



'''simple docstring'''






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowerCAmelCase: int							=						5_0_0_0_0
lowerCAmelCase: Dict							=						5_0_0_0

lowerCAmelCase	,		lowerCAmelCase: List[Any]							=						os.path.split(__file__)
lowerCAmelCase: List[str]							=						os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 				lowerCamelCase__	(						_A			,      _A    ):
  for i in range(_A    ):
    a						: List[str]					       =					dataset[i]
@get_duration
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  for i in range(0			,      len(_A    )			,      _A    ):
    a						: int					       =					dataset[i : i + batch_size]
@get_duration
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  with dataset.formatted_as(type=_A    ):
    for i in range(_A    ):
      a						: Any					       =					dataset[i]
@get_duration
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A    ):
  with dataset.formatted_as(type=_A    ):
    for i in range(0			,      _A			,      _A    ):
      a						: str					       =					dataset[i : i + batch_size]
def 				lowerCamelCase__	(						):
  a						: int					       =					{'num examples': SPEED_TEST_N_EXAMPLES}
  a						: Dict					       =					[
      (read, {'length': SMALL_TEST}),
      (read, {'length': SPEED_TEST_N_EXAMPLES}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
      (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
      (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
      (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
      (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
      (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
      (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
  ]

  a						: List[Any]					       =					[
      (read, {'length': SMALL_TEST}),
      (read, {'length': SPEED_TEST_N_EXAMPLES}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
      (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
      (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
      (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
      (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
  ]
  with tempfile.TemporaryDirectory() as tmp_dir:
    print('generating dataset'    )
    a						: Dict					       =					datasets.Features(
        {'list': datasets.Sequence(datasets.Value('float32'    )    ), 'numbers': datasets.Value('float32'    )}    )
    a						: Dict					       =					generate_example_dataset(
        os.path.join(_A			,      'dataset.arrow'    )			,      _A			,      num_examples=_A			,      seq_shapes={'list': (100,)}			,      )
    print('first set of iterations'    )
    for func, kwargs in functions:
      print(func.__name__			,      str(_A    )    )
      a						: Optional[Any]					       =					func(_A			,      **_A    )

    print('shuffling dataset'    )
    a						: Optional[Any]					       =					dataset.shuffle()
    print('Second set of iterations (after shuffling'    )
    for func, kwargs in functions_shuffled:
      print('shuffled '			,      func.__name__			,      str(_A    )    )
      a						: List[Any]					       =					func(
          _A			,      **_A    )

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


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




'''simple docstring'''






from math import factorial, pi
def 				lowerCamelCase__	(						_A			,      _A = 30    ):

  if not isinstance(_A			,      (int, float)    ):
    raise ValueError('maclaurin_sin() requires either an int or float for theta'    )

  if not isinstance(_A			,      _A    ) or accuracy <= 0:
    raise ValueError('maclaurin_sin() requires a positive int for accuracy'    )

  a						: Dict					       =					float(_A    )
  a						: List[Any]					       =					theta // (2 * pi)
  theta -= 2 * div * pi
  return sum(
      (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1    ) for r in range(_A    )    )
def 				lowerCamelCase__	(						_A			,      _A = 30    ):

  if not isinstance(_A			,      (int, float)    ):
    raise ValueError('maclaurin_cos() requires either an int or float for theta'    )

  if not isinstance(_A			,      _A    ) or accuracy <= 0:
    raise ValueError('maclaurin_cos() requires a positive int for accuracy'    )

  a						: int					       =					float(_A    )
  a						: str					       =					theta // (2 * pi)
  theta -= 2 * div * pi
  return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r    ) for r in range(_A    )    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     print(maclaurin_sin(1_0))
     print(maclaurin_sin(-1_0))
     print(maclaurin_sin(1_0, 1_5))
     print(maclaurin_sin(-1_0, 1_5))

     print(maclaurin_cos(5))
     print(maclaurin_cos(-5))
     print(maclaurin_cos(1_0, 1_5))
     print(maclaurin_cos(-1_0, 1_5))