



import argparse

import torch

from transformers import BertForMaskedLM


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

		if args.model_type == "bert":
				_lowercase		:				List[str]      		=BertForMaskedLM.from_pretrained(args.model_name)
				_lowercase		:				Optional[Any]      		="bert"
		else:
				raise ValueError("args.model_type should be \"bert\".")

		_lowercase		:				Optional[int]      		=model.state_dict()
		_lowercase		:				str      		={}

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

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

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

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

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

def      lowerCAmelCase_					(  _lowercase    :       int)							->	list[int]:







			"""simple docstring"""

			if num <= 0:
						a__       :						Tuple							=  F'''{num}: Invalid input, please enter a positive integer.'''
						raise ValueError(_lowercase)

			a__       :						List[Any]							=  [True] * (num + 1)
			a__       :						List[str]							=  []
			a__       :						List[Any]							=  2
			a__       :						Optional[int]							=  int(math.sqrt(_lowercase))

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(_lowercase)

									# Set multiples of start be False
									for i in range(start * start						,       num + 1						,       _lowercase):
												if sieve[i] is True:
															a__       :						Optional[int]							=  False

						start += 1

			for j in range(end + 1						,       num + 1):
						if sieve[j] is True:
									prime.append(_lowercase)

			return prime


if __name__ == "__main__":
		print(prime_sieve(int(input("Enter a positive integer: ").strip())))




