def  snake_case						(__lowercase						)	->	Union[str, Any]:

      '''simple docstring'''



      _snake_case						:					Tuple    =   len(snake_case_						)
      while cur > 1:
            # Find the maximum number in arr
            _snake_case						:					Any    =   arr.index(max(arr[0:cur]						)						)
            # Reverse from 0 to mi
            _snake_case						:					Tuple    =   arr[mi::-1] + arr[mi + 1 : len(snake_case_						)]
            # Reverse whole list
            _snake_case						:					Optional[int]    =   arr[cur - 1 :: -1] + arr[cur : len(snake_case_						)]
            cur -= 1
      return arr


if __name__ == "__main__":
    __SCREAMING_SNAKE_CASE						:   Any         =			input('Enter numbers separated by a comma:\n').strip()
    __SCREAMING_SNAKE_CASE						:   int         =			[int(item) for item in user_input.split(',')]
    print(pancake_sort(unsorted))
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			[
				    '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

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)