

'''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


snake_case_       =       (3_0, 4_8_7)
snake_case_       =       (3_6, 1_0_2_4)
snake_case_       =       (1_0_0, 1_0_0)
snake_case_       =       1_0_0

snake_case_       =       datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

snake_case_  ,	snake_case_       =       os.path.split(__file__)
snake_case_       =       os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	str  ,  SCREAMING_SNAKE_CASE_     :	Any  ,  SCREAMING_SNAKE_CASE_     :	str    )							->   Optional[int]:
      """simple docstring"""

      with ArrowWriter(features=SCREAMING_SNAKE_CASE_  ,  path=os.path.join(SCREAMING_SNAKE_CASE_  ,  "beta.arrow"    )    ) as writer:
            for key, record in dummy_data:
                  SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       my_features.encode_example(SCREAMING_SNAKE_CASE_    )
                  writer.write(SCREAMING_SNAKE_CASE_    )
            SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			List[Any]     =       writer.finalize()


@get_duration
def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	List[Any]  ,  SCREAMING_SNAKE_CASE_     :	str    )							->   Tuple:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Tuple     =       datasets.Dataset.from_file(
          filename=os.path.join(SCREAMING_SNAKE_CASE_  ,  "beta.arrow"    )  ,  info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_    )    )
      for _ in dataset:
            pass


@get_duration
def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Tuple  ,  SCREAMING_SNAKE_CASE_     :	str    )							->   Any:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			str     =       datasets.Dataset.from_file(
          filename=os.path.join(SCREAMING_SNAKE_CASE_  ,  "beta.arrow"    )  ,  info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_    )    )
      dataset.set_format("numpy"    )
      for _ in dataset:
            pass


@get_duration
def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	str  ,  SCREAMING_SNAKE_CASE_     :	Optional[int]    )							->   Any:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Any     =       1_0
      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       datasets.Dataset.from_file(
          filename=os.path.join(SCREAMING_SNAKE_CASE_  ,  "beta.arrow"    )  ,  info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_    )    )
      for i in range(0  ,  len(SCREAMING_SNAKE_CASE_    )  ,  SCREAMING_SNAKE_CASE_    ):
            SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       dataset[i : i + batch_size]


@get_duration
def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	List[str]  ,  SCREAMING_SNAKE_CASE_     :	List[str]    )							->   Dict:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       1_0
      SCREAMING_SNAKE_CASE_      :			str     =       datasets.Dataset.from_file(
          filename=os.path.join(SCREAMING_SNAKE_CASE_  ,  "beta.arrow"    )  ,  info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_    )    )
      dataset.set_format("numpy"    )
      for i in range(0  ,  len(SCREAMING_SNAKE_CASE_    )  ,  SCREAMING_SNAKE_CASE_    ):
            SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       dataset[i : i + batch_size]


@get_duration
def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Tuple  ,  SCREAMING_SNAKE_CASE_     :	Any    )							->   Optional[int]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			List[Any]     =       datasets.Dataset.from_file(
          filename=os.path.join(SCREAMING_SNAKE_CASE_  ,  "beta.arrow"    )  ,  info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_    )    )
      for col in feats:
            SCREAMING_SNAKE_CASE_      :			Any     =       dataset[col]


@get_duration
def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Union[str, Any]  ,  SCREAMING_SNAKE_CASE_     :	Dict    )							->   List[str]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       datasets.Dataset.from_file(
          filename=os.path.join(SCREAMING_SNAKE_CASE_  ,  "beta.arrow"    )  ,  info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_    )    )
      dataset.set_format("numpy"    )
      for col in feats:
            SCREAMING_SNAKE_CASE_      :			List[str]     =       dataset[col]


def 					__lowerCamelCase			(      )							->   int:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Tuple     =       {}
      SCREAMING_SNAKE_CASE_      :			str     =       (
          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:
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       datasets.Features({"image": ArrayaD(SCREAMING_SNAKE_CASE_  ,  dtype="float32"    )}    )
            SCREAMING_SNAKE_CASE_      :			Tuple     =       generate_examples(features=SCREAMING_SNAKE_CASE_  ,  num_examples=SCREAMING_SNAKE_CASE_    )
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       write(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    )
            for read_func in read_functions:
                  SCREAMING_SNAKE_CASE_      :			Any     =       read_func(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    )

      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])}
            # )
            SCREAMING_SNAKE_CASE_      :			List[Any]     =       datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"    )    )    )}    )
            SCREAMING_SNAKE_CASE_      :			Optional[int]     =       generate_examples(
                features=SCREAMING_SNAKE_CASE_  ,  num_examples=SCREAMING_SNAKE_CASE_  ,  seq_shapes={"image": SPEED_TEST_SHAPE}    )
            SCREAMING_SNAKE_CASE_      :			int     =       write(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    )
            for read_func in read_functions:
                  SCREAMING_SNAKE_CASE_      :			List[Any]     =       read_func(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    )

      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])}
            # )
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       datasets.Features({"image": datasets.Sequence(datasets.Value("float32"    )    )}    )
            SCREAMING_SNAKE_CASE_      :			Any     =       generate_examples(
                features=SCREAMING_SNAKE_CASE_  ,  num_examples=SCREAMING_SNAKE_CASE_  ,  seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}  ,  )
            SCREAMING_SNAKE_CASE_      :			Any     =       write(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    )
            for read_func in read_functions:
                  SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       read_func(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    )

      with open(SCREAMING_SNAKE_CASE_  ,  "wb"    ) as f:
            f.write(json.dumps(SCREAMING_SNAKE_CASE_    ).encode("utf-8"    )    )


if __name__ == "__main__":  # useful to run the profiler
      benchmark_array_xd()





'''simple docstring'''






def 					_snake_case   (     A ,    A       )						->    float:
							return a + b


if __name__ == "__main__":
		__UpperCAmelCase    				=							5
		__UpperCAmelCase    				=							6
		print(f"""The sum of {a} + {b} is {add(a, b)}""")