


'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					List[str]:
     """simple docstring"""




     __a       =   len(__SCREAMING_SNAKE_CASE		)
     while cur > 1:
          # Find the maximum number in arr
          __a       =   arr.index(max(arr[0:cur]		)		)
          # Reverse from 0 to mi
          __a       =   arr[mi::-1] + arr[mi + 1 : len(__SCREAMING_SNAKE_CASE		)]
          # Reverse whole list
          __a       =   arr[cur - 1 :: -1] + arr[cur : len(__SCREAMING_SNAKE_CASE		)]
          cur -= 1
     return arr


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










'''simple docstring'''







from typing import TYPE_CHECKING

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


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

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

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			[
     '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

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






