


import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


a_       :Any				  =				datasets.utils.logging.get_logger(__name__)




@dataclass
class  snake_case__						(	datasets.BuilderConfig					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     None
     _SCREAMING_SNAKE_CASE            =     "utf-8"
     _SCREAMING_SNAKE_CASE            =     None
     _SCREAMING_SNAKE_CASE            =     10 << 20  # 10MB
     _SCREAMING_SNAKE_CASE            =     False
     _SCREAMING_SNAKE_CASE            =     "line"




class  snake_case__						(	datasets.ArrowBasedBuilder					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     TextConfig






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






     def        lowercase_       (	self  :  Optional[Any],			_snake_case  :  Dict )       ->str:
            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__							:     Union[str, Any]            =     dl_manager.download_and_extract(self.config.data_files )
            if isinstance(_A,			(str, list, tuple) ):
                   snake_case__							:     Union[str, Any]            =     data_files
                   if isinstance(_A,			_A ):
                          snake_case__							:     Tuple            =     [files]
                   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__							:     Tuple            =     []
            for split_name, files in data_files.items():
                   if isinstance(_A,			_A ):
                          snake_case__							:     Dict            =     [files]
                   snake_case__							:     Optional[Any]            =     [dl_manager.iter_files(_A ) for file in files]
                   splits.append(datasets.SplitGenerator(name=_A,			gen_kwargs={'files': files} ) )
            return splits






     def        lowercase_       (	self  :  Optional[Any],			_snake_case  :  Tuple )       ->int:
            if self.config.features is not None:
                   snake_case__							:     Dict            =     self.config.features.arrow_schema
                   if all(not require_storage_cast(_A ) for feature in self.config.features.values() ):
                          # cheaper cast
                          snake_case__							:     Optional[Any]            =     pa_table.cast(_A )
                   else:
                          # more expensive cast; allows str <-> int/float or str to Audio for example
                          snake_case__							:     Dict            =     table_cast(_A,			_A )
                   return pa_table
            else:
                   return pa_table.cast(pa.schema({'text': pa.string()} ) )






     def        lowercase_       (	self  :  Tuple,			_snake_case  :  Dict )       ->Union[str, Any]:
            snake_case__							:     Any            =     list(self.config.features ) if self.config.features is not None else ['text']
            for file_idx, file in enumerate(itertools.chain.from_iterable(_A ) ):
                   # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
                   with open(_A,			encoding=self.config.encoding,			errors=self.config.errors ) as f:
                          if self.config.sample_by == "line":
                                 snake_case__							:     Tuple            =     0
                                 while True:
                                        snake_case__							:     int            =     f.read(self.config.chunksize )
                                        if not batch:
                                               break
                                        batch += f.readline()  # finish current line
                                        # StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
                                        snake_case__							:     Any            =     StringIO(_A ).readlines()
                                        if not self.config.keep_linebreaks:
                                               snake_case__							:     List[str]            =     [line.rstrip('\n' ) for line in batch]
                                        snake_case__							:     List[Any]            =     pa.Table.from_arrays([pa.array(_A )],			names=_A )
                                        # 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 (file_idx, batch_idx), self._cast_table(_A )
                                        batch_idx += 1
                          elif self.config.sample_by == "paragraph":
                                 snake_case__							:     Tuple            =     0
                                 snake_case__							:     List[str]            =     ''
                                 while True:
                                        snake_case__							:     str            =     f.read(self.config.chunksize )
                                        if not new_batch:
                                               break
                                        batch += new_batch
                                        batch += f.readline()  # finish current line
                                        snake_case__							:     Dict            =     batch.split('\n\n' )
                                        snake_case__							:     Tuple            =     pa.Table.from_arrays(
                                            [pa.array([example for example in batch[:-1] if example] )],			names=_A )
                                        # 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 (file_idx, batch_idx), self._cast_table(_A )
                                        batch_idx += 1
                                        snake_case__							:     Union[str, Any]            =     batch[-1]
                                 if batch:
                                        snake_case__							:     List[Any]            =     pa.Table.from_arrays([pa.array([batch] )],			names=_A )
                                        yield (file_idx, batch_idx), self._cast_table(_A )
                          elif self.config.sample_by == "document":
                                 snake_case__							:     Optional[Any]            =     f.read()
                                 snake_case__							:     Dict            =     pa.Table.from_arrays([pa.array([text] )],			names=_A )
                                 yield file_idx, self._cast_table(_A )











def 	lowercase_							(A			:			Dict				):
       snake_case__							:     Optional[int]            =     len(A				)
       for i in range(length - 1				):
              snake_case__							:     List[str]            =     False
              for j in range(length - 1 - i				):
                     if collection[j] > collection[j + 1]:
                            snake_case__							:     Optional[Any]            =     True
                            snake_case__     ,    snake_case__							:     Dict            =     collection[j + 1], collection[j]
              if not swapped:
                     break  # Stop iteration if the collection is sorted.
       return collection


if __name__ == "__main__":
 import doctest
 import time

 doctest.testmod()

 a_       :Any				  =				input("Enter numbers separated by a comma:").strip()
 a_       :Optional[Any]				  =				[int(item) for item in user_input.split(",")]
 a_       :Optional[int]				  =				time.process_time()
 print(*bubble_sort(unsorted), sep=",")
 print(F"""Processing time: {(time.process_time() - start)%1e9 + 7}""")






