


'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowercase:		Tuple    						=  {
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		List[str]    						=  ["ImageGPTFeatureExtractor"]
       __lowercase:		Tuple    						=  ["ImageGPTImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		str    						=  [
           "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ImageGPTForCausalImageModeling",
           "ImageGPTForImageClassification",
           "ImageGPTModel",
           "ImageGPTPreTrainedModel",
           "load_tf_weights_in_imagegpt",
       ]


if TYPE_CHECKING:
       from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
              from .image_processing_imagegpt import ImageGPTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_imagegpt import (
                  IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ImageGPTForCausalImageModeling,
                  ImageGPTForImageClassification,
                  ImageGPTModel,
                  ImageGPTPreTrainedModel,
                  load_tf_weights_in_imagegpt,
              )

else:
       import sys

       __lowercase:		List[str]    						=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
"""simple docstring"""


def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				sorted(A     ,					key=lambda A   : value[2]     ,					reverse=A   )

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

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()




