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       :  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'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  UpperCamelCase_     			=       {
      """configuration_deformable_detr""": ["""DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeformableDetrConfig"""],
  }
  
  try:
       if not is_vision_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       ["""DeformableDetrFeatureExtractor"""]
       UpperCamelCase_     			=       ["""DeformableDetrImageProcessor"""]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       [
           """DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """DeformableDetrForObjectDetection""",
           """DeformableDetrModel""",
           """DeformableDetrPreTrainedModel""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig
  
       try:
            if not is_vision_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
            from .image_processing_deformable_detr import DeformableDetrImageProcessor
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_deformable_detr import (
                DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                DeformableDetrForObjectDetection,
                DeformableDetrModel,
                DeformableDetrPreTrainedModel,
            )
  
  else:
       import sys
  
       UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  