






from __future__ import annotations

from scipy.special import comb  # type: ignore
class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					list[tuple[float, float]]						):

								'''simple docstring'''
								lowercase__     				=    list_of_points
								# Degree determines the flexibility of the curve.
								# Degree = 1 will produce a straight line.
								lowercase__     				=    len(lowerCamelCase						) - 1




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					float						):

								'''simple docstring'''
								assert 0 <= t <= 1, "Time t must be between 0 and 1."
								lowercase__     				=    []
								for i in range(len(self.list_of_points						)						):
												# basis function for each i
												output_values.append(
												    comb(self.degree,						lowerCamelCase						) * ((1 - t) ** (self.degree - i)) * (t**i)						)
								# the basis must sum up to 1 for it to produce a valid Bezier curve.
								assert round(sum(lowerCamelCase						),						5						) == 1
								return output_values




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					float						):

								'''simple docstring'''
								assert 0 <= t <= 1, "Time t must be between 0 and 1."

								lowercase__     				=    self.basis_function(lowerCamelCase						)
								lowercase__     				=    0.0
								lowercase__     				=    0.0
								for i in range(len(self.list_of_points						)						):
												# For all points, sum up the product of i-th basis function and i-th point.
												x += basis_function[i] * self.list_of_points[i][0]
												y += basis_function[i] * self.list_of_points[i][1]
								return (x, y)




				def        lowercase__     (  self		:					List[Any],						lowerCamelCase		:					float = 0.01						):

								'''simple docstring'''
								from matplotlib import pyplot as plt  # type: ignore

								lowercase__     				=    []  # x coordinates of points to plot
								lowercase__     				=    []  # y coordinates of points to plot

								lowercase__     				=    0.0
								while t <= 1:
												lowercase__     				=    self.bezier_curve_function(lowerCamelCase						)
												to_plot_x.append(value[0]						)
												to_plot_y.append(value[1]						)
												t += step_size

								lowercase__     				=    [i[0] for i in self.list_of_points]
								lowercase__     				=    [i[1] for i in self.list_of_points]

								plt.plot(
								    lowerCamelCase,						lowerCamelCase,						color='''blue''',						label='''Curve of Degree ''' + str(self.degree						),						)
								plt.scatter(lowerCamelCase,						lowerCamelCase,						color='''red''',						label='''Control Points'''						)
								plt.legend()
								plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
							BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
							BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3












import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


A__			:    Dict         =				50_00_00

A__     ,     A__			:    str         =				os.path.split(__file__)
A__			:    Optional[Any]         =				os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.map(**lowerCamelCase_       )






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.filter(**lowerCamelCase_       )






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    {'''num examples''': SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								lowercase__     				=    datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
								lowercase__     				=    generate_example_dataset(
								    os.path.join(lowerCamelCase_	,     '''dataset.arrow'''       )	,     lowerCamelCase_	,     num_examples=lowerCamelCase_       )

								lowercase__     				=    transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''	,     use_fast=lowerCamelCase_       )

								def tokenize(lowerCamelCase_       ):
												return tokenizer(examples['''text''']       )

								lowercase__     				=    map(lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''numpy'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''pandas'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''torch'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''tensorflow'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    filter(lowerCamelCase_       )

								# Activate later when tokenizer support batched inputs
								# with dataset.formatted_as(type='numpy'):
								#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

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


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




