


'''simple docstring'''





import argparse

import torch

from transformers import BertForMaskedLM


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

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

	a : Union[str, Any]		=				model.state_dict()
	a : str		=				{}

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

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

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

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

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

import typing
from collections.abc import Iterable

import numpy as np

a : Optional[int]		=				typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
a : Union[str, Any]		=				typing.Union[np.floataa, int, float]  # noqa: UP007


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			return np.sqrt(np.sum((np.asarray(_UpperCamelCase  ) - np.asarray(_UpperCamelCase  )) ** 2  )  )


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			return sum((va - va) ** 2 for va, va in zip(_UpperCamelCase       ,			_UpperCamelCase  )  ) ** (1 / 2)


if __name__ == "__main__":


	def 		lowercase_  (     ):


				'''simple docstring'''







				from timeit import timeit

				print('''Without Numpy'''  )
				print(
				    timeit(
				        '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'''       ,			number=1_00_00       ,			globals=globals()       ,			)  )
				print('''With Numpy'''  )
				print(
				    timeit(
				        '''euclidean_distance([1, 2, 3], [4, 5, 6])'''       ,			number=1_00_00       ,			globals=globals()       ,			)  )





	benchmark()


