

from __future__ import annotations

# Define the Gravitational Constant G and the function
A__										=      6.6743E-11  # unit of G : m^3 * kg^-1 * s^-2





def 				__a							(       __A			,							__A			,							__A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    mass_a * mass_a

    if (force, mass_a, mass_a, distance).count(0						) != 1:
        raise ValueError("One and only one argument must be 0"						)
    if force < 0:
        raise ValueError("Gravitational force can not be negative"						)
    if distance < 0:
        raise ValueError("Distance can not be negative"						)
    if mass_a < 0 or mass_a < 0:
        raise ValueError("Mass can not be negative"						)
    if force == 0:
        SCREAMING_SNAKE_CASE__         =    GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
        return {"force": force}
    elif mass_a == 0:
        SCREAMING_SNAKE_CASE__         =    (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
        return {"mass_1": mass_a}
    elif mass_a == 0:
        SCREAMING_SNAKE_CASE__         =    (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
        return {"mass_2": mass_a}
    elif distance == 0:
        SCREAMING_SNAKE_CASE__         =    (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
        return {"distance": distance}
    raise ValueError("One and only one argument must be 0"						)


# Run doctest
if __name__ == "__main__":
  import doctest

  doctest.testmod()





from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


A__										=      [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

A__										=      get_logger(__name__)


A__										=      {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    task_template_dict.get("task"						)
    if task_name is None:
        logger.warning(f"Couldn't find template for task '{task_name}'. Available templates: {list(__A						)}"						)
        return None
    SCREAMING_SNAKE_CASE__         =    NAMEaTEMPLATE.get(__A						)
    return template.from_dict(__A						)


