





'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def 				lowercase__   (   __lowercase						:  Features     ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  np.inf

       def set_batch_size(__lowercase						:  FeatureType     ) -> None:
              nonlocal batch_size
              if isinstance(__lowercase				,  __lowercase     ):
                     __UpperCamelCase       =  min(__lowercase				,  config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS     )
              elif isinstance(__lowercase				,  __lowercase     ):
                     __UpperCamelCase       =  min(__lowercase				,  config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS     )
              elif isinstance(__lowercase				,  __lowercase     ) and feature.dtype == "binary":
                     __UpperCamelCase       =  min(__lowercase				,  config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS     )

       _visit(__lowercase				,  __lowercase     )

       return None if batch_size is np.inf else batch_size

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							List[str]							,	__A       :							NestedDataStructureLike[PathLike]							,	__A       :							Optional[NamedSplit] = None							,	__A       :							Optional[Features] = None							,	__A       :							str = None							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							Optional[int] = None							,	**__A       :							Dict							,	):
        super().__init__(
            __A							,	split=__A							,	features=__A							,	cache_dir=__A							,	keep_in_memory=__A							,	streaming=__A							,	num_proc=__A							,	**__A							,	)
        __UpperCamelCase       =  path_or_paths if isinstance(__A							,	__A	) else {self.split: path_or_paths}
        __UpperCamelCase       =  _PACKAGED_DATASETS_MODULES['parquet'][1]
        __UpperCamelCase       =  Parquet(
            cache_dir=__A							,	data_files=__A							,	features=__A							,	hash=__A							,	**__A							,	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        # Build iterable dataset
        if self.streaming:
               __UpperCamelCase       =  self.builder.as_streaming_dataset(split=self.split	)
        # Build regular (map-style) dataset
        else:
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None

               self.builder.download_and_prepare(
                   download_config=__A							,	download_mode=__A							,	verification_mode=__A							,	base_path=__A							,	num_proc=self.num_proc							,	)
               __UpperCamelCase       =  self.builder.as_dataset(
                   split=self.split							,	verification_mode=__A							,	in_memory=self.keep_in_memory	)
        return dataset






class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							List[str]							,	__A       :							Dataset							,	__A       :							Union[PathLike, BinaryIO]							,	__A       :							Optional[int] = None							,	**__A       :							Dict							,	):
        __UpperCamelCase       =  dataset
        __UpperCamelCase       =  path_or_buf
        __UpperCamelCase       =  batch_size or get_writer_batch_size(dataset.features	)
        __UpperCamelCase       =  parquet_writer_kwargs






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

        if isinstance(self.path_or_buf							,	(str, bytes, os.PathLike)	):
               with open(self.path_or_buf							,	'wb+'	) as buffer:
                      __UpperCamelCase       =  self._write(file_obj=__A							,	batch_size=__A							,	**self.parquet_writer_kwargs	)
        else:
               __UpperCamelCase       =  self._write(file_obj=self.path_or_buf							,	batch_size=__A							,	**self.parquet_writer_kwargs	)
        return written






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							BinaryIO							,	__A       :							int							,	**__A       :							List[str]	):
        __UpperCamelCase       =  0
        __UpperCamelCase       =  parquet_writer_kwargs.pop('path_or_buf'							,	__A	)
        __UpperCamelCase       =  self.dataset.features.arrow_schema

        __UpperCamelCase       =  pq.ParquetWriter(__A							,	schema=__A							,	**__A	)

        for offset in logging.tqdm(
            range(0							,	len(self.dataset	)							,	__A	)							,	unit='ba'							,	disable=not logging.is_progress_bar_enabled()							,	desc='Creating parquet from Arrow format'							,	):
               __UpperCamelCase       =  query_table(
                   table=self.dataset._data							,	key=slice(__A							,	offset + batch_size	)							,	indices=self.dataset._indices if self.dataset._indices is not None else None							,	)
               writer.write_table(__A	)
               written += batch.nbytes
        writer.close()
        return written

'''simple docstring'''





from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE_:     Optional[int]  =Lock()





def 	lowerCAmelCase_				(					snake_case_       : Dict					,    snake_case_       : Dict					,    snake_case_       : Optional[int]					,    snake_case_       : Tuple					,    snake_case_       : Any					,    snake_case_       : Optional[Any]					,    snake_case_       : Union[str, Any]			)    ->      Optional[Any]:


     '''simple docstring'''




     global process_lock

     # we perform n swaps since after n swaps we know we are sorted
     # we *could* stop early if we are sorted already, but it takes as long to
     # find out we are sorted as it does to sort the list with this algorithm
     for i in range(0					,    10			):
          if (i + position) % 2 == 0 and r_send is not None:
               # send your value to your right neighbor
               process_lock.acquire()
               r_send[1].send(snake_case_			)
               process_lock.release()

               # receive your right neighbor's value
               process_lock.acquire()
               UpperCAmelCase_           =   rr_cv[0].recv()
               process_lock.release()

               # take the lower value since you are on the left
               UpperCAmelCase_           =   min(snake_case_					,    snake_case_			)
          elif (i + position) % 2 != 0 and l_send is not None:
               # send your value to your left neighbor
               process_lock.acquire()
               l_send[1].send(snake_case_			)
               process_lock.release()

               # receive your left neighbor's value
               process_lock.acquire()
               UpperCAmelCase_           =   lr_cv[0].recv()
               process_lock.release()

               # take the higher value since you are on the right
               UpperCAmelCase_           =   max(snake_case_					,    snake_case_			)
    # after all swaps are performed, send the values back to main
     result_pipe[1].send(snake_case_			)





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]			)    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   []
     UpperCAmelCase_           =   []
     # initialize the list of pipes where the values will be retrieved
     for _ in arr:
          result_pipe.append(Pipe()			)
     # creates the processes
     # the first and last process only have one neighbor so they are made outside
     # of the loop
     UpperCAmelCase_           =   Pipe()
     UpperCAmelCase_           =   Pipe()
     process_array_.append(
         Process(
             target=snake_case_					,    args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])					,    )			)
     UpperCAmelCase_           =   temp_rs
     UpperCAmelCase_           =   temp_rr

     for i in range(1					,    len(snake_case_			) - 1			):
          UpperCAmelCase_           =   Pipe()
          UpperCAmelCase_           =   Pipe()
          process_array_.append(
              Process(
                  target=snake_case_					,    args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])					,    )			)
          UpperCAmelCase_           =   temp_rs
          UpperCAmelCase_           =   temp_rr

     process_array_.append(
         Process(
             target=snake_case_					,    args=(
                 len(snake_case_			) - 1,
                 arr[len(snake_case_			) - 1],
                 temp_ls,
                 None,
                 temp_lr,
                 None,
                 result_pipe[len(snake_case_			) - 1],
             )					,    )			)

     # start the processes
     for p in process_array_:
          p.start()

     # wait for the processes to end and write their values to the list
     for p in range(0					,    len(snake_case_			)			):
          UpperCAmelCase_           =   result_pipe[p][0].recv()
          process_array_[p].join()
     return arr





def 	lowerCAmelCase_				(					)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   list(range(10					,    0					,    -1			)			)
     print("Initial List"			)
     print(*snake_case_			)
     UpperCAmelCase_           =   odd_even_transposition(snake_case_			)
     print("Sorted List\n"			)
     print(*snake_case_			)


if __name__ == "__main__":
    main()




