


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      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   def __init__(		self						,    __A						,    __A = None						,    __A = None						,    __A = None						,    __A = False						,    __A = False						,    __A = None						,    **__A						,    ):






     """simple docstring"""





     super().__init__(
         __A						,    split=__A						,    features=__A						,    cache_dir=__A						,    keep_in_memory=__A						,    streaming=__A						,    num_proc=__A						,    **__A						,    )
     lowerCamelCase			: Any   =    path_or_paths if isinstance(__A						,    __A			) else {self.split: path_or_paths}
     lowerCamelCase			: Union[str, Any]   =    Csv(
         cache_dir=__A						,    data_files=__A						,    features=__A						,    **__A						,    )






   def _snake_case   (		self			):






     """simple docstring"""





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

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



class      UpperCAmelCase_ :

   '''simple docstring'''




   def __init__(		self						,    __A						,    __A						,    __A = None						,    __A = None						,    **__A						,    ):






     """simple docstring"""





     if num_proc is not None and num_proc <= 0:
       raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""			)

     lowerCamelCase			: List[Any]   =    dataset
     lowerCamelCase			: Optional[Any]   =    path_or_buf
     lowerCamelCase			: Any   =    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
     lowerCamelCase			: List[str]   =    num_proc
     lowerCamelCase			: Dict   =    "utf-8"
     lowerCamelCase			: Optional[int]   =    to_csv_kwargs






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Union[str, Any]   =    self.to_csv_kwargs.pop("path_or_buf"						,    __A			)
     lowerCamelCase			: Dict   =    self.to_csv_kwargs.pop("header"						,    __A			)
     lowerCamelCase			: int   =    self.to_csv_kwargs.pop("index"						,    __A			)

     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=__A						,    header=__A						,    index=__A						,    **self.to_csv_kwargs			)
     else:
       lowerCamelCase			: Any   =    self._write(file_obj=self.path_or_buf						,    header=__A						,    index=__A						,    **self.to_csv_kwargs			)
     return written






   def _snake_case   (		self						,    __A			):






     """simple docstring"""





     lowerCamelCase			: List[str]   =    args

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






   def _snake_case   (		self						,    __A						,    __A						,    __A						,    **__A			):






     """simple docstring"""





     lowerCamelCase			: 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"						,    ):
         lowerCamelCase			: int   =    self._batch_csv((offset, header, index, to_csv_kwargs)			)
         written += file_obj.write(__A			)

     else:
       lowerCamelCase			: List[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						,    __A						,    __A			)]						,    )						,    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(__A			)

     return written








import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: str   =    ArgumentParser(
      description=(
          "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
      )     )

  # Optional arguments for the launch helper
  parser.add_argument("--num_cores"				,						type=SCREAMING_SNAKE_CASE_				,						default=1				,						help="Number of TPU cores to use (1 or 8)."     )

  # positional
  parser.add_argument(
      "training_script"				,						type=SCREAMING_SNAKE_CASE_				,						help=(
          "The full path to the single TPU training "
          "program/script to be launched in parallel, "
          "followed by all the arguments for the "
          "training script"
      )				,						)

  # rest from the training program
  parser.add_argument("training_script_args"				,						nargs=SCREAMING_SNAKE_CASE_     )

  return parser.parse_args()






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: Optional[Any]   =    parse_args()

  # Import training_script as a module.
  lowerCamelCase			: Tuple   =    Path(args.training_script     )
  sys.path.append(str(script_fpath.parent.resolve()     )     )
  lowerCamelCase			: int   =    script_fpath.stem
  lowerCamelCase			: Tuple   =    importlib.import_module(SCREAMING_SNAKE_CASE_     )

  # Patch sys.argv
  lowerCamelCase			: Union[str, Any]   =    [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores     )]

  xmp.spawn(mod._mp_fn				,						args=()				,						nprocs=args.num_cores     )


if __name__ == "__main__":
   main()



