




def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_		):
    a__      =       sorted(UpperCAmelCase_   ,    key=lambda UpperCAmelCase_		: value[2]   ,    reverse=UpperCAmelCase_		)

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

    # Finding the maximum profit and the count of jobs
    a__      =       0
    a__      =       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:
                a__      =       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


SCREAMING_SNAKE_CASE_						:					str			   =       {
    '''configuration_wav2vec2_conformer''': [
        '''WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Wav2Vec2ConformerConfig''',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       [
     '''WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Wav2Vec2ConformerForAudioFrameClassification''',
     '''Wav2Vec2ConformerForCTC''',
     '''Wav2Vec2ConformerForPreTraining''',
     '''Wav2Vec2ConformerForSequenceClassification''',
     '''Wav2Vec2ConformerForXVector''',
     '''Wav2Vec2ConformerModel''',
     '''Wav2Vec2ConformerPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_wavaveca_conformer import (
     WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     WavaVecaConformerConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_wavaveca_conformer import (
      WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WavaVecaConformerForAudioFrameClassification,
      WavaVecaConformerForCTC,
      WavaVecaConformerForPreTraining,
      WavaVecaConformerForSequenceClassification,
      WavaVecaConformerForXVector,
      WavaVecaConformerModel,
      WavaVecaConformerPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






