


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


__a   				=      (30, 4_87)
__a   				=      (36, 10_24)
__a   				=      (1_00, 1_00)
__a   				=      1_00

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

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




@get_duration
def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:







						"""simple docstring"""

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




@get_duration
def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[str]:







						"""simple docstring"""

						lowercase			:   List[str]					   =		datasets.Dataset.from_file(
						    filename=os.path.join(_UpperCamelCase,    '''beta.arrow'''     ),    info=datasets.DatasetInfo(features=_UpperCamelCase     )     )
						for _ in dataset:
												pass




@get_duration
def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Optional[int]:







						"""simple docstring"""

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




@get_duration
def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Tuple:







						"""simple docstring"""

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




@get_duration
def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Union[str, Any]:







						"""simple docstring"""

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




@get_duration
def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->int:







						"""simple docstring"""

						lowercase			:   Any					   =		datasets.Dataset.from_file(
						    filename=os.path.join(_UpperCamelCase,    '''beta.arrow'''     ),    info=datasets.DatasetInfo(features=_UpperCamelCase     )     )
						for col in feats:
												lowercase			:   Optional[int]					   =		dataset[col]




@get_duration
def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Optional[Any]:







						"""simple docstring"""

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




def 			__lowercase	(						)    ->Dict:







						"""simple docstring"""

						lowercase			:   Union[str, Any]					   =		{}
						lowercase			:   Dict					   =		(
						    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:
												lowercase			:   str					   =		datasets.Features({'''image''': ArrayaD(_UpperCamelCase,    dtype='''float32'''     )}     )
												lowercase			:   List[Any]					   =		generate_examples(features=_UpperCamelCase,    num_examples=_UpperCamelCase     )
												lowercase			:   List[str]					   =		write(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
												for read_func in read_functions:
																		lowercase			:   Dict					   =		read_func(_UpperCamelCase,    _UpperCamelCase     )

						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])}
												# )
												lowercase			:   Optional[Any]					   =		datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''     )     )     )}     )
												lowercase			:   Optional[int]					   =		generate_examples(
												    features=_UpperCamelCase,    num_examples=_UpperCamelCase,    seq_shapes={'''image''': SPEED_TEST_SHAPE}     )
												lowercase			:   Any					   =		write(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
												for read_func in read_functions:
																		lowercase			:   Union[str, Any]					   =		read_func(_UpperCamelCase,    _UpperCamelCase     )

						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])}
												# )
												lowercase			:   Optional[Any]					   =		datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''     )     )}     )
												lowercase			:   Optional[int]					   =		generate_examples(
												    features=_UpperCamelCase,    num_examples=_UpperCamelCase,    seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]},    )
												lowercase			:   int					   =		write(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )
												for read_func in read_functions:
																		lowercase			:   List[Any]					   =		read_func(_UpperCamelCase,    _UpperCamelCase     )

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


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





import numpy as np




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Dict:







      """simple docstring"""

      lowercase			:   Optional[Any]					   =		np.array(_UpperCamelCase     )
      lowercase			:   Dict					   =		np.array(_UpperCamelCase     )

      lowercase			:   List[Any]					   =		abs(predict - actual     )
      lowercase			:   int					   =		difference.mean()

      return score




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:







      """simple docstring"""

      lowercase			:   Tuple					   =		np.array(_UpperCamelCase     )
      lowercase			:   str					   =		np.array(_UpperCamelCase     )

      lowercase			:   List[str]					   =		predict - actual
      lowercase			:   Tuple					   =		np.square(_UpperCamelCase     )

      lowercase			:   Union[str, Any]					   =		square_diff.mean()
      return score




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->str:







      """simple docstring"""

      lowercase			:   Union[str, Any]					   =		np.array(_UpperCamelCase     )
      lowercase			:   Optional[Any]					   =		np.array(_UpperCamelCase     )

      lowercase			:   Tuple					   =		predict - actual
      lowercase			:   Dict					   =		np.square(_UpperCamelCase     )
      lowercase			:   Union[str, Any]					   =		square_diff.mean()
      lowercase			:   Union[str, Any]					   =		np.sqrt(_UpperCamelCase     )
      return score




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:







      """simple docstring"""

      lowercase			:   List[str]					   =		np.array(_UpperCamelCase     )
      lowercase			:   List[Any]					   =		np.array(_UpperCamelCase     )

      lowercase			:   Dict					   =		np.log(predict + 1     )
      lowercase			:   List[str]					   =		np.log(actual + 1     )

      lowercase			:   int					   =		log_predict - log_actual
      lowercase			:   Tuple					   =		np.square(_UpperCamelCase     )
      lowercase			:   List[Any]					   =		square_diff.mean()

      lowercase			:   int					   =		np.sqrt(_UpperCamelCase     )

      return score




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[str]:







      """simple docstring"""

      lowercase			:   List[Any]					   =		np.array(_UpperCamelCase     )
      lowercase			:   Optional[int]					   =		np.array(_UpperCamelCase     )

      lowercase			:   Optional[int]					   =		predict - actual
      lowercase			:   Optional[int]					   =		np.sum(_UpperCamelCase     ) / len(_UpperCamelCase     )
      lowercase			:   Dict					   =		np.sum(_UpperCamelCase     ) / len(_UpperCamelCase     )
      # print(numerator, denumerator)
      lowercase			:   int					   =		float(_UpperCamelCase     ) / denumerator * 100

      return score




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:







      """simple docstring"""

      return np.mean(np.array(_UpperCamelCase     ) == np.array(_UpperCamelCase     )     )

