#  Copyright (c) ZenML GmbH 2022. All Rights Reserved.
#
#  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:
#
#       https://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,
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#  or implied. See the License for the specific language governing
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"""Implementation of label config generators for Label Studio."""

from typing import List, Tuple

from zenml.enums import AnnotationTasks
from zenml.logger import get_logger

logger = get_logger(__name__)


TASK_TO_FILENAME_REFERENCE_MAPPING = {
    AnnotationTasks.IMAGE_CLASSIFICATION.value: "image",
    AnnotationTasks.OBJECT_DETECTION_BOUNDING_BOXES.value: "image",
    AnnotationTasks.OCR.value: "image",
    AnnotationTasks.TEXT_CLASSIFICATION.value: "image",
}


def _generate_label_config() -> str:
    # TODO [HIGH] Implement label config generator
    # refactoring out duplicated code from the various functions below
    raise NotImplementedError()


def generate_text_classification_label_config(
    labels: List[str],
) -> Tuple[str, str]:
    """Generates a Label Studio label config for text classification.

    This is based on the basic config example shown at
    https://labelstud.io/templates/sentiment_analysis.html.

    Args:
        labels: A list of labels to be used in the label config.

    Returns:
        A tuple of the generated label config and the label config type.

    Raises:
        ValueError: If no labels are provided.
    """
    if not labels:
        raise ValueError("No labels provided")

    label_config_type = AnnotationTasks.TEXT_CLASSIFICATION

    label_config_start = """<View>
    <Header value="Choose text class:"/>
    <Text name="text" value="$text"/>
    <Choices name="class" toName="text" choice="single" showInline="true">
    """
    label_config_choices = "".join(
        f"<Choice value='{label}' />\n" for label in labels
    )
    label_config_end = "</Choices>\n</View>"

    label_config = label_config_start + label_config_choices + label_config_end
    return (
        label_config,
        label_config_type,
    )


def generate_image_classification_label_config(
    labels: List[str],
) -> Tuple[str, str]:
    """Generates a Label Studio label config for image classification.

    This is based on the basic config example shown at
    https://labelstud.io/templates/image_classification.html.

    Args:
        labels: A list of labels to be used in the label config.

    Returns:
        A tuple of the generated label config and the label config type.

    Raises:
        ValueError: If no labels are provided.
    """
    if not labels:
        raise ValueError("No labels provided")

    label_config_type = AnnotationTasks.IMAGE_CLASSIFICATION

    label_config_start = """<View>
    <Image name="image" value="$image"/>
    <Choices name="choice" toName="image">
    """
    label_config_choices = "".join(
        f"<Choice value='{label}' />\n" for label in labels
    )
    label_config_end = "</Choices>\n</View>"

    label_config = label_config_start + label_config_choices + label_config_end
    return (
        label_config,
        label_config_type,
    )


def generate_basic_object_detection_bounding_boxes_label_config(
    labels: List[str],
) -> Tuple[str, str]:
    """Generates a Label Studio config for object detection with bounding boxes.

    This is based on the basic config example shown at
    https://labelstud.io/templates/image_bbox.html.

    Args:
        labels: A list of labels to be used in the label config.

    Returns:
        A tuple of the generated label config and the label config type.

    Raises:
        ValueError: If no labels are provided.
    """
    if not labels:
        raise ValueError("No labels provided")

    label_config_type = AnnotationTasks.OBJECT_DETECTION_BOUNDING_BOXES

    label_config_start = """<View>
    <Image name="image" value="$image"/>
    <RectangleLabels name="label" toName="image">
    """
    label_config_choices = "".join(
        f"<Label value='{label}' />\n" for label in labels
    )
    label_config_end = "</RectangleLabels>\n</View>"
    label_config = label_config_start + label_config_choices + label_config_end

    return (
        label_config,
        label_config_type,
    )


def METHOD_NAME(
    labels: List[str],
) -> Tuple[str, str]:
    """Generates a Label Studio config for optical character recognition (OCR) labeling task.

    This is based on the basic config example shown at
    https://labelstud.io/templates/optical_character_recognition.html

    Args:
        labels: A list of labels to be used in the label config.

    Returns:
        A tuple of the generated label config and the label config type.

    Raises:
        ValueError: If no labels are provided.
    """
    if not labels:
        raise ValueError("No labels provided")

    label_config_type = AnnotationTasks.OCR

    label_config_start = """
    <View>
    <Image name="image" value="$ocr" zoom="true" zoomControl="true" rotateControl="true"/>
    <View>
    <Filter toName="label" minlength="0" name="filter"/>
    <Labels name="label" toName="image">
    """
    label_config_choices = "".join(
        f"<Label value='{label}' />\n" for label in labels
    )

    label_config_end = """
    </Labels>
    </View>
    <Rectangle name="bbox" toName="image" strokeWidth="3"/>
    <Polygon name="poly" toName="image" strokeWidth="3"/>
    <TextArea name="transcription" toName="image" editable="true" perRegion="true" required="true" maxSubmissions="1" rows="5" placeholder="Recognized Text" displayMode="region-list"/>
    </View>
    """
    label_config = label_config_start + label_config_choices + label_config_end

    return (
        label_config,
        label_config_type,
    )