
'''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()




'''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__)


