code: 
  
  
  """simple docstring"""
  import json
  import os
  from dataclasses import dataclass
  
  import numpy as np
  import pyarrow as pa
  
  import datasets
  from utils import get_duration
  
  
  UpperCAmelCase   =	1000_0000_0000
  UpperCAmelCase   =	1_0000
  
  UpperCAmelCase ,     UpperCAmelCase   =	os.path.split(__file__)
  UpperCAmelCase   =	os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
  
  def 							_snake_case			(      __snake_case       :			int	,   __snake_case       :			int      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				List[Any]      =		pa.Table.from_pydict({"""col""": [0] * chunk_size}      )
     _lowerCamelCase  :				List[Any]      =		pa.concat_tables([table] * (num_examples // chunk_size)      )
     return datasets.Dataset(__snake_case	,   fingerprint="""table_100B"""      )
  
  
  
  
  @dataclass
  class   lowercase__							:
     __UpperCAmelCase       					=				42
     __UpperCAmelCase       					=				42
     __UpperCAmelCase       					=				42
     __UpperCAmelCase       					=				42
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        _lowerCamelCase  :				Tuple      =		np.random.default_rng(self.seed)
        _lowerCamelCase  :				Tuple      =		rng.integers(low=self.low			,      high=self.high			,      size=self.size).tolist()
  
  
  
  
  
     def __iter__(       self)     ->     Dict:
        return iter(self._sampled_values)
  
  
  
  
  
     def __len__(       self)     ->     Optional[Any]:
        return self.size
  
  
  
  
  
  
  
  @get_duration
  def 							_snake_case			(      __snake_case       :			datasets.Dataset      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Optional[int]      =		dataset[0]
  
  @get_duration
  def 							_snake_case			(      __snake_case       :			datasets.Dataset      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				str      =		dataset[-1]
  
  @get_duration
  def 							_snake_case			(      __snake_case       :			datasets.Dataset      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				int      =		dataset[range(len(__snake_case      ) // 2	,   len(__snake_case      ) // 2 + 1024      )]
  
  @get_duration
  def 							_snake_case			(      __snake_case       :			datasets.Dataset      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Tuple      =		dataset[RandIter(0	,   len(__snake_case      )	,   1024	,   seed=42      )]
  
  def 							_snake_case			(      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Any      =		{"""num examples""": SPEED_TEST_N_EXAMPLES}
     _lowerCamelCase  :				Optional[Any]      =		(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
     print("""generating dataset"""      )
     _lowerCamelCase  :				Union[str, Any]      =		generate_100B_dataset(num_examples=__snake_case	,   chunk_size=__snake_case      )
     print("""Functions"""      )
     for func in functions:
        print(func.__name__      )
        _lowerCamelCase  :				int      =		func(__snake_case      )
  
     with open(__snake_case	,   """wb"""      ) as f:
        f.write(json.dumps(__snake_case      ).encode("""utf-8"""      )      )
  
  
  if __name__ == "__main__":  # useful to run the profiler
   benchmark_table_100B()
  
  
style_context: 
  
  
  """simple docstring"""
  import itertools
  from dataclasses import dataclass
  from io import StringIO
  from typing import Optional
  
  import pyarrow as pa
  
  import datasets
  from datasets.features.features import require_storage_cast
  from datasets.table import table_cast
  
  
  UpperCAmelCase   =	datasets.utils.logging.get_logger(__name__)
  
  
  
  
  @dataclass
  class   lowercase__							(				datasets.BuilderConfig							):
     __UpperCAmelCase       					=				None
     __UpperCAmelCase       					=				"utf-8"
     __UpperCAmelCase       					=				None
     __UpperCAmelCase       					=				10 << 20  # 10MB
     __UpperCAmelCase       					=				False
     __UpperCAmelCase       					=				"line"
  
  
  
  
  
