




import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast







@dataclass
class   A(						datasets.BuilderConfig   ):



 '''simple docstring'''


 UpperCamelCase						   =						None







class   A(						datasets.ArrowBasedBuilder   ):



 '''simple docstring'''


 UpperCamelCase						   =						PandasConfig

 def 				a__ (				self					:  Any  )   ->	Optional[int]:

       """simple docstring"""






       return datasets.DatasetInfo(features=self.config.features  )

 def 				a__ (				self					:  int						,							A_					:  Any  )   ->	List[Any]:

       """simple docstring"""






       if not self.config.data_files:
             raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""  )
       lowerCamelCase_		     =    dl_manager.download_and_extract(self.config.data_files  )
       if isinstance(A_						,							(str, list, tuple)  ):
             lowerCamelCase_		     =    data_files
             if isinstance(A_						,							A_  ):
                   lowerCamelCase_		     =    [files]
             # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
             lowerCamelCase_		     =    [dl_manager.iter_files(A_  ) for file in files]
             return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,							gen_kwargs={'files': files}  )]
       lowerCamelCase_		     =    []
       for split_name, files in data_files.items():
             if isinstance(A_						,							A_  ):
                   lowerCamelCase_		     =    [files]
             # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
             lowerCamelCase_		     =    [dl_manager.iter_files(A_  ) for file in files]
             splits.append(datasets.SplitGenerator(name=A_						,							gen_kwargs={'files': files}  )  )
       return splits

 def 				a__ (				self					:  Any						,							A_					:  pa.Table  )   ->	pa.Table:

       """simple docstring"""






       if self.config.features is not None:
             # more expensive cast to support nested features with keys in a different order
             # allows str <-> int/float or str to Audio for example
             lowerCamelCase_		     =    table_cast(A_						,							self.config.features.arrow_schema  )
       return pa_table





 def 				a__ (				self					:  Union[str, Any]						,							A_					:  List[Any]  )   ->	str:

       """simple docstring"""






       for i, file in enumerate(itertools.chain.from_iterable(A_  )  ):
             with open(A_						,							'rb'  ) as f:
                   lowerCamelCase_		     =    pa.Table.from_pandas(pd.read_pickle(A_  )  )
                   yield i, self._cast_table(A_  )









def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       list     ):


      '''simple docstring'''



      for i in range(len(lowercase     ) - 1	,							0	,							-1     ):
            lowerCamelCase_		     =    False

            for j in range(lowercase	,							0	,							-1     ):
                  if unsorted[j] < unsorted[j - 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j - 1], unsorted[j]
                        lowerCamelCase_		     =    True

            for j in range(lowercase     ):
                  if unsorted[j] > unsorted[j + 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j + 1], unsorted[j]
                        lowerCamelCase_		     =    True

            if not swapped:
                  break
      return unsorted


if __name__ == "__main__":
 import doctest

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



