'''simple docstring'''






import numpy as np





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : Optional[Any]="utf-8"      ,		__snake_case       : Any="surrogatepass"							):
     _A								=     bin(int.from_bytes(text.encode(__a      ,		__a							)      ,		'big'							)							)[2:]
     return bits.zfill(8 * ((len(__a							) + 7) // 8)							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]      ,		__snake_case       : str="utf-8"      ,		__snake_case       : int="surrogatepass"							):
     _A								=     int(__a      ,		2							)
     return n.to_bytes((n.bit_length() + 7) // 8      ,		'big'							).decode(__a      ,		__a							) or "\0"





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[str]      ,		__snake_case       : Union[str, Any]							):





     if size_par + len(__a							) <= 2**size_par - (len(__a							) - 1):
          raise ValueError('size of parity don\'t match with size of data'							)

     _A								=     []
     _A								=     []
     _A								=     [bin(__a							)[2:] for x in range(1      ,		size_par + len(__a							) + 1							)]

     # sorted information data for the size of the output data
     _A								=     []
     # data position template + parity
     _A								=     []
     # parity bit counter
     _A								=     0
     # counter position of data bits
     _A								=     0

     for x in range(1      ,		size_par + len(__a							) + 1							):
          # Performs a template of bit positions - who should be given,
          # and who should be parity
          if qtd_bp < size_par:
               if (np.log(__a							) / np.log(2							)).is_integer():
                    data_out_gab.append('P'							)
                    _A								=     qtd_bp + 1
               else:
                    data_out_gab.append('D'							)
          else:
               data_out_gab.append('D'							)

          # Sorts the data to the new output size
          if data_out_gab[-1] == "D":
               data_ord.append(data[cont_data]							)
               cont_data += 1
          else:
               data_ord.append(__a							)

    # Calculates parity
     _A								=     0  # parity bit counter
     for bp in range(1      ,		size_par + 1							):
          # Bit counter one for a given parity
          _A								=     0
          # counter to control the loop reading
          _A								=     0
          for x in data_ord:
               if x is not None:
                    try:
                         _A								=     (bin_pos[cont_loop])[-1 * (bp)]
                    except IndexError:
                         _A								=     '''0'''
                    if aux == "1" and x == "1":
                         cont_bo += 1
               cont_loop += 1
          parity.append(cont_bo % 2							)

          qtd_bp += 1

     # Mount the message
     _A								=     0  # parity bit counter
     for x in range(0      ,		size_par + len(__a							)							):
          if data_ord[x] is None:
               data_out.append(str(parity[cont_bp]							)							)
               cont_bp += 1
          else:
               data_out.append(data_ord[x]							)

     return data_out





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]      ,		__snake_case       : List[str]							):
     _A								=     []
     # Parity bit counter
     _A								=     0
     # Counter p data bit reading
     _A								=     0
     # list of parity received
     _A								=     []
     _A								=     []

     for x in range(1      ,		len(__a							) + 1							):
          # Performs a template of bit positions - who should be given,
          #  and who should be parity
          if qtd_bp < size_par and (np.log(__a							) / np.log(2							)).is_integer():
               data_out_gab.append('P'							)
               _A								=     qtd_bp + 1
          else:
               data_out_gab.append('D'							)

          # Sorts the data to the new output size
          if data_out_gab[-1] == "D":
               data_output.append(data[cont_data]							)
          else:
               parity_received.append(data[cont_data]							)
          cont_data += 1

     # -----------calculates the parity with the data
     _A								=     []
     _A								=     []
     _A								=     [bin(__a							)[2:] for x in range(1      ,		size_par + len(__a							) + 1							)]

     #  sorted information data for the size of the output data
     _A								=     []
     # Data position feedback + parity
     _A								=     []
     # Parity bit counter
     _A								=     0
     # Counter p data bit reading
     _A								=     0

     for x in range(1      ,		size_par + len(__a							) + 1							):
          # Performs a template position of bits - who should be given,
          # and who should be parity
          if qtd_bp < size_par and (np.log(__a							) / np.log(2							)).is_integer():
               data_out_gab.append('P'							)
               _A								=     qtd_bp + 1
          else:
               data_out_gab.append('D'							)

          # Sorts the data to the new output size
          if data_out_gab[-1] == "D":
               data_ord.append(data_output[cont_data]							)
               cont_data += 1
          else:
               data_ord.append(__a							)

