
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


__lowerCAmelCase			:     int           =   datasets.utils.logging.get_logger(__name__)

__lowerCAmelCase			:     str           =   ['names', 'prefix']
__lowerCAmelCase			:     List[Any]           =   ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
__lowerCAmelCase			:     Dict           =   ['encoding_errors', 'on_bad_lines']
__lowerCAmelCase			:     int           =   ['date_format']




@dataclass
class        UpperCAmelCase_			( datasets.BuilderConfig     ):


  '''simple docstring'''







  a__           =		","
  a__           =		None
  a__           =		"infer"
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		True
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		False
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		True
  a__           =		True
  a__           =		False
  a__           =		True
  a__           =		None
  a__           =		"."
  a__           =		None
  a__           =		'"'
  a__           =		0
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		None
  a__           =		True
  a__           =		True
  a__           =		0
  a__           =		True
  a__           =		False
  a__           =		None
  a__           =		1_00_00
  a__           =		None
  a__           =		"strict"
  a__           =		"error"
  a__           =		None
  def 							_lowercase							( self			: Tuple    )			->	Optional[Any]:







    """simple docstring"""






    if self.delimiter is not None:
      __magic_name__				     =       self.delimiter
    if self.column_names is not None:
      __magic_name__				     =       self.column_names





  @property
  def 							_lowercase							( self			: Any    )			->	str:







    """simple docstring"""






    __magic_name__				     =       {
        """sep""": self.sep,
        """header""": self.header,
        """names""": self.names,
        """index_col""": self.index_col,
        """usecols""": self.usecols,
        """prefix""": self.prefix,
        """mangle_dupe_cols""": self.mangle_dupe_cols,
        """engine""": self.engine,
        """converters""": self.converters,
        """true_values""": self.true_values,
        """false_values""": self.false_values,
        """skipinitialspace""": self.skipinitialspace,
        """skiprows""": self.skiprows,
        """nrows""": self.nrows,
        """na_values""": self.na_values,
        """keep_default_na""": self.keep_default_na,
        """na_filter""": self.na_filter,
        """verbose""": self.verbose,
        """skip_blank_lines""": self.skip_blank_lines,
        """thousands""": self.thousands,
        """decimal""": self.decimal,
        """lineterminator""": self.lineterminator,
        """quotechar""": self.quotechar,
        """quoting""": self.quoting,
        """escapechar""": self.escapechar,
        """comment""": self.comment,
        """encoding""": self.encoding,
        """dialect""": self.dialect,
        """error_bad_lines""": self.error_bad_lines,
        """warn_bad_lines""": self.warn_bad_lines,
        """skipfooter""": self.skipfooter,
        """doublequote""": self.doublequote,
        """memory_map""": self.memory_map,
        """float_precision""": self.float_precision,
        """chunksize""": self.chunksize,
        """encoding_errors""": self.encoding_errors,
        """on_bad_lines""": self.on_bad_lines,
        """date_format""": self.date_format,
    }

    # some kwargs must not be passed if they don't have a default value
    # some others are deprecated and we can also not pass them if they are the default value
    for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
      if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()			,							UpperCamelCase__    ):
        del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
    if not (datasets.config.PANDAS_VERSION.major >= 2):
      for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
        del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
    if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
      for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
        del pd_read_csv_kwargs[pd_read_csv_parameter]

    return pd_read_csv_kwargs







class        UpperCAmelCase_			( datasets.ArrowBasedBuilder     ):


  '''simple docstring'''







  a__           =		CsvConfig
  def 							_lowercase							( self			: Union[str, Any]    )			->	str:







    """simple docstring"""






    return datasets.DatasetInfo(features=self.config.features    )
  def 							_lowercase							( self			: Any			,							UpperCamelCase__			: List[Any]    )			->	int:







    """simple docstring"""






    if not self.config.data_files:
      raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''    )
    __magic_name__				     =       dl_manager.download_and_extract(self.config.data_files    )
    if isinstance(UpperCamelCase__			,							(str, list, tuple)    ):
      __magic_name__				     =       data_files
      if isinstance(UpperCamelCase__			,							UpperCamelCase__    ):
        __magic_name__				     =       [files]
      __magic_name__				     =       [dl_manager.iter_files(UpperCamelCase__    ) for file in files]
      return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,							gen_kwargs={"""files""": files}    )]
    __magic_name__				     =       []
    for split_name, files in data_files.items():
      if isinstance(UpperCamelCase__			,							UpperCamelCase__    ):
        __magic_name__				     =       [files]
      __magic_name__				     =       [dl_manager.iter_files(UpperCamelCase__    ) for file in files]
      splits.append(datasets.SplitGenerator(name=UpperCamelCase__			,							gen_kwargs={"""files""": files}    )    )
    return splits
  def 							_lowercase							( self			: Dict			,							UpperCamelCase__			: pa.Table    )			->	pa.Table:







    """simple docstring"""






    if self.config.features is not None:
      __magic_name__				     =       self.config.features.arrow_schema
      if all(not require_storage_cast(UpperCamelCase__    ) for feature in self.config.features.values()    ):
        # cheaper cast
        __magic_name__				     =       pa.Table.from_arrays([pa_table[field.name] for field in schema]			,							schema=UpperCamelCase__    )
      else:
        # more expensive cast; allows str <-> int/float or str to Audio for example
        __magic_name__				     =       table_cast(UpperCamelCase__			,							UpperCamelCase__    )
    return pa_table





  def 							_lowercase							( self			: Any			,							UpperCamelCase__			: List[Any]    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       self.config.features.arrow_schema if self.config.features else None
    # dtype allows reading an int column as str
    __magic_name__				     =       (
        {
            name: dtype.to_pandas_dtype() if not require_storage_cast(UpperCamelCase__    ) else object
            for name, dtype, feature in zip(schema.names			,							schema.types			,							self.config.features.values()    )
        }
        if schema is not None
        else None
    )
    for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase__    )    ):
      __magic_name__				     =       pd.read_csv(UpperCamelCase__			,							iterator=UpperCamelCase__			,							dtype=UpperCamelCase__			,							**self.config.pd_read_csv_kwargs    )
      try:
        for batch_idx, df in enumerate(UpperCamelCase__    ):
          __magic_name__				     =       pa.Table.from_pandas(UpperCamelCase__    )
          # Uncomment for debugging (will print the Arrow table size and elements)
          # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
          # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
          yield (file_idx, batch_idx), self._cast_table(UpperCamelCase__    )
      except ValueError as e:
        logger.error(F'''Failed to read file \'{file}\' with error {type(UpperCamelCase__    )}: {e}'''    )
        raise



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

import torch_xla.distributed.xla_multiprocessing as xmp

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       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=A_,     default=1,     help="""Number of TPU cores to use (1 or 8)."""					)

  # positional
  parser.add_argument(
      """training_script""",     type=A_,     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=A_					)

  return parser.parse_args()

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       parse_args()

  # Import training_script as a module.
  __magic_name__				     =       Path(args.training_script					)
  sys.path.append(str(script_fpath.parent.resolve()					)					)
  __magic_name__				     =       script_fpath.stem
  __magic_name__				     =       importlib.import_module(A_					)

  # Patch sys.argv
  __magic_name__				     =       [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()

