import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   def __init__(     self				,  lowerCAmelCase_				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = False				,  lowerCAmelCase_ = False				,  lowerCAmelCase_ = None				,  **lowerCAmelCase_				,  )						->str:
        super().__init__(
            lowerCAmelCase_				,  split=lowerCAmelCase_				,  features=lowerCAmelCase_				,  cache_dir=lowerCAmelCase_				,  keep_in_memory=lowerCAmelCase_				,  streaming=lowerCAmelCase_				,  num_proc=lowerCAmelCase_				,  **lowerCAmelCase_				,  )
        a	:     List[Any]						      =    path_or_paths if isinstance(lowerCAmelCase_				,  lowerCAmelCase_     ) else {self.split: path_or_paths}
        a	:     Any						      =    Csv(
            cache_dir=lowerCAmelCase_				,  data_files=lowerCAmelCase_				,  features=lowerCAmelCase_				,  **lowerCAmelCase_				,  )


   def _lowercase(     self     )						->str:
        # Build iterable dataset
        if self.streaming:
             a	:     str						      =    self.builder.as_streaming_dataset(split=self.split     )
        # Build regular (map-style) dataset
        else:
             a	:     List[str]						      =    None
             a	:     Any						      =    None
             a	:     Any						      =    None
             a	:     Dict						      =    None

             self.builder.download_and_prepare(
                 download_config=lowerCAmelCase_				,  download_mode=lowerCAmelCase_				,  verification_mode=lowerCAmelCase_				,  base_path=lowerCAmelCase_				,  num_proc=self.num_proc				,  )
             a	:     Optional[Any]						      =    self.builder.as_dataset(
                 split=self.split				,  verification_mode=lowerCAmelCase_				,  in_memory=self.keep_in_memory     )
        return dataset






class 				_SCREAMING_SNAKE_CASE:
   def __init__(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  **lowerCAmelCase_				,  )						->List[str]:
        if num_proc is not None and num_proc <= 0:
             raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""     )

        a	:     int						      =    dataset
        a	:     List[Any]						      =    path_or_buf
        a	:     Optional[Any]						      =    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        a	:     Tuple						      =    num_proc
        a	:     Union[str, Any]						      =    '''utf-8'''
        a	:     List[str]						      =    to_csv_kwargs
   def _lowercase(     self     )						->int:
        a	:     List[str]						      =    self.to_csv_kwargs.pop('''path_or_buf'''				,  lowerCAmelCase_     )
        a	:     Any						      =    self.to_csv_kwargs.pop('''header'''				,  lowerCAmelCase_     )
        a	:     Union[str, Any]						      =    self.to_csv_kwargs.pop('''index'''				,  lowerCAmelCase_     )

        if isinstance(self.path_or_buf				,  (str, bytes, os.PathLike)     ):
             with open(self.path_or_buf				,  '''wb+'''     ) as buffer:
                  a	:     Optional[Any]						      =    self._write(file_obj=lowerCAmelCase_				,  header=lowerCAmelCase_				,  index=lowerCAmelCase_				,  **self.to_csv_kwargs     )
        else:
             a	:     Dict						      =    self._write(file_obj=self.path_or_buf				,  header=lowerCAmelCase_				,  index=lowerCAmelCase_				,  **self.to_csv_kwargs     )
        return written
   def _lowercase(     self				,  lowerCAmelCase_     )						->List[Any]:
        a      ,				a      ,				a      ,				a	:     Dict						      =    args

        a	:     Dict						      =    query_table(
            table=self.dataset.data				,  key=slice(lowerCAmelCase_				,  offset + self.batch_size     )				,  indices=self.dataset._indices				,  )
        a	:     int						      =    batch.to_pandas().to_csv(
            path_or_buf=lowerCAmelCase_				,  header=header if (offset == 0) else False				,  index=lowerCAmelCase_				,  **lowerCAmelCase_     )
        return csv_str.encode(self.encoding     )


   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  **lowerCAmelCase_     )						->int:
        a	:     List[Any]						      =    0

        if self.num_proc is None or self.num_proc == 1:
             for offset in logging.tqdm(
                 range(0				,  len(self.dataset     )				,  self.batch_size     )				,  unit='''ba'''				,  disable=not logging.is_progress_bar_enabled()				,  desc='''Creating CSV from Arrow format'''				,  ):
                  a	:     Any						      =    self._batch_csv((offset, header, index, to_csv_kwargs)     )
                  written += file_obj.write(lowerCAmelCase_     )

        else:
             a      ,				a	:     Optional[Any]						      =    len(self.dataset     ), self.batch_size
             with multiprocessing.Pool(self.num_proc     ) as pool:
                  for csv_str in logging.tqdm(
                      pool.imap(
                          self._batch_csv				,  [(offset, header, index, to_csv_kwargs) for offset in range(0				,  lowerCAmelCase_				,  lowerCAmelCase_     )]				,  )				,  total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size				,  unit='''ba'''				,  disable=not logging.is_progress_bar_enabled()				,  desc='''Creating CSV from Arrow format'''				,  ):
                       written += file_obj.write(lowerCAmelCase_     )

        return written
UpperCAmelCase				 =   {
    0: """underwear""",
    1: """pants""",
    2: """belt""",
    3: """suit""",
    4: """shoe""",
    5: """socks""",
    6: """shirt""",
    7: """tie""",
    8: """watch""",
}

UpperCAmelCase				 =   [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

UpperCAmelCase				 =   [0 for x in range(len(graph))]
UpperCAmelCase				 =   []







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						List[Any]:
     a	:     Tuple						      =    1
     while stack:
          a	:     Optional[Any]						      =    stack.pop()
          print(lowerCAmelCase_    ,   clothes[current_clothing]							)
          order += 1







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Any:
     a	:     Union[str, Any]						      =    1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)

     stack.append(lowerCAmelCase_							)







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Optional[Any]:
     for v in range(len(lowerCAmelCase_							)							):
          if not visited[v]:
               depth_first_search(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)


if __name__ == "__main__":
  topological_sort(graph, visited)
  print(stack)
  print_stack(stack, clothes)