
def     a__			(    snake_case   ,	snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[str]			=						sorted(_SCREAMING_SNAKE_CASE   ,	key=lambda snake_case    : value[2]   ,	reverse=_SCREAMING_SNAKE_CASE    )

  # Create a list of size equal to the maximum deadline
  # and initialize it with -1
  __SCREAMING_SNAKE_CASE     :					int			=						max(_SCREAMING_SNAKE_CASE   ,	key=lambda snake_case    : value[1]    )[1]
  __SCREAMING_SNAKE_CASE     :					Dict			=						[-1] * max_deadline

  # Finding the maximum profit and the count of jobs
  __SCREAMING_SNAKE_CASE     :					int			=						0
  __SCREAMING_SNAKE_CASE     :					str			=						0
  for job in jobs:
    # Find a free time slot for this job
    # (Note that we start from the last possible slot)
    for i in range(job[1] - 1   ,	-1   ,	-1    ):
      if time_slots[i] == -1:
        __SCREAMING_SNAKE_CASE     :					List[str]			=						job[0]
        count += 1
        max_profit += job[2]
        break
  return [count, max_profit]


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


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

lowercase_											=	get_logger(__name__)


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


def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					str			=						task_template_dict.get('''task'''    )
  if task_name is None:
    logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(snake_case    )}'''    )
    return None
  __SCREAMING_SNAKE_CASE     :					Any			=						NAMEaTEMPLATE.get(snake_case    )
  return template.from_dict(snake_case    )
