






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			:					Optional[Any]         =	datasets.utils.logging.get_logger(__name__)



@dataclass
class 							_UpperCamelCase			(datasets.BuilderConfig			):
      snake_case_  =							10000
      snake_case_  =							None
      snake_case_  =							None



class 							_UpperCamelCase			(datasets.ArrowBasedBuilder			):
      snake_case_  =							ParquetConfig


      def  __UpperCAmelCase		(	self	)->	List[Any]:
           return datasets.DatasetInfo(features=self.config.features	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	int:
           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(__UpperCamelCase					,						(str, list, tuple)	):
                __lowerCAmelCase								=					data_files
                if isinstance(__UpperCamelCase					,						__UpperCamelCase	):
                     __lowerCAmelCase								=					[files]
                # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                __lowerCAmelCase								=					[dl_manager.iter_files(__UpperCamelCase	) 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(__UpperCamelCase					,						__UpperCamelCase	):
                     __lowerCAmelCase								=					[files]
                # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                __lowerCAmelCase								=					[dl_manager.iter_files(__UpperCamelCase	) 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(__UpperCamelCase	):
                          with open(__UpperCamelCase					,						"rb"	) as f:
                               __lowerCAmelCase								=					datasets.Features.from_arrow_schema(pq.read_schema(__UpperCamelCase	)	)
                          break
                splits.append(datasets.SplitGenerator(name=__UpperCamelCase					,						gen_kwargs={"files": files}	)	)
           return splits


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	pa.Table:
           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
                __lowerCAmelCase								=					table_cast(__UpperCamelCase					,						self.info.features.arrow_schema	)
           return pa_table


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase	)->	int:
           __lowerCAmelCase								=					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(__UpperCamelCase	)	):
                with open(__UpperCamelCase					,						"rb"	) as f:
                     __lowerCAmelCase								=					pq.ParquetFile(__UpperCamelCase	)
                     try:
                          for batch_idx, record_batch in enumerate(
                              parquet_file.iter_batches(batch_size=self.config.batch_size					,						columns=self.config.columns	)	):
                               __lowerCAmelCase								=					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(__UpperCamelCase	)
                     except ValueError as e:
                          logger.error(F"""Failed to read file '{file}' with error {type(__UpperCamelCase	)}: {e}"""	)
                          raise














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


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






