str]:
        return ["rows", "cols", "video_metadata"]


__all__ = ["SmolVLMProcessor"]