from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_tf_available():
    import tensorflow as tf

    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
    from ..tf_utils import stable_softmax

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES

logger = logging.get_logger(__name__)


@add_end_docstrings(PIPELINE_INIT_ARGS)
class ImageClassificationPipeline(Pipeline):
    """
    Image classification pipeline using any `AutoModelForImageClassification`. This pipeline predicts the class of an
    image.

    Example:

    ```python
    >>> from transformers import pipeline

    >>> classifier = pipeline(model="microsoft/beit-base-patch16-224-pt22k-ft22k")
    >>> classifier("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")
    [{'score': 0.442, 'label': 'macaw'}, {'score': 0.088, 'label': 'popinjay'}, {'score': 0.075, 'label': 'parrot'}, {'score': 0.073, 'label': 'parodist, lampooner'}, {'score': 0.046, 'label': 'poll, poll_parrot'}]
    ```

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

    This image classification pipeline can currently be loaded from [`pipeline`] using the following task identifier:
    `"image-classification"`.

    See the list of available models on
    [huggingface.co/models](https://huggingface.co/models?filter=image-classification).
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        requires_backends(self, "vision")
        self.check_model_type(
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
            if self.framework == "tf"
            else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
        )

    def _sanitize_parameters(self, top_k=None):
        postprocess_params = {}
        if top_k is not None:
            postprocess_params["top_k"] = top_k
        return {}, {}, postprocess_params

    def __call__(self, images: Union[str, List[str], "Image.Image", List["Image.Image"]], **kwargs):
        """
        Assign labels to the image(s) passed as inputs.

        Args:
            images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
                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, which must then be passed as a string.
                Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL
                images.
            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.

        Return:
            A dictionary or a list of dictionaries containing result. If the input is a single image, will return a
            dictionary, if the input is a list of several images, will return a list of dictionaries corresponding to
            the images.

            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.
        """
        return super().__call__(images, **kwargs)

    def preprocess(self, image):
        image = load_image(image)
        model_inputs = self.image_processor(images=image, return_tensors=self.framework)
        return model_inputs

    def _forward(self, model_inputs):
        model_outputs = self.model(**model_inputs)
        return model_outputs

    def postprocess(self, model_outputs, top_k=5):
        if top_k > self.model.config.num_labels:
            top_k = self.model.config.num_labels

        if self.framework == "pt":
            probs = model_outputs.logits.softmax(-1)[0]
            scores, ids = probs.topk(top_k)
        elif self.framework == "tf":
            probs = stable_softmax(model_outputs.logits, axis=-1)[0]
            topk = tf.math.top_k(probs, k=top_k)
            scores, ids = topk.values.numpy(), topk.indices.numpy()
        else:
            raise ValueError(f"Unsupported framework: {self.framework}")

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