





"""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


lowerCamelCase__      =      (30, 487)
lowerCamelCase__      =      (36, 1024)
lowerCamelCase__      =      (100, 100)
lowerCamelCase__      =      100

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

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




@get_duration
def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Optional[int]:




      """simple docstring"""

      with ArrowWriter(features=_lowercase						,path=os.path.join(_lowercase						,"beta.arrow"	)	) as writer:
            for key, record in dummy_data:
                  _UpperCamelCase					:		int  =				my_features.encode_example(_lowercase	)
                  writer.write(_lowercase	)
            _UpperCamelCase					:		int  =				writer.finalize()




@get_duration
def     lowercase__			(    lowercase_						,lowercase_	)    ->						Union[str, Any]:




      """simple docstring"""

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




@get_duration
def     lowercase__			(    lowercase_						,lowercase_	)    ->						str:




      """simple docstring"""

      _UpperCamelCase					:		str  =				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     lowercase__			(    lowercase_						,lowercase_	)    ->						List[Any]:




      """simple docstring"""

      _UpperCamelCase					:		Tuple  =				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					:		Dict  =				dataset[i : i + batch_size]




@get_duration
def     lowercase__			(    lowercase_						,lowercase_	)    ->						int:




      """simple docstring"""

      _UpperCamelCase					:		List[Any]  =				10
      _UpperCamelCase					:		List[str]  =				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					:		Union[str, Any]  =				dataset[i : i + batch_size]




@get_duration
def     lowercase__			(    lowercase_						,lowercase_	)    ->						Optional[int]:




      """simple docstring"""

      _UpperCamelCase					:		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     lowercase__			(    lowercase_						,lowercase_	)    ->						Dict:




      """simple docstring"""

      _UpperCamelCase					:		Union[str, 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					:		str  =				dataset[col]




def     lowercase__			(    )    ->						int:




      """simple docstring"""

      _UpperCamelCase					:		List[Any]  =				{}
      _UpperCamelCase					:		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:
            _UpperCamelCase					:		List[str]  =				datasets.Features({"image": ArrayaD(_lowercase						,dtype="float32"	)}	)
            _UpperCamelCase					:		Union[str, Any]  =				generate_examples(features=_lowercase						,num_examples=_lowercase	)
            _UpperCamelCase					:		Any  =				write(_lowercase						,_lowercase						,_lowercase	)
            for read_func in read_functions:
                  _UpperCamelCase					:		List[str]  =				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					:		Dict  =				datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"	)	)	)}	)
            _UpperCamelCase					:		Tuple  =				generate_examples(
                features=_lowercase						,num_examples=_lowercase						,seq_shapes={"image": SPEED_TEST_SHAPE}	)
            _UpperCamelCase					:		Tuple  =				write(_lowercase						,_lowercase						,_lowercase	)
            for read_func in read_functions:
                  _UpperCamelCase					:		List[Any]  =				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					:		List[str]  =				datasets.Features({"image": datasets.Sequence(datasets.Value("float32"	)	)}	)
            _UpperCamelCase					:		Union[str, Any]  =				generate_examples(
                features=_lowercase						,num_examples=_lowercase						,seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}						,)
            _UpperCamelCase					:		Optional[int]  =				write(_lowercase						,_lowercase						,_lowercase	)
            for read_func in read_functions:
                  _UpperCamelCase					:		str  =				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 = 10**6   ):

						if n <= 0:
												raise ValueError('''Please enter an integer greater than 0'''   )

						_UpperCamelCase			:					Any     =    list(range(n + 1   )   )
						for number in range(2       ,						n + 1   ):
												if phi[number] == number:
																		phi[number] -= 1
																		for multiple in range(number * 2       ,						n + 1       ,						_lowercase   ):
																								_UpperCamelCase			:					List[str]     =    (phi[multiple] // number) * (number - 1)

						_UpperCamelCase			:					Union[str, Any]     =    1
						for number in range(1       ,						n + 1   ):
												if (answer / phi[answer]) < (number / phi[number]):
																		_UpperCamelCase			:					List[str]     =    number

						return answer


if __name__ == "__main__":
						print(solution())



