


"""simple docstring"""





def   _snake_case    (    _snake_case					:      Union[str, Any]			, _snake_case					:      Optional[Any]      ):
      lowerCAmelCase       :						Any							= sorted(_snake_case			, key=lambda _snake_case      : value[2]			, reverse=_snake_case      )

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

      # Finding the maximum profit and the count of jobs
      lowerCAmelCase       :						List[Any]							= 0
      lowerCAmelCase       :						List[Any]							= 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:
                        lowerCAmelCase       :						Tuple							= job[0]
                        count += 1
                        max_profit += job[2]
                        break
      return [count, max_profit]


if __name__ == "__main__":
 import doctest

 doctest.testmod()








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__						=		{
    'configuration_prophetnet': ['PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ProphetNetConfig'],
    'tokenization_prophetnet': ['ProphetNetTokenizer'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ProphetNetDecoder',
							    'ProphetNetEncoder',
							    'ProphetNetForCausalLM',
							    'ProphetNetForConditionalGeneration',
							    'ProphetNetModel',
							    'ProphetNetPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
							from .tokenization_prophetnet import ProphetNetTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_prophetnet import (
														    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ProphetNetDecoder,
														    ProphetNetEncoder,
														    ProphetNetForCausalLM,
														    ProphetNetForConditionalGeneration,
														    ProphetNetModel,
														    ProphetNetPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase__						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
