





from math import sqrt




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		list[int]:
       UpperCamelCase_ 							=	[1]
       for i in range(2       ,		int(sqrt(UpperCamelCase_	)	) + 1       ,		1	):
              if number % i == 0:
                     values.append(UpperCamelCase_	)
                     if int(number // i	) != i:
                            values.append(int(number // i	)	)
       return sorted(UpperCamelCase_	)




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		bool:
       return sum(factors(UpperCamelCase_	)	) > n




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		bool:
       UpperCamelCase_ 							=	factors(UpperCamelCase_	)
       UpperCamelCase_ 							=	len(UpperCamelCase_	)
       UpperCamelCase_ 							=	[[0 for i in range(number + 1	)] for j in range(r + 1	)]
       for i in range(r + 1	):
              UpperCamelCase_ 							=	True

       for i in range(1       ,		number + 1	):
              UpperCamelCase_ 							=	False

       for i in range(1       ,		r + 1	):
              for j in range(1       ,		number + 1	):
                     if j < values[i - 1]:
                            UpperCamelCase_ 							=	subset[i - 1][j]
                     else:
                            UpperCamelCase_ 							=	subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

       return subset[r][number] != 0




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		bool:
       return abundant(UpperCamelCase_	) and not semi_perfect(UpperCamelCase_	)


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
     for number in (6_9, 7_0, 7_1):
          print(f'''{number} is {'' if weird(number) else 'not '}weird.''')




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						)


