"""simple docstring"""




def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       list	)       ->      list:
  for i in range(len(_lowerCamelCase	) - 1			,0			,-1	):
    _lowerCAmelCase						:       Dict  			=     False

    for j in range(_lowerCamelCase			,0			,-1	):
      if unsorted[j] < unsorted[j - 1]:
        _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     unsorted[j - 1], unsorted[j]
        _lowerCAmelCase						:       int  			=     True

    for j in range(_lowerCamelCase	):
      if unsorted[j] > unsorted[j + 1]:
        _lowerCAmelCase			,	_lowerCAmelCase						:       Any  			=     unsorted[j + 1], unsorted[j]
        _lowerCAmelCase						:       int  			=     True

    if not swapped:
      break
  return unsorted


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     _a      :		Tuple           =   input('Enter numbers separated by a comma:\n').strip()
     _a      :		Optional[Any]           =   [int(item) for item in user_input.split(',')]
     print(F"""{cocktail_shaker_sort(unsorted) = }""")








'''simple docstring'''

from typing import TYPE_CHECKING

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


lowerCamelCase							:   str  						=	{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:   Dict  						=	[
     'RagModel',
     'RagPreTrainedModel',
     'RagSequenceForGeneration',
     'RagTokenForGeneration',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:   Optional[int]  						=	[
     'TFRagModel',
     'TFRagPreTrainedModel',
     'TFRagSequenceForGeneration',
     'TFRagTokenForGeneration',
 ]


if TYPE_CHECKING:
 from .configuration_rag import RagConfig
 from .retrieval_rag import RagRetriever
 from .tokenization_rag import RagTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rag import (
      TFRagModel,
      TFRagPreTrainedModel,
      TFRagSequenceForGeneration,
      TFRagTokenForGeneration,
  )

else:
 import sys

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




