def 			A__      (	snake_case_ :		list[int]       ,				snake_case_ :		list[int]      ):

 '''simple docstring'''





 SCREAMING_SNAKE_CASE__:   str=      len(snake_case__      )
 print('''The following activities are selected:'''      )

 # The first activity is always selected
 SCREAMING_SNAKE_CASE__:   List[str]=      0
 print(snake_case__       ,				end=''','''      )

 # Consider rest of the activities
 for j in range(snake_case__      ):
  # If this activity has start time greater than
  # or equal to the finish time of previously
  # selected activity, then select it
  if start[j] >= finish[i]:
   print(snake_case__       ,				end=''','''      )
   SCREAMING_SNAKE_CASE__:   Optional[int]=      j


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      lowercase_     :   Dict 				=				[1, 3, 0, 5, 8, 5]
      lowercase_     :   str 				=				[2, 4, 6, 7, 9, 9]
      print_max_activities(start, finish)


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_     :   Dict 				=				{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   str 				=				[
          'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'AutoformerForPrediction',
          'AutoformerModel',
          'AutoformerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_autoformer import (
          AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AutoformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_autoformer import (
                AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                AutoformerForPrediction,
                AutoformerModel,
                AutoformerPreTrainedModel,
            )

else:
      import sys

      lowercase_     :   Tuple 				=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

