code: 
  '''simple docstring'''
  
  import json
  import os
  import tempfile
  
  import datasets
  from datasets.arrow_writer import ArrowWriter
  from datasets.features import ArrayaD
  from utils import generate_examples, get_duration
  
  
  UpperCamelCase_     			=       (30, 4_87)
  UpperCamelCase_     			=       (36, 10_24)
  UpperCamelCase_     			=       (1_00, 1_00)
  UpperCamelCase_     			=       1_00
  
  UpperCamelCase_     			=       datasets.Features(
      {"""text""": ArrayaD(SHAPE_TEST_1, dtype="""float32"""), """image""": ArrayaD(SHAPE_TEST_2, dtype="""float32""")}
  )
  
  UpperCamelCase_			,					UpperCamelCase_     			=       os.path.split(__file__)
  UpperCamelCase_     			=       os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
  
  
  @get_duration
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Optional[Any]  ,      _lowerCamelCase						:       List[Any]  ,      _lowerCamelCase						:       Optional[int]		)    ->						Dict:
    with ArrowWriter(features=_lowerCamelCase  ,      path=os.path.join(_lowerCamelCase  ,      """beta.arrow"""		)		) as writer:
      for key, record in dummy_data:
        _lowerCAmelCase       :  int   				=					my_features.encode_example(_lowerCamelCase		)
        writer.write(_lowerCamelCase		)
      _lowerCAmelCase				,					_lowerCAmelCase       :  Optional[int]   				=					writer.finalize()
  
  
  @get_duration
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       int  ,      _lowerCamelCase						:       Union[str, Any]		)    ->						Tuple:
    _lowerCAmelCase       :  Optional[Any]   				=					datasets.Dataset.from_file(
        filename=os.path.join(_lowerCamelCase  ,      """beta.arrow"""		)  ,      info=datasets.DatasetInfo(features=_lowerCamelCase		)		)
    for _ in dataset:
      pass
  
  
  @get_duration
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Union[str, Any]  ,      _lowerCamelCase						:       Optional[Any]		)    ->						Any:
    _lowerCAmelCase       :  int   				=					datasets.Dataset.from_file(
        filename=os.path.join(_lowerCamelCase  ,      """beta.arrow"""		)  ,      info=datasets.DatasetInfo(features=_lowerCamelCase		)		)
    dataset.set_format("""numpy"""		)
    for _ in dataset:
      pass
  
  
  @get_duration
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Dict  ,      _lowerCamelCase						:       Any		)    ->						List[str]:
    _lowerCAmelCase       :  Any   				=					10
    _lowerCAmelCase       :  Any   				=					datasets.Dataset.from_file(
        filename=os.path.join(_lowerCamelCase  ,      """beta.arrow"""		)  ,      info=datasets.DatasetInfo(features=_lowerCamelCase		)		)
    for i in range(0  ,      len(_lowerCamelCase		)  ,      _lowerCamelCase		):
      _lowerCAmelCase       :  Union[str, Any]   				=					dataset[i : i + batch_size]
  
  
  @get_duration
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       Any  ,      _lowerCamelCase						:       List[str]		)    ->						Optional[int]:
    _lowerCAmelCase       :  str   				=					10
    _lowerCAmelCase       :  Dict   				=					datasets.Dataset.from_file(
        filename=os.path.join(_lowerCamelCase  ,      """beta.arrow"""		)  ,      info=datasets.DatasetInfo(features=_lowerCamelCase		)		)
    dataset.set_format("""numpy"""		)
    for i in range(0  ,      len(_lowerCamelCase		)  ,      _lowerCamelCase		):
      _lowerCAmelCase       :  Optional[Any]   				=					dataset[i : i + batch_size]
  
  
  @get_duration
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       int  ,      _lowerCamelCase						:       Dict		)    ->						Any:
    _lowerCAmelCase       :  Union[str, Any]   				=					datasets.Dataset.from_file(
        filename=os.path.join(_lowerCamelCase  ,      """beta.arrow"""		)  ,      info=datasets.DatasetInfo(features=_lowerCamelCase		)		)
    for col in feats:
      _lowerCAmelCase       :  Any   				=					dataset[col]
  
