

'''simple docstring'''
from __future__ import annotations

from scipy.special import comb  # type: ignore







class 							A__   :





  def __init__(		self				:     Any							, _a				:     list[tuple[float, float]]			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =list_of_points
       # Degree determines the flexibility of the curve.
       # Degree = 1 will produce a straight line.
       _SCREAMING_SNAKE_CASE							   =len(_a			) - 1





  def 						A  (		self				:     Optional[int]							, _a				:     float			)     ->   list[float]:





       '''simple docstring'''






       assert 0 <= t <= 1, "Time t must be between 0 and 1."
       _SCREAMING_SNAKE_CASE							   =[]
       for i in range(len(self.list_of_points			)			):
            # basis function for each i
            output_values.append(
                comb(self.degree							, _a			) * ((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(_a			)							, 5			) == 1
       return output_values





  def 						A  (		self				:     Union[str, Any]							, _a				:     float			)     ->   tuple[float, float]:





       '''simple docstring'''






       assert 0 <= t <= 1, "Time t must be between 0 and 1."

       _SCREAMING_SNAKE_CASE							   =self.basis_function(_a			)
       _SCREAMING_SNAKE_CASE							   =0.0
       _SCREAMING_SNAKE_CASE							   =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				:     Any							, _a				:     float = 0.01			)     ->   List[str]:





       '''simple docstring'''






       from matplotlib import pyplot as plt  # type: ignore

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

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

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

       plt.plot(
           _a							, _a							, color='blue'							, label='Curve of Degree ' + str(self.degree			)							, )
       plt.scatter(_a							, _a							, 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 argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCamelCase			:				int 			=					logging.getLogger(__name__)

if __name__ == "__main__":
     lowerCamelCase			:				Optional[Any] 			=					argparse.ArgumentParser(
         description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
     )
     parser.add_argument(
         "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
     )
     parser.add_argument(
         "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
     )
     parser.add_argument("--vocab_size", default=3_0_5_2_2, type=int)
     lowerCamelCase			:				Optional[Any] 			=					parser.parse_args()

     logger.info(f'''Loading data from {args.data_file}''')
     with open(args.data_file, "rb") as fp:
          lowerCamelCase			:				Optional[int] 			=					pickle.load(fp)

     logger.info("Counting occurrences for MLM.")
     lowerCamelCase			:				Dict 			=					Counter()
     for tk_ids in data:
          counter.update(tk_ids)
     lowerCamelCase			:				Tuple 			=					[0] * args.vocab_size
     for k, v in counter.items():
          lowerCamelCase			:				Any 			=					v

     logger.info(f'''Dump to {args.token_counts_dump}''')
     with open(args.token_counts_dump, "wb") as handle:
          pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
