


from __future__ import annotations

from scipy.special import comb  # type: ignore

class 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      list_of_points
									# Degree determines the flexibility of the curve.
									# Degree = 1 will produce a straight line.
									_UpperCAmelCase					=      len(_SCREAMING_SNAKE_CASE						) - 1
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	list[float]:




									"""simple docstring"""





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




									"""simple docstring"""





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

									_UpperCAmelCase					=      self.basis_function(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      0.0
									_UpperCAmelCase					=      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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE = 0.01						)			->	Optional[int]:




									"""simple docstring"""





									from matplotlib import pyplot as plt  # type: ignore

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

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

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

									plt.plot(
									    _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   color='blue'      ,   label='Curve of Degree ' + str(self.degree						)      ,   )
									plt.scatter(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   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 argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
		lowerCAmelCase__  :Tuple             =							argparse.ArgumentParser(
		    description=(
		        '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
		        ''' Distillation'''
		    )
		)
		parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
		parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
		parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
		parser.add_argument('''--vocab_transform''', action='''store_true''')
		lowerCAmelCase__  :Optional[int]             =							parser.parse_args()

		if args.model_type == "bert":
				lowerCAmelCase__  :Tuple             =							BertForMaskedLM.from_pretrained(args.model_name)
				lowerCAmelCase__  :Optional[int]             =							'''bert'''
		else:
				raise ValueError('''args.model_type should be "bert".''')

		lowerCAmelCase__  :Any             =							model.state_dict()
		lowerCAmelCase__  :Dict             =							{}

		for w in ["word_embeddings", "position_embeddings"]:
				lowerCAmelCase__  :List[Any]             =							state_dict[f'''{prefix}.embeddings.{w}.weight''']
		for w in ["weight", "bias"]:
				lowerCAmelCase__  :Union[str, Any]             =							state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

		lowerCAmelCase__  :str             =							0
		for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
				for w in ["weight", "bias"]:
						lowerCAmelCase__  :Any             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
						]
						lowerCAmelCase__  :List[str]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
						]

						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
						]
						lowerCAmelCase__  :int             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
						]

						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
						]
						lowerCAmelCase__  :List[Any]             =							state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
						]
				std_idx += 1

		lowerCAmelCase__  :Optional[int]             =							state_dict['''cls.predictions.decoder.weight''']
		lowerCAmelCase__  :List[str]             =							state_dict['''cls.predictions.bias''']
		if args.vocab_transform:
				for w in ["weight", "bias"]:
						lowerCAmelCase__  :Any             =							state_dict[f'''cls.predictions.transform.dense.{w}''']
						lowerCAmelCase__  :List[str]             =							state_dict[f'''cls.predictions.transform.LayerNorm.{w}''']

		print(f'''N layers selected for distillation: {std_idx}''')
		print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

		print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
		torch.save(compressed_sd, args.dump_checkpoint)
