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
  
  
  lowercase__						=			(3_0, 4_8_7)
  lowercase__						=			(3_6, 1_0_2_4)
  lowercase__						=			(1_0_0, 1_0_0)
  lowercase__						=			1_0_0
  
  lowercase__						=			datasets.Features(
      {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
  )
  
  lowercase__						=			os.path.split(__file__)
  lowercase__						=			os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
  
  
  
  
  @get_duration
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       List[str]:
  
  
    '''simple docstring'''
  
  
    with ArrowWriter(features=_UpperCamelCase   ,					path=os.path.join(_UpperCamelCase   ,					'''beta.arrow''' ) ) as writer:
      for key, record in dummy_data:
        snake_case     :					List[str]     =	my_features.encode_example(_UpperCamelCase )
        writer.write(_UpperCamelCase )
      snake_case ,snake_case     :					Any     =	writer.finalize()
  
  
  
  
  @get_duration
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:
  
  
    '''simple docstring'''
  
  
    snake_case     :					int     =	datasets.Dataset.from_file(
        filename=os.path.join(_UpperCamelCase   ,					'''beta.arrow''' )   ,					info=datasets.DatasetInfo(features=_UpperCamelCase ) )
    for _ in dataset:
      pass
  
  
  
  
  @get_duration
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Optional[int]:
  
  
    '''simple docstring'''
  
  
    snake_case     :					Tuple     =	datasets.Dataset.from_file(
        filename=os.path.join(_UpperCamelCase   ,					'''beta.arrow''' )   ,					info=datasets.DatasetInfo(features=_UpperCamelCase ) )
    dataset.set_format('''numpy''' )
    for _ in dataset:
      pass
  
  
  
  
  @get_duration
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       List[Any]:
  
  
    '''simple docstring'''
  
  
    snake_case     :					Optional[Any]     =	10
    snake_case     :					Optional[Any]     =	datasets.Dataset.from_file(
        filename=os.path.join(_UpperCamelCase   ,					'''beta.arrow''' )   ,					info=datasets.DatasetInfo(features=_UpperCamelCase ) )
    for i in range(0   ,					len(_UpperCamelCase )   ,					_UpperCamelCase ):
      snake_case     :					Dict     =	dataset[i : i + batch_size]
  
  
  
  
  @get_duration
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Any:
  
  
    '''simple docstring'''
  
  
    snake_case     :					Tuple     =	10
    snake_case     :					Union[str, Any]     =	datasets.Dataset.from_file(
        filename=os.path.join(_UpperCamelCase   ,					'''beta.arrow''' )   ,					info=datasets.DatasetInfo(features=_UpperCamelCase ) )
    dataset.set_format('''numpy''' )
    for i in range(0   ,					len(_UpperCamelCase )   ,					_UpperCamelCase ):
      snake_case     :					str     =	dataset[i : i + batch_size]
  
  
  
  
  @get_duration
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       List[Any]:
  
  
    '''simple docstring'''
  
  
    snake_case     :					Optional[int]     =	datasets.Dataset.from_file(
        filename=os.path.join(_UpperCamelCase   ,					'''beta.arrow''' )   ,					info=datasets.DatasetInfo(features=_UpperCamelCase ) )
    for col in feats:
      snake_case     :					Tuple     =	dataset[col]
  
  
  
  
  @get_duration
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Tuple:
  
  
    '''simple docstring'''
  
  
    snake_case     :					List[str]     =	datasets.Dataset.from_file(
        filename=os.path.join(_UpperCamelCase   ,					'''beta.arrow''' )   ,					info=datasets.DatasetInfo(features=_UpperCamelCase ) )
    dataset.set_format('''numpy''' )
    for col in feats:
      snake_case     :					List[Any]     =	dataset[col]
  
  
  
  
  def       _UpperCamelCase	(					)     ->       Tuple:
  
  
    '''simple docstring'''
  
  
    snake_case     :					int     =	{}
    snake_case     :					Tuple     =	(
        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:
      snake_case     :					Union[str, Any]     =	datasets.Features({'''image''': ArrayaD(_UpperCamelCase   ,					dtype='''float32''' )} )
      snake_case     :					Optional[Any]     =	generate_examples(features=_UpperCamelCase   ,					num_examples=_UpperCamelCase )
      snake_case     :					List[str]     =	write(_UpperCamelCase   ,					_UpperCamelCase   ,					_UpperCamelCase )
      for read_func in read_functions:
        snake_case     :					Dict     =	read_func(_UpperCamelCase   ,					_UpperCamelCase )
  
    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])}
      # )
      snake_case     :					Any     =	datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32''' ) ) )} )
      snake_case     :					Any     =	generate_examples(
          features=_UpperCamelCase   ,					num_examples=_UpperCamelCase   ,					seq_shapes={'''image''': SPEED_TEST_SHAPE} )
      snake_case     :					Tuple     =	write(_UpperCamelCase   ,					_UpperCamelCase   ,					_UpperCamelCase )
      for read_func in read_functions:
        snake_case     :					List[Any]     =	read_func(_UpperCamelCase   ,					_UpperCamelCase )
  
    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])}
      # )
      snake_case     :					Optional[Any]     =	datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32''' ) )} )
      snake_case     :					Any     =	generate_examples(
          features=_UpperCamelCase   ,					num_examples=_UpperCamelCase   ,					seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}   ,					)
      snake_case     :					List[str]     =	write(_UpperCamelCase   ,					_UpperCamelCase   ,					_UpperCamelCase )
      for read_func in read_functions:
        snake_case     :					Union[str, Any]     =	read_func(_UpperCamelCase   ,					_UpperCamelCase )
  
    with open(_UpperCamelCase   ,					'''wb''' ) as f:
      f.write(json.dumps(_UpperCamelCase ).encode('''utf-8''' ) )
  
  
  if __name__ == "__main__":  # useful to run the profiler
        benchmark_array_xd()
  
  
  
style_context: 
  '''simple docstring'''
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowercase__						=			{
      "configuration_flava": [
          "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "FlavaConfig",
          "FlavaImageCodebookConfig",
          "FlavaImageConfig",
          "FlavaMultimodalConfig",
          "FlavaTextConfig",
      ],
  }
  
  try:
  						if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						lowercase__						=			["FlavaFeatureExtractor"]
  						lowercase__						=			["FlavaImageProcessor"]
  						lowercase__						=			["FlavaProcessor"]
  
  try:
  						if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						lowercase__						=			[
  						    "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
  						    "FlavaForPreTraining",
  						    "FlavaImageCodebook",
  						    "FlavaImageModel",
  						    "FlavaModel",
  						    "FlavaMultimodalModel",
  						    "FlavaPreTrainedModel",
  						    "FlavaTextModel",
  						]
  
  if TYPE_CHECKING:
  						from .configuration_flava import (
  						    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
  						    FlavaConfig,
  						    FlavaImageCodebookConfig,
  						    FlavaImageConfig,
  						    FlavaMultimodalConfig,
  						    FlavaTextConfig,
  						)
  
  						try:
  												if not is_vision_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .feature_extraction_flava import FlavaFeatureExtractor
  												from .image_processing_flava import FlavaImageProcessor
  												from .processing_flava import FlavaProcessor
  
  						try:
  												if not is_torch_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_flava import (
  												    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    FlavaForPreTraining,
  												    FlavaImageCodebook,
  												    FlavaImageModel,
  												    FlavaModel,
  												    FlavaMultimodalModel,
  												    FlavaPreTrainedModel,
  												    FlavaTextModel,
  												)
  
  else:
  						import sys
  
  						lowercase__						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  