from typing import Union

from ..generation import GenerationConfig
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import Pipeline, build_pipeline_init_args


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES
    from .pt_utils import KeyDataset

logger = logging.get_logger(__name__)


@add_end_docstrings(build_pipeline_init_args(has_tokenizer=True, has_image_processor=True))
class VisualQuestionAnsweringPipeline(Pipeline):
    """
    Visual Question Answering pipeline using a `AutoModelForVisualQuestionAnswering`. This pipeline is currently only
    available in PyTorch.

    Unless the model you're using explicitly sets these generation parameters in its configuration files
    (`generation_config.json`), the following default values will be used:
    - max_new_tokens: 256

    Example:

    ```python
    >>> from transformers import pipeline

    >>> oracle = pipeline(model="dandelin/vilt-b32-finetuned-vqa")
    >>> image_url = "https://huggingface.co/datasets/Narsil/image_dummy/raw/main/lena.png"
    >>> oracle(question="What is she wearing ?", image=image_url)
    [{'score': 0.948, 'answer': 'hat'}, {'score': 0.009, 'answer': 'fedora'}, {'score': 0.003, 'answer': 'clothes'}, {'score': 0.003, 'answer': 'sun hat'}, {'score': 0.002, 'answer': 'nothing'}]

    >>> oracle(question="What is she wearing ?", image=image_url, top_k=1)
    [{'score': 0.948, 'answer': 'hat'}]

    >>> oracle(question="Is this a person ?", image=image_url, top_k=1)
    [{'score': 0.993, 'answer': 'yes'}]

    >>> oracle(question="Is this a man ?", image=image_url, top_k=1)
    [{'score': 0.996, 'answer': 'no'}]
    ```

    Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)

    This visual question answering pipeline can currently be loaded from [`pipeline`] using the following task
    identifiers: `"visual-question-answering", "vqa"`.

    The models that this pipeline can use are models that have been fine-tuned on a visual question answering task. See
    the up-to-date list of available models on
    [huggingface.co/models](https://huggingface.co/models?filter=visual-question-answering).
    """

    _load_processor = False
    _load_image_processor = True
    _load_feature_extractor = False
    _load_tokenizer = True

    _pipeline_calls_generate = True
    # Make sure the docstring is updated when the default generation config is changed
    _default_generation_config = GenerationConfig(
        max_new_tokens=256,
    )

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.check_model_type(MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES)

    def _sanitize_parameters(self, top_k=None, padding=None, truncation=None, timeout=None, **kwargs):
        preprocess_params, postprocess_params = {}, {}
        if padding is not None:
            preprocess_params["padding"] = padding
        if truncation is not None:
            preprocess_params["truncation"] = truncation
        if timeout is not None:
            preprocess_params["timeout"] = timeout
        if top_k is not None:
            postprocess_params["top_k"] = top_k

        forward_params = {}
        if getattr(self, "assistant_model", None) is not None:
            forward_params["assistant_model"] = self.assistant_model
        if getattr(self, "assistant_tokenizer", None) is not None:
            forward_params["tokenizer"] = self.tokenizer
            forward_params["assistant_tokenizer"] = self.assistant_tokenizer

        return preprocess_params, forward_params, postprocess_params

    def __call__(
        self,
        image: Union["Image.Image", str, list["Image.Image"], list[str], "KeyDataset"],
        question: str | list[str] | None = None,
        **kwargs,
    ):
        r"""
        Answers open-ended questions about images. The pipeline accepts several types of inputs which are detailed
        below:

        - `pipeline(image=image, question=question)`
        - `pipeline({"image": image, "question": question})`
        - `pipeline([{"image": image, "question": question}])`
        - `pipeline([{"image": image, "question": question}, {"image": image, "question": question}])`

        Args:
            image (`str`, `list[str]`, `PIL.Image`, `list[PIL.Image]` or `KeyDataset`):
                The pipeline handles three types of images:

