
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 							A__		(     __SCREAMING_SNAKE_CASE):
      def __init__(				self   ,  __magic_name__   ,  __magic_name__ = None   ,  __magic_name__ = None   ,  __magic_name__ = None   ,  __magic_name__ = False   ,  __magic_name__ = False   ,  __magic_name__ = None   ,  **__magic_name__   ,  ):
       super().__init__(
           __magic_name__   ,  split=__magic_name__   ,  features=__magic_name__   ,  cache_dir=__magic_name__   ,  keep_in_memory=__magic_name__   ,  streaming=__magic_name__   ,  num_proc=__magic_name__   ,  **__magic_name__   ,  )
       lowerCamelCase		:     Union[str, Any]          =				path_or_paths if isinstance(__magic_name__   ,  __magic_name__				) else {self.split: path_or_paths}
       lowerCamelCase		:     Optional[int]          =				Csv(
           cache_dir=__magic_name__   ,  data_files=__magic_name__   ,  features=__magic_name__   ,  **__magic_name__   ,  )






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

        self.builder.download_and_prepare(
            download_config=__magic_name__   ,  download_mode=__magic_name__   ,  verification_mode=__magic_name__   ,  base_path=__magic_name__   ,  num_proc=self.num_proc   ,  )
        lowerCamelCase		:     int          =				self.builder.as_dataset(
            split=self.split   ,  verification_mode=__magic_name__   ,  in_memory=self.keep_in_memory				)
       return dataset





class 							A__		:
      def __init__(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__ = None   ,  __magic_name__ = None   ,  **__magic_name__   ,  ):
       if num_proc is not None and num_proc <= 0:
        raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''				)

       lowerCamelCase		:     Dict          =				dataset
       lowerCamelCase		:     Optional[int]          =				path_or_buf
       lowerCamelCase		:     Optional[Any]          =				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       lowerCamelCase		:     Optional[int]          =				num_proc
       lowerCamelCase		:     Dict          =				"""utf-8"""
       lowerCamelCase		:     str          =				to_csv_kwargs
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     str          =				self.to_csv_kwargs.pop("""path_or_buf"""   ,  __magic_name__				)
       lowerCamelCase		:     Dict          =				self.to_csv_kwargs.pop("""header"""   ,  __magic_name__				)
       lowerCamelCase		:     Tuple          =				self.to_csv_kwargs.pop("""index"""   ,  __magic_name__				)

       if isinstance(self.path_or_buf   ,  (str, bytes, os.PathLike)				):
        with open(self.path_or_buf   ,  """wb+"""				) as buffer:
         lowerCamelCase		:     Optional[Any]          =				self._write(file_obj=__magic_name__   ,  header=__magic_name__   ,  index=__magic_name__   ,  **self.to_csv_kwargs				)
       else:
        lowerCamelCase		:     Union[str, Any]          =				self._write(file_obj=self.path_or_buf   ,  header=__magic_name__   ,  index=__magic_name__   ,  **self.to_csv_kwargs				)
       return written
      def 						UpperCamelCase__							(				self   ,  __magic_name__				):
       lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase		:     str          =				args

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






      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  **__magic_name__				):
       lowerCamelCase		:     Dict          =				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"""   ,  ):
         lowerCamelCase		:     str          =				self._batch_csv((offset, header, index, to_csv_kwargs)				)
         written += file_obj.write(__magic_name__				)

       else:
        lowerCamelCase      ,   lowerCamelCase		:     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   ,  __magic_name__   ,  __magic_name__				)]   ,  )   ,  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(__magic_name__				)

       return written






import sys





def    _a (						lowerCamelCase,     lowerCamelCase							):
 if not numbers:
  return 0
 if target == 0 and target in numbers:
  return 0
 if not isinstance(lowerCamelCase,     (list, tuple)							) or not all(
     isinstance(lowerCamelCase,     lowerCamelCase							) for number in numbers							):
  raise ValueError("""numbers must be an iterable of integers"""							)

 lowerCamelCase		:     str          =				0
 lowerCamelCase		:     List[str]          =				sys.maxsize

 while right < len(lowerCamelCase							):
  curr_sum += numbers[right]
  while curr_sum >= target and left <= right:
   lowerCamelCase		:     Any          =				min(lowerCamelCase,     right - left + 1							)
   curr_sum -= numbers[left]
   left += 1
  right += 1

 return 0 if min_len == sys.maxsize else min_len