  
  @get_duration
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       List[str]  ,      _lowerCamelCase						:       Optional[int]		)    ->						Union[str, Any]:
    _lowerCAmelCase       :  List[str]   				=					datasets.Dataset.from_file(
        filename=os.path.join(_lowerCamelCase  ,      """beta.arrow"""		)  ,      info=datasets.DatasetInfo(features=_lowerCamelCase		)		)
    dataset.set_format("""numpy"""		)
    for col in feats:
      _lowerCAmelCase       :  str   				=					dataset[col]
  
  
  def 	_UpperCAmelCase				(  )    ->						Dict:
    _lowerCAmelCase       :  List[str]   				=					{}
    _lowerCAmelCase       :  List[Any]   				=					(
        read_unformated,
        read_formatted_as_numpy,
        read_batch_unformated,
        read_batch_formatted_as_numpy,
        read_col_unformated,
        read_col_formatted_as_numpy,
    )
    with tempfile.TemporaryDirectory() as tmp_dir:
      _lowerCAmelCase       :  Any   				=					datasets.Features({"""image""": ArrayaD(_lowerCamelCase  ,      dtype="""float32"""		)}		)
      _lowerCAmelCase       :  Tuple   				=					generate_examples(features=_lowerCamelCase  ,      num_examples=_lowerCamelCase		)
      _lowerCAmelCase       :  List[str]   				=					write(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase		)
      for read_func in read_functions:
        _lowerCAmelCase       :  Tuple   				=					read_func(_lowerCamelCase  ,      _lowerCamelCase		)
  
    with tempfile.TemporaryDirectory() as tmp_dir:
      # don't use fixed length for fair comparison
      # feats = datasets.Features(
      #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
      # )
      _lowerCAmelCase       :  Optional[int]   				=					datasets.Features({"""image""": datasets.Sequence(datasets.Sequence(datasets.Value("""float32"""		)		)		)}		)
      _lowerCAmelCase       :  Any   				=					generate_examples(
          features=_lowerCamelCase  ,      num_examples=_lowerCamelCase  ,      seq_shapes={"""image""": SPEED_TEST_SHAPE}		)
      _lowerCAmelCase       :  str   				=					write(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase		)
      for read_func in read_functions:
        _lowerCAmelCase       :  List[str]   				=					read_func(_lowerCamelCase  ,      _lowerCamelCase		)
  
    with tempfile.TemporaryDirectory() as tmp_dir:
      # don't use fixed length for fair comparison
      # feats = datasets.Features(
      #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
      # )
      _lowerCAmelCase       :  str   				=					datasets.Features({"""image""": datasets.Sequence(datasets.Value("""float32"""		)		)}		)
      _lowerCAmelCase       :  Optional[int]   				=					generate_examples(
          features=_lowerCamelCase  ,      num_examples=_lowerCamelCase  ,      seq_shapes={"""image""": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}  ,      )
      _lowerCAmelCase       :  int   				=					write(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase		)
      for read_func in read_functions:
        _lowerCAmelCase       :  Union[str, Any]   				=					read_func(_lowerCamelCase  ,      _lowerCamelCase		)
  
    with open(_lowerCamelCase  ,      """wb"""		) as f:
      f.write(json.dumps(_lowerCamelCase		).encode("""utf-8"""		)		)
  
  
  if __name__ == "__main__":  # useful to run the profiler
       benchmark_array_xd()
  
  
  
style_context: 
  '''simple docstring'''
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       int  ,      _lowerCamelCase						:       int  ,      _lowerCamelCase						:       int		)    ->						float:
    if common_ratio == 1:
      # Formula for sum if common ratio is 1
      return num_of_terms * first_term
  
    # Formula for finding sum of n terms of a GeometricProgression
    return (first_term / (1 - common_ratio)) * (1 - common_ratio**num_of_terms)
  
  
  