                - A string containing a http link pointing to an image
                - A string containing a local path to an image
                - An image loaded in PIL directly

                The pipeline accepts either a single image or a batch of images. If given a single image, it can be
                broadcasted to multiple questions.
                For dataset: the passed in dataset must be of type `transformers.pipelines.pt_utils.KeyDataset`
                Example:
                ```python
                >>> from transformers.pipelines.pt_utils import KeyDataset
                >>> from datasets import load_dataset

                >>> dataset = load_dataset("detection-datasets/coco")
                >>> oracle(image=KeyDataset(dataset, "image"), question="What's in this image?")

                ```
            question (`str`, `list[str]`):
                The question(s) asked. If given a single question, it can be broadcasted to multiple images.
                If multiple images and questions are given, each and every question will be broadcasted to all images
                (same effect as a Cartesian product)
            top_k (`int`, *optional*, defaults to 5):
                The number of top labels that will be returned by the pipeline. If the provided number is higher than
                the number of labels available in the model configuration, it will default to the number of labels.
            timeout (`float`, *optional*, defaults to None):
                The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and
                the call may block forever.
        Return:
            A dictionary or a list of dictionaries containing the result. The dictionaries contain the following keys:

            - **label** (`str`) -- The label identified by the model.
            - **score** (`int`) -- The score attributed by the model for that label.
        """
        is_dataset = isinstance(image, KeyDataset)
        is_image_batch = isinstance(image, list) and all(isinstance(item, (Image.Image, str)) for item in image)
        is_question_batch = isinstance(question, list) and all(isinstance(item, str) for item in question)

        if isinstance(image, (Image.Image, str)) and isinstance(question, str):
            inputs = {"image": image, "question": question}
        elif (is_image_batch or is_dataset) and isinstance(question, str):
            inputs = [{"image": im, "question": question} for im in image]
        elif isinstance(image, (Image.Image, str)) and is_question_batch:
            inputs = [{"image": image, "question": q} for q in question]
        elif (is_image_batch or is_dataset) and is_question_batch:
            question_image_pairs = []
            for q in question:
                for im in image:
                    question_image_pairs.append({"image": im, "question": q})
            inputs = question_image_pairs
        else:
            """
            Supports the following format
            - {"image": image, "question": question}
            - [{"image": image, "question": question}]
            - Generator and datasets
            """
            inputs = image
        results = super().__call__(inputs, **kwargs)
        return results

    def preprocess(self, inputs, padding=False, truncation=False, timeout=None):
        image = load_image(inputs["image"], timeout=timeout)
        model_inputs = self.tokenizer(
            inputs["question"],
            return_tensors="pt",
            padding=padding,
            truncation=truncation,
        )
        image_features = self.image_processor(images=image, return_tensors="pt")
        image_features = image_features.to(self.dtype)
        model_inputs.update(image_features)
        return model_inputs

    def _forward(self, model_inputs, **generate_kwargs):
        if self.model.can_generate():
            # User-defined `generation_config` passed to the pipeline call take precedence
            if "generation_config" not in generate_kwargs:
                generate_kwargs["generation_config"] = self.generation_config

            model_outputs = self.model.generate(**model_inputs, **generate_kwargs)
        else:
            model_outputs = self.model(**model_inputs)
        return model_outputs

    def postprocess(self, model_outputs, top_k=5):
        if self.model.can_generate():
            return [
                {"answer": self.tokenizer.decode(output_ids, skip_special_tokens=True).strip()}
                for output_ids in model_outputs
            ]
        else:
            if top_k > self.model.config.num_labels:
                top_k = self.model.config.num_labels

            probs = model_outputs.logits.sigmoid()[0]
            scores, ids = probs.topk(top_k)

            scores = scores.tolist()
            ids = ids.tolist()
            return [{"score": score, "answer": self.model.config.id2label[_id]} for score, _id in zip(scores, ids)]
