



'''simple docstring'''


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
		lowercase							       =			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''')
		lowercase							       =			parser.parse_args()

		if args.model_type == "bert":
				lowercase							       =			BertForMaskedLM.from_pretrained(args.model_name)
				lowercase							       =			'''bert'''
		else:
				raise ValueError('''args.model_type should be "bert".''')

		lowercase							       =			model.state_dict()
		lowercase							       =			{}

		for w in ["word_embeddings", "position_embeddings"]:
				lowercase							       =			state_dict[F"""{prefix}.embeddings.{w}.weight"""]
		for w in ["weight", "bias"]:
				lowercase							       =			state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

		lowercase							       =			0
		for teacher_idx in [0, 2, 4, 7, 9, 11]:
				for w in ["weight", "bias"]:
						lowercase							       =			state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
						]
						lowercase							       =			state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
						]
						lowercase							       =			state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
						]

						lowercase							       =			state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
						]
						lowercase							       =			state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
						]

						lowercase							       =			state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
						]
						lowercase							       =			state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
						]
						lowercase							       =			state_dict[
						    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
						]
				std_idx += 1

		lowercase							       =			state_dict['''cls.predictions.decoder.weight''']
		lowercase							       =			state_dict['''cls.predictions.bias''']
		if args.vocab_transform:
				for w in ["weight", "bias"]:
						lowercase							       =			state_dict[F"""cls.predictions.transform.dense.{w}"""]
						lowercase							       =			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)







'''simple docstring'''


from typing import Any

import numpy as np






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     return np.array_equal(lowercase__       ,	matrix.conjugate().T       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=v.conjugate().T
     a_       		=v_star.dot(lowercase__       )
     assert isinstance(lowercase__       ,	np.ndarray       )
     return (v_star_dot.dot(lowercase__       )) / (v_star.dot(lowercase__       ))






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		=np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]       )
     a_       		=np.array([[1], [2], [3]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     print(rayleigh_quotient(lowercase__       ,	lowercase__       )       )

     a_       		=np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     assert rayleigh_quotient(lowercase__       ,	lowercase__       ) == float(3       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()


