



from __future__ import annotations

from scipy.special import comb  # type: ignore




class      __UpperCamelCase       :







	def __init__(    self ,				_lowerCAmelCase						)    ->    Any:
							'''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   _a   (    self ,				_lowerCAmelCase						)    ->    list[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   _a   (    self ,				_lowerCAmelCase						)    ->    tuple[float, 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   _a   (    self ,				_lowerCAmelCase = 0.01						)    ->    Union[str, Any]:
							'''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







'''simple docstring'''


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_      :				int   					=    50_0000

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







@get_duration
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      datasets.Dataset ,  **lowercase_    :      Dict						):
						lowercase        =							dataset.map(**lowercase_						)







@get_duration
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      datasets.Dataset ,  **lowercase_    :      Optional[int]						):
						lowercase        =							dataset.filter(**lowercase_						)







def      SCREAMING_SNAKE_CASE				(				):
						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(lowercase_ ,  """dataset.arrow"""						) ,  lowercase_ ,  num_examples=lowercase_						)

												lowercase        =							transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" ,  use_fast=lowercase_						)

												def tokenize(lowercase_    :      Dict						):
																		return tokenizer(examples["""text"""]						)

												lowercase        =							map(lowercase_						)

												lowercase        =							map(lowercase_ ,  batched=lowercase_						)

												lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""numpy"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""pandas"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""torch""" ,  columns="""numbers"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""tensorflow""" ,  columns="""numbers"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												lowercase        =							map(lowercase_ ,  function=lowercase_ ,  batched=lowercase_						)

												lowercase        =							filter(lowercase_						)

												# 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(lowercase_ ,  """wb"""						) as f:
												f.write(json.dumps(lowercase_						).encode("""utf-8"""						)						)


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


