


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   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")
     _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   parser.parse_args()

     if args.model_type == "bert":
          _SCREAMING_SNAKE_CASE      :			Tuple      				=   BertForMaskedLM.from_pretrained(args.model_name)
          _SCREAMING_SNAKE_CASE      :			Dict      				=   'bert'
     else:
          raise ValueError("args.model_type should be \"bert\".")

     _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   model.state_dict()
     _SCREAMING_SNAKE_CASE      :			List[str]      				=   {}

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

     _SCREAMING_SNAKE_CASE      :			List[Any]      				=   0
     for teacher_idx in [0, 2, 4, 7, 9, 11]:
          for w in ["weight", "bias"]:
               _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
               ]
               _SCREAMING_SNAKE_CASE      :			Any      				=   state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
               ]
               _SCREAMING_SNAKE_CASE      :			List[Any]      				=   state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
               ]

               _SCREAMING_SNAKE_CASE      :			Tuple      				=   state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
               ]
               _SCREAMING_SNAKE_CASE      :			str      				=   state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
               ]

               _SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
               ]
               _SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
               ]
               _SCREAMING_SNAKE_CASE      :			Tuple      				=   state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
               ]
          std_idx += 1

     _SCREAMING_SNAKE_CASE      :			Tuple      				=   state_dict['cls.predictions.decoder.weight']
     _SCREAMING_SNAKE_CASE      :			Optional[int]      				=   state_dict['cls.predictions.bias']
     if args.vocab_transform:
          for w in ["weight", "bias"]:
               _SCREAMING_SNAKE_CASE      :			Dict      				=   state_dict[f'''cls.predictions.transform.dense.{w}''']
               _SCREAMING_SNAKE_CASE      :			Optional[int]      				=   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)








from __future__ import annotations

from scipy.special import comb  # type: ignore

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case   ):
         snake_case					=    list_of_points
         # Degree determines the flexibility of the curve.
         # Degree = 1 will produce a straight line.
         snake_case					=    len(__snake_case   ) - 1
    def 		a_      (	self      , __snake_case   ):
         assert 0 <= t <= 1, "Time t must be between 0 and 1."
         snake_case					=    []
         for i in range(len(self.list_of_points   )   ):
              # basis function for each i
              output_values.append(
                  comb(self.degree      , __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(__snake_case   )      , 5   ) == 1
         return output_values
    def 		a_      (	self      , __snake_case   ):
         assert 0 <= t <= 1, "Time t must be between 0 and 1."

         snake_case					=    self.basis_function(__snake_case   )
         snake_case					=    0.0
         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      , __snake_case = 0.01   ):
         from matplotlib import pyplot as plt  # type: ignore

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

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

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

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