  
  
  class   lowercase__							(				datasets.ArrowBasedBuilder							):
     __UpperCAmelCase       					=				TextConfig
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     int:
        return datasets.DatasetInfo(features=self.config.features)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Optional[int]:
        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}')
        _lowerCamelCase  :				Dict      =		dl_manager.download_and_extract(self.config.data_files)
        if isinstance(SCREAMING_SNAKE_CASE			,      (str, list, tuple)):
           _lowerCamelCase  :				Optional[Any]      =		data_files
           if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
              _lowerCamelCase  :				List[Any]      =		[files]
           _lowerCamelCase  :				List[str]      =		[dl_manager.iter_files(SCREAMING_SNAKE_CASE) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,      gen_kwargs={"""files""": files})]
        _lowerCamelCase  :				int      =		[]
        for split_name, files in data_files.items():
           if isinstance(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE):
              _lowerCamelCase  :				Any      =		[files]
           _lowerCamelCase  :				Any      =		[dl_manager.iter_files(SCREAMING_SNAKE_CASE) for file in files]
           splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE			,      gen_kwargs={"""files""": files}))
        return splits
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     pa.Table:
        if self.config.features is not None:
           _lowerCamelCase  :				Union[str, Any]      =		self.config.features.arrow_schema
           if all(not require_storage_cast(SCREAMING_SNAKE_CASE) for feature in self.config.features.values()):
              # cheaper cast
              _lowerCamelCase  :				Any      =		pa_table.cast(SCREAMING_SNAKE_CASE)
           else:
              # more expensive cast; allows str <-> int/float or str to Audio for example
              _lowerCamelCase  :				Any      =		table_cast(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
           return pa_table
        else:
           return pa_table.cast(pa.schema({"""text""": pa.string()}))
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE)     ->     Any:
        _lowerCamelCase  :				Optional[Any]      =		list(self.config.features) if self.config.features is not None else ["""text"""]
        for file_idx, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE)):
           # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
           with open(SCREAMING_SNAKE_CASE			,      encoding=self.config.encoding			,      errors=self.config.errors) as f:
              if self.config.sample_by == "line":
                 _lowerCamelCase  :				Union[str, Any]      =		0
                 while True:
                    _lowerCamelCase  :				int      =		f.read(self.config.chunksize)
                    if not batch:
                       break
                    batch += f.readline()  # finish current line
                    # StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
                    _lowerCamelCase  :				List[str]      =		StringIO(SCREAMING_SNAKE_CASE).readlines()
                    if not self.config.keep_linebreaks:
                       _lowerCamelCase  :				Optional[Any]      =		[line.rstrip("""\n""") for line in batch]
                    _lowerCamelCase  :				Any      =		pa.Table.from_arrays([pa.array(SCREAMING_SNAKE_CASE)]			,      names=SCREAMING_SNAKE_CASE)
                    # 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(SCREAMING_SNAKE_CASE)
                    batch_idx += 1
              elif self.config.sample_by == "paragraph":
                 _lowerCamelCase  :				Optional[Any]      =		0
                 _lowerCamelCase  :				Dict      =		""""""
                 while True:
                    _lowerCamelCase  :				Union[str, Any]      =		f.read(self.config.chunksize)
                    if not new_batch:
                       break
                    batch += new_batch
                    batch += f.readline()  # finish current line
                    _lowerCamelCase  :				Any      =		batch.split("""\n\n""")
                    _lowerCamelCase  :				Dict      =		pa.Table.from_arrays(
                        [pa.array([example for example in batch[:-1] if example])]			,      names=SCREAMING_SNAKE_CASE)
                    # 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(SCREAMING_SNAKE_CASE)
                    batch_idx += 1
                    _lowerCamelCase  :				Union[str, Any]      =		batch[-1]
                 if batch:
                    _lowerCamelCase  :				Optional[int]      =		pa.Table.from_arrays([pa.array([batch])]			,      names=SCREAMING_SNAKE_CASE)
                    yield (file_idx, batch_idx), self._cast_table(SCREAMING_SNAKE_CASE)
              elif self.config.sample_by == "document":
                 _lowerCamelCase  :				Tuple      =		f.read()
                 _lowerCamelCase  :				List[Any]      =		pa.Table.from_arrays([pa.array([text])]			,      names=SCREAMING_SNAKE_CASE)
                 yield file_idx, self._cast_table(SCREAMING_SNAKE_CASE)
  
  