


'''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     :Any												=  (3_0, 4_8_7)
__UpperCAmelCase     :Optional[Any]												=  (3_6, 1_0_2_4)
__UpperCAmelCase     :int												=  (1_0_0, 1_0_0)
__UpperCAmelCase     :List[str]												=  1_0_0

__UpperCAmelCase     :Union[str, Any]												=  datasets.Features(
    {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
)

__UpperCAmelCase							,       __UpperCAmelCase     :Optional[int]												=  os.path.split(__file__)
__UpperCAmelCase     :Tuple												=  os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))




@get_duration
def       _a      (		_lowercase   :      Tuple     ,   _lowercase   :      int     ,   _lowercase   :      str    ):





     '''simple docstring'''






     with ArrowWriter(features=_lowercase     ,   path=os.path.join(_lowercase     ,   '''beta.arrow'''    )    ) as writer:
          for key, record in dummy_data:
               __UpperCAmelCase      :					Optional[int]    				=     my_features.encode_example(_lowercase    )
               writer.write(_lowercase    )
          __UpperCAmelCase				,  __UpperCAmelCase      :					int    				=     writer.finalize()




@get_duration
def       _a      (		_lowercase   :      Union[str, Any]     ,   _lowercase   :      List[str]    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Optional[int]    				=     datasets.Dataset.from_file(
         filename=os.path.join(_lowercase     ,   '''beta.arrow'''    )     ,   info=datasets.DatasetInfo(features=_lowercase    )    )
     for _ in dataset:
          pass




@get_duration
def       _a      (		_lowercase   :      Optional[Any]     ,   _lowercase   :      Union[str, Any]    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Dict    				=     datasets.Dataset.from_file(
         filename=os.path.join(_lowercase     ,   '''beta.arrow'''    )     ,   info=datasets.DatasetInfo(features=_lowercase    )    )
     dataset.set_format('''numpy'''    )
     for _ in dataset:
          pass




@get_duration
def       _a      (		_lowercase   :      Optional[Any]     ,   _lowercase   :      Tuple    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Optional[int]    				=     10
     __UpperCAmelCase      :					int    				=     datasets.Dataset.from_file(
         filename=os.path.join(_lowercase     ,   '''beta.arrow'''    )     ,   info=datasets.DatasetInfo(features=_lowercase    )    )
     for i in range(0     ,   len(_lowercase    )     ,   _lowercase    ):
          __UpperCAmelCase      :					List[str]    				=     dataset[i : i + batch_size]




@get_duration
def       _a      (		_lowercase   :      Optional[int]     ,   _lowercase   :      Optional[int]    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Dict    				=     10
     __UpperCAmelCase      :					List[Any]    				=     datasets.Dataset.from_file(
         filename=os.path.join(_lowercase     ,   '''beta.arrow'''    )     ,   info=datasets.DatasetInfo(features=_lowercase    )    )
     dataset.set_format('''numpy'''    )
     for i in range(0     ,   len(_lowercase    )     ,   _lowercase    ):
          __UpperCAmelCase      :					Optional[Any]    				=     dataset[i : i + batch_size]




@get_duration
def       _a      (		_lowercase   :      int     ,   _lowercase   :      Optional[Any]    ):





     '''simple docstring'''






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




@get_duration
def       _a      (		_lowercase   :      Dict     ,   _lowercase   :      int    ):





     '''simple docstring'''






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




def       _a      (		):





     '''simple docstring'''






     __UpperCAmelCase      :					str    				=     {}
     __UpperCAmelCase      :					Optional[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:
          __UpperCAmelCase      :					int    				=     datasets.Features({'''image''': ArrayaD(_lowercase     ,   dtype='''float32'''    )}    )
          __UpperCAmelCase      :					List[Any]    				=     generate_examples(features=_lowercase     ,   num_examples=_lowercase    )
          __UpperCAmelCase      :					Any    				=     write(_lowercase     ,   _lowercase     ,   _lowercase    )
          for read_func in read_functions:
               __UpperCAmelCase      :					Tuple    				=     read_func(_lowercase     ,   _lowercase    )

     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])}
          # )
          __UpperCAmelCase      :					Union[str, Any]    				=     datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''    )    )    )}    )
          __UpperCAmelCase      :					str    				=     generate_examples(
              features=_lowercase     ,   num_examples=_lowercase     ,   seq_shapes={'''image''': SPEED_TEST_SHAPE}    )
          __UpperCAmelCase      :					List[Any]    				=     write(_lowercase     ,   _lowercase     ,   _lowercase    )
          for read_func in read_functions:
               __UpperCAmelCase      :					Tuple    				=     read_func(_lowercase     ,   _lowercase    )

     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])}
          # )
          __UpperCAmelCase      :					Any    				=     datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''    )    )}    )
          __UpperCAmelCase      :					Dict    				=     generate_examples(
              features=_lowercase     ,   num_examples=_lowercase     ,   seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}     ,   )
          __UpperCAmelCase      :					Tuple    				=     write(_lowercase     ,   _lowercase     ,   _lowercase    )
          for read_func in read_functions:
               __UpperCAmelCase      :					Dict    				=     read_func(_lowercase     ,   _lowercase    )

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


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



'''simple docstring'''



def       _a      (		_lowercase   :      list    ):





     '''simple docstring'''






     if len(_lowercase    ) <= 1:
          return data
     else:
          return [
              *quick_sort([e for e in data[1:] if e <= data[0]]    ),
              data[0],
              *quick_sort([e for e in data[1:] if e > data[0]]    ),
          ]


if __name__ == "__main__":
      import doctest

      doctest.testmod()