    # Calculates parity
     _A								=     0  # parity bit counter
     for bp in range(1      ,		size_par + 1							):
          # Bit counter one for a certain parity
          _A								=     0
          # Counter to control loop reading
          _A								=     0
          for x in data_ord:
               if x is not None:
                    try:
                         _A								=     (bin_pos[cont_loop])[-1 * (bp)]
                    except IndexError:
                         _A								=     '''0'''
                    if aux == "1" and x == "1":
                         cont_bo += 1
               cont_loop += 1
          parity.append(str(cont_bo % 2							)							)

          qtd_bp += 1

     # Mount the message
     _A								=     0  # Parity bit counter
     for x in range(0      ,		size_par + len(__a							)							):
          if data_ord[x] is None:
               data_out.append(str(parity[cont_bp]							)							)
               cont_bp += 1
          else:
               data_out.append(data_ord[x]							)

     _A								=     parity_received == parity
     return data_output, ack




'''simple docstring'''






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 lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   def __init__(			self			: Any,   UpperCamelCase__			: NestedDataStructureLike[PathLike],   UpperCamelCase__			: Optional[NamedSplit] = None,   UpperCamelCase__			: Optional[Features] = None,   UpperCamelCase__			: str = None,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: Optional[int] = None,   **UpperCamelCase__			: Optional[Any],   )     -> Tuple:
        super().__init__(
            UpperCamelCase__,   split=UpperCamelCase__,   features=UpperCamelCase__,   cache_dir=UpperCamelCase__,   keep_in_memory=UpperCamelCase__,   streaming=UpperCamelCase__,   num_proc=UpperCamelCase__,   **UpperCamelCase__,   )
        _A								=     path_or_paths if isinstance(UpperCamelCase__,   UpperCamelCase__    ) else {self.split: path_or_paths}
        _A								=     Csv(
            cache_dir=UpperCamelCase__,   data_files=UpperCamelCase__,   features=UpperCamelCase__,   **UpperCamelCase__,   )

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

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







class lowercase_					:







   """simple docstring"""

   def __init__(			self			: Optional[Any],   UpperCamelCase__			: Dataset,   UpperCamelCase__			: Union[PathLike, BinaryIO],   UpperCamelCase__			: Optional[int] = None,   UpperCamelCase__			: Optional[int] = None,   **UpperCamelCase__			: Dict,   )     -> Any:
        if num_proc is not None and num_proc <= 0:
             raise ValueError(f'num_proc {num_proc} must be an integer > 0.'    )

        _A								=     dataset
        _A								=     path_or_buf
        _A								=     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        _A								=     num_proc
        _A								=     'utf-8'
        _A								=     to_csv_kwargs
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> int:
        _A								=     self.to_csv_kwargs.pop('path_or_buf',   UpperCamelCase__    )
        _A								=     self.to_csv_kwargs.pop('header',   UpperCamelCase__    )
        _A								=     self.to_csv_kwargs.pop('index',   UpperCamelCase__    )

        if isinstance(self.path_or_buf,   (str, bytes, os.PathLike)    ):
             with open(self.path_or_buf,   'wb+'    ) as buffer:
                  _A								=     self._write(file_obj=UpperCamelCase__,   header=UpperCamelCase__,   index=UpperCamelCase__,   **self.to_csv_kwargs    )
        else:
             _A								=     self._write(file_obj=self.path_or_buf,   header=UpperCamelCase__,   index=UpperCamelCase__,   **self.to_csv_kwargs    )
        return written
   def   __UpperCAmelCase      (			self			: str,   UpperCamelCase__			: Optional[int]    )     -> str:
        _A		,					_A		,					_A		,					_A								=     args

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

   def   __UpperCAmelCase      (			self			: Union[str, Any],   UpperCamelCase__			: BinaryIO,   UpperCamelCase__			: List[str],   UpperCamelCase__			: Dict,   **UpperCamelCase__			: Optional[int]    )     -> int:
        _A								=     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								=     self._batch_csv((offset, header, index, to_csv_kwargs)    )
                  written += file_obj.write(UpperCamelCase__    )

        else:
             _A		,					_A								=     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,   UpperCamelCase__,   UpperCamelCase__    )],   ),   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(UpperCamelCase__    )

        return written



