






import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


__lowerCamelCase	:		Tuple										=   datasets.utils.logging.get_logger(__name__)







@dataclass
class  _lowercase   (   datasets.BuilderConfig						):
      _a							:						int										=							1_0000
      _a							:						Optional[List[str]]										=							None
      _a							:						Optional[datasets.Features]										=							None







class  _lowercase   (   datasets.ArrowBasedBuilder						):
      _a							:						Tuple										=							ParquetConfig
      def 						lowercase__					(     self   ):
       return datasets.DatasetInfo(features=self.config.features   )
      def 						lowercase__					(     self				,			a   ):
       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}"   )
       snake_case__  :    Dict							       =dl_manager.download_and_extract(self.config.data_files   )
       if isinstance(a				,			(str, list, tuple)   ):
        snake_case__  :    List[str]							       =data_files
        if isinstance(a				,			a   ):
         snake_case__  :    int							       =[files]
        # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
        snake_case__  :    List[str]							       =[dl_manager.iter_files(a   ) for file in files]
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,			gen_kwargs={"""files""": files}   )]
       snake_case__  :    Dict							       =[]
       for split_name, files in data_files.items():
        if isinstance(a				,			a   ):
         snake_case__  :    Tuple							       =[files]
        # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
        snake_case__  :    Optional[Any]							       =[dl_manager.iter_files(a   ) for file in files]
        # Infer features is they are stoed in the arrow schema
        if self.info.features is None:
         for file in itertools.chain.from_iterable(a   ):
          with open(a				,			"""rb"""   ) as f:
           snake_case__  :    List[Any]							       =datasets.Features.from_arrow_schema(pq.read_schema(a   )   )
          break
        splits.append(datasets.SplitGenerator(name=a				,			gen_kwargs={"""files""": files}   )   )
       return splits
      def 						lowercase__					(     self				,			a   ):
       if self.info.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
        snake_case__  :    List[Any]							       =table_cast(a				,			self.info.features.arrow_schema   )
       return pa_table



      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Optional[int]							       =self.info.features.arrow_schema if self.info.features is not None else None
       if self.info.features is not None and self.config.columns is not None:
        if sorted(field.name for field in schema   ) != sorted(self.config.columns   ):
         raise ValueError(
             F"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"   )
       for file_idx, file in enumerate(itertools.chain.from_iterable(a   )   ):
        with open(a				,			"""rb"""   ) as f:
         snake_case__  :    str							       =pq.ParquetFile(a   )
         try:
          for batch_idx, record_batch in enumerate(
              parquet_file.iter_batches(batch_size=self.config.batch_size				,			columns=self.config.columns   )   ):
           snake_case__  :    Optional[Any]							       =pa.Table.from_batches([record_batch]   )
           # Uncomment for debugging (will print the Arrow table size and elements)
           # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
           # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
           yield F"{file_idx}_{batch_idx}", self._cast_table(a   )
         except ValueError as e:
          logger.error(F"Failed to read file '{file}' with error {type(a   )}: {e}"   )
          raise













import math







def 			A__							(      _a  :					list		,  _a  :					int = 0		,  _a  :					int = 0	):





 '''simple docstring'''







 snake_case__  :    Tuple							       =end or len(_a	)
 for i in range(_a		,  _a	):
  snake_case__  :    int							       =i
  snake_case__  :    int							       =array[i]
  while temp_index != start and temp_index_value < array[temp_index - 1]:
   snake_case__  :    Dict							       =array[temp_index - 1]
   temp_index -= 1
  snake_case__  :    Dict							       =temp_index_value
 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int	):  # Max Heap





 '''simple docstring'''







 snake_case__  :    Tuple							       =index
 snake_case__  :    Dict							       =2 * index + 1  # Left Node
 snake_case__  :    Union[str, Any]							       =2 * index + 2  # Right Node

 if left_index < heap_size and array[largest] < array[left_index]:
  snake_case__  :    Dict							       =left_index

 if right_index < heap_size and array[largest] < array[right_index]:
  snake_case__  :    List[Any]							       =right_index

 if largest != index:
  snake_case__       ,   snake_case__  :    List[Any]							       =array[largest], array[index]
  heapify(_a		,  _a		,  _a	)







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =len(_a	)

 for i in range(n // 2		,  -1		,  -1	):
  heapify(_a		,  _a		,  _a	)

 for i in range(n - 1		,  0		,  -1	):
  snake_case__       ,   snake_case__  :    Tuple							       =array[0], array[i]
  heapify(_a		,  0		,  _a	)

 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 if (array[first_index] > array[middle_index]) != (
     array[first_index] > array[last_index]
 ):
  return array[first_index]
 elif (array[middle_index] > array[first_index]) != (
     array[middle_index] > array[last_index]
 ):
  return array[middle_index]
 else:
  return array[last_index]







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 snake_case__  :    Dict							       =low
 snake_case__  :    Union[str, Any]							       =high
 while True:
  while array[i] < pivot:
   i += 1
  j -= 1
  while pivot < array[j]:
   j -= 1
  if i >= j:
   return i
  snake_case__       ,   snake_case__  :    List[Any]							       =array[j], array[i]
  i += 1







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 if len(_a	) == 0:
  return array
 snake_case__  :    Optional[int]							       =2 * math.ceil(math.loga(len(_a	)	)	)
 snake_case__  :    Tuple							       =16
 return intro_sort(_a		,  0		,  len(_a	)		,  _a		,  _a	)







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 while end - start > size_threshold:
  if max_depth == 0:
   return heap_sort(_a	)
  max_depth -= 1
  snake_case__  :    Dict							       =median_of_a(_a		,  _a		,  start + ((end - start) // 2) + 1		,  end - 1	)
  snake_case__  :    List[str]							       =partition(_a		,  _a		,  _a		,  _a	)
  intro_sort(_a		,  _a		,  _a		,  _a		,  _a	)
  snake_case__  :    Union[str, Any]							       =p
 return insertion_sort(_a		,  _a		,  _a	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __lowerCamelCase	:		Optional[int]										=   input("""Enter numbers separated by a comma : """).strip()
       __lowerCamelCase	:		Dict										=   [float(item) for item in user_input.split(""",""")]
       print(sort(unsorted))





