





from __future__ import annotations




def 	lowerCAmelCase_					(						__UpperCAmelCase:    list[int]   )   ->			list[int]:
    if len(__UpperCAmelCase   ) == 0:
        return array

    UpperCamelCase__     ,UpperCamelCase__							:       int      =   min(__UpperCAmelCase   ), max(__UpperCAmelCase   )

    # Compute the variables
    UpperCamelCase__							:       Optional[int]      =   _max - _min + 1
    UpperCamelCase__     ,UpperCamelCase__							:       Any      =   [0] * holes_range, [0] * holes_range

    # Make the sorting.
    for i in array:
        UpperCamelCase__							:       str      =   i - _min
        UpperCamelCase__							:       int      =   i
        holes_repeat[index] += 1

    # Makes the array back by replacing the numbers.
    UpperCamelCase__							:       Optional[int]      =   0
    for i in range(__UpperCAmelCase   ):
        while holes_repeat[i] > 0:
            UpperCamelCase__							:       str      =   holes[i]
            index += 1
            holes_repeat[i] -= 1

    # Returns the sorted array.
    return array


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  UpperCAmelCase_  							=				input('Enter numbers separated by comma:\n')
  UpperCAmelCase_  							=				[int(x) for x in user_input.split(',')]
  print(pigeon_sort(unsorted))








from typing import TYPE_CHECKING

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


UpperCAmelCase_  							=				{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_  							=				[
      'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'InformerForPrediction',
      'InformerModel',
      'InformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_informer import (
        INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        InformerForPrediction,
        InformerModel,
        InformerPreTrainedModel,
    )

else:
  import sys

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

