





import unittest

import numpy as np



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray | None = None					,				)	->						np.ndarray:







      '''simple docstring'''






      __A          =				np.shape(lowerCamelCase )
      __A          =				np.shape(lowerCamelCase )
      __A          =				np.shape(lowerCamelCase )

      if shape_a[0] != shape_b[0]:
            __A          =				(
                '''Expected the same number of rows for A and B. '''
                F"""Instead found A of size {shape_a} and B of size {shape_b}"""
            )
            raise ValueError(lowerCamelCase )

      if shape_b[1] != shape_c[1]:
            __A          =				(
                '''Expected the same number of columns for B and C. '''
                F"""Instead found B of size {shape_b} and C of size {shape_c}"""
            )
            raise ValueError(lowerCamelCase )

      __A          =				pseudo_inv
      if a_inv is None:
            try:
                  __A          =				np.linalg.inv(lowerCamelCase )
            except np.linalg.LinAlgError:
                  raise ValueError(
                      '''Input matrix A is not invertible. Cannot compute Schur complement.''' )

      return mat_c - mat_b.T @ a_inv @ mat_b





class A_					(			unittest.TestCase   ):




   def _lowerCAmelCase      (self	:Dict )->					None:
         __A          =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
         __A          =				np.array([[0, 3], [3, 0], [2, 3]] )
         __A          =				np.array([[2, 1], [6, 3]] )

         __A          =				schur_complement(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )

         __A          =				np.block([[a, b], [b.T, c]] )

         __A          =				np.linalg.det(_UpperCamelCase )
         __A          =				np.linalg.det(_UpperCamelCase )
         __A          =				np.linalg.det(_UpperCamelCase )

         self.assertAlmostEqual(_UpperCamelCase    ,   det_a * det_s )




   def _lowerCAmelCase      (self	:List[Any] )->					None:
         __A          =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
         __A          =				np.array([[0, 3], [3, 0], [2, 3]] )
         __A          =				np.array([[2, 1], [6, 3]] )

         with self.assertRaises(_UpperCamelCase ):
               schur_complement(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )






   def _lowerCAmelCase      (self	:Dict )->					None:
         __A          =				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
         __A          =				np.array([[0, 3], [3, 0], [2, 3]] )
         __A          =				np.array([[2, 1, 3], [6, 3, 5]] )

         with self.assertRaises(_UpperCamelCase ):
               schur_complement(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       unittest.main()












import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
       snake_case__					:							Optional[int]													=     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')
       snake_case__					:							Optional[int]													=     parser.parse_args()

       if args.model_type == "bert":
              snake_case__					:							Dict													=     BertForMaskedLM.from_pretrained(args.model_name)
              snake_case__					:							Union[str, Any]													=     'bert'
       else:
              raise ValueError('args.model_type should be "bert".')

       snake_case__					:							Optional[int]													=     model.state_dict()
       snake_case__					:							List[Any]													=     {}

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

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

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

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

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





