


"""simple docstring"""





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_SCREAMING_SNAKE_CASE       :  str        = 5_0_0_0_0_0

_SCREAMING_SNAKE_CASE       :  List[Any]        = os.path.split(__file__)
_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))



@get_duration
def        _lowerCAmelCase      (						UpperCAmelCase     :							datasets.Dataset				):


					'''simple docstring'''






					UpperCamelCase__		:    Union[str, Any]         =dataset.select(range(0      ,							len(UpperCAmelCase				)      ,							2				)				)



@get_duration
def        _lowerCAmelCase      (						UpperCAmelCase     :							datasets.Dataset				):


					'''simple docstring'''






					UpperCamelCase__		:    Optional[int]         =dataset.sort('''numbers'''				)



@get_duration
def        _lowerCAmelCase      (						UpperCAmelCase     :							datasets.Dataset				):


					'''simple docstring'''






					UpperCamelCase__		:    str         =dataset.shuffle()



@get_duration
def        _lowerCAmelCase      (						UpperCAmelCase     :							datasets.Dataset				):


					'''simple docstring'''






					UpperCamelCase__		:    Union[str, Any]         =dataset.train_test_split(0.1				)



@get_duration
def        _lowerCAmelCase      (						UpperCAmelCase     :							datasets.Dataset      ,							UpperCAmelCase     :							Dict=10				):


					'''simple docstring'''






					for shard_id in range(UpperCAmelCase				):
										UpperCamelCase__		:    Dict         =dataset.shard(UpperCAmelCase      ,							UpperCAmelCase				)



def        _lowerCAmelCase      (						):


					'''simple docstring'''






					UpperCamelCase__		:    Dict         ={'''num examples''': SPEED_TEST_N_EXAMPLES}
					UpperCamelCase__		:    Union[str, Any]         =(select, sort, shuffle, train_test_split, shard)
					with tempfile.TemporaryDirectory() as tmp_dir:
										print('''generating dataset'''				)
										UpperCamelCase__		:    List[Any]         =datasets.Features({'''text''': datasets.Value('''string'''				), '''numbers''': datasets.Value('''float32'''				)}				)
										UpperCamelCase__		:    Any         =generate_example_dataset(
										    os.path.join(UpperCAmelCase      ,							'''dataset.arrow'''				)      ,							UpperCAmelCase      ,							num_examples=UpperCAmelCase				)
										print('''Functions'''				)
										for func in functions:
															print(func.__name__				)
															UpperCamelCase__		:    Union[str, Any]         =func(UpperCAmelCase				)

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


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





"""simple docstring"""





import math as m



def        _lowerCAmelCase      (						UpperCAmelCase     :							Optional[int]      ,							UpperCAmelCase     :							Optional[Any]      ,							UpperCAmelCase     :							Optional[Any]=0.001				):


					'''simple docstring'''






					return (f(a + h				) - f(a - h				)) / (2 * h)



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[str]      ,							UpperCAmelCase     :							Optional[Any]=0      ,							UpperCAmelCase     :							Optional[int]=100      ,							UpperCAmelCase     :							Union[str, Any]=0.0001      ,							UpperCAmelCase     :							Dict=1E-6      ,							UpperCAmelCase     :							Any=False				):


					'''simple docstring'''






					UpperCamelCase__		:    Optional[Any]         =xa  # set the initial guess
					UpperCamelCase__		:    List[Any]         =[a]
					UpperCamelCase__		:    Optional[Any]         =abs(f(UpperCAmelCase				)				)
					UpperCamelCase__		:    List[str]         =lambda UpperCAmelCase				: calc_derivative(UpperCAmelCase      ,							UpperCAmelCase      ,							h=UpperCAmelCase				)  # noqa: E731  Derivative of f(x)
					for _ in range(UpperCAmelCase				):
										if fa(UpperCAmelCase				) == 0:
															raise ValueError('''No converging solution found'''				)
										UpperCamelCase__		:    Any         =a - f(UpperCAmelCase				) / fa(UpperCAmelCase				)  # Calculate the next estimate
										if logsteps:
															steps.append(UpperCAmelCase				)
										if error < maxerror:
															break
					else:
										raise ValueError('''Iteration limit reached, no converging solution found'''				)
					if logsteps:
										# If logstep is true, then log intermediate steps
										return a, error, steps
					return a, error


if __name__ == "__main__":
				from matplotlib import pyplot as plt

				_SCREAMING_SNAKE_CASE       :  str        = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       :  Any        = newton_raphson(
				    f, xa=1_0, maxiter=1_0_0_0, step=1e-6, logsteps=True
				)
				plt.plot([abs(f(x)) for x in steps])
				plt.xlabel("""step""")
				plt.ylabel("""error""")
				plt.show()
				print(F'''solution = {{{solution:f}}}, error = {{{error:f}}}''')


