






from math import floor, sqrt



def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Optional[int]:
				A__     :							Optional[int]   				=0.0
				A__     :							List[Any]   				=1.0
				A__     :							Union[str, Any]   				=int(sqrt(snake_case_	)	)
				A__     :							Dict   				=root
				A__     :							Optional[int]   				=0
				while integer_part != 2 * root:
								A__     :							List[Any]   				=denominator * integer_part - numerator
								A__     :							str   				=(n - numerator**2) / denominator
								A__     :							Union[str, Any]   				=int((root + numerator) / denominator	)
								period += 1
				return period



def   SCREAMING_SNAKE_CASE__						(							snake_case_ = 1_0_0_0_0	)			->							List[str]:
				A__     :							Optional[int]   				=0
				for i in range(2,	n + 1	):
								A__     :							str   				=sqrt(snake_case_	)
								if sr - floor(snake_case_	) != 0 and continuous_fraction_period(snake_case_	) % 2 == 1:
												count_odd_periods += 1
				return count_odd_periods


if __name__ == "__main__":
						print(F"{solution(int(input().strip()))}")







import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


__lowerCamelCase       :       Optional[Any]				   =			collections.namedtuple(
    "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
)



def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							CheckpointState:
				print(f'Reading saved model from {model_file}'	)
				A__     :							Optional[int]   				=torch.load(snake_case_,	map_location=lambda snake_case_,	snake_case_	: default_restore_location(snake_case_,	'''cpu'''	)	)
				return CheckpointState(**snake_case_	)



class 			a      :






		def __init__(  self	,     __UpperCamelCase							)->			Union[str, Any]:
						'''simple docstring'''


						A__     :							Dict   				=src_file






		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
						'''simple docstring'''


						raise NotImplementedError






		@staticmethod
		def       lowerCAmelCase_  (  __UpperCamelCase	,     *__UpperCamelCase	,     **__UpperCamelCase							)->			"DPRState":
						'''simple docstring'''


						if comp_type.startswith('''c'''							):
										return DPRContextEncoderState(*__UpperCamelCase	,     **__UpperCamelCase							)
						if comp_type.startswith('''q'''							):
										return DPRQuestionEncoderState(*__UpperCamelCase	,     **__UpperCamelCase							)
						if comp_type.startswith('''r'''							):
										return DPRReaderState(*__UpperCamelCase	,     **__UpperCamelCase							)
						else:
										raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''							)







class 			a      (      UpperCamelCase_   ):






		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
						'''simple docstring'''


						A__     :							Optional[int]   				=DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''							)[0]							)							)
						print(F'Loading DPR biencoder from {self.src_file}'							)
						A__     :							Optional[int]   				=load_states_from_checkpoint(self.src_file							)
						A__				,  A__     :							List[str]   				=model.ctx_encoder, '''ctx_model.'''
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						A__     :							Optional[int]   				={'''bert_model.embeddings.position_ids''': model.ctx_encoder.bert_model.embeddings.position_ids}
						for key, value in saved_state.model_dict.items():
										if key.startswith(__UpperCamelCase							):
														A__     :							int   				=key[len(__UpperCamelCase							) :]
														if not key.startswith('''encode_proj.'''							):
																		A__     :							Any   				='''bert_model.''' + key
														A__     :							List[str]   				=value
						encoder.load_state_dict(__UpperCamelCase							)
						return model







class 			a      (      UpperCamelCase_   ):






		def       lowerCAmelCase_  (  self							)->			Dict:
						'''simple docstring'''


						A__     :							Union[str, Any]   				=DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''							)[0]							)							)
						print(F'Loading DPR biencoder from {self.src_file}'							)
						A__     :							List[str]   				=load_states_from_checkpoint(self.src_file							)
						A__				,  A__     :							Optional[int]   				=model.question_encoder, '''question_model.'''
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						A__     :							Optional[Any]   				={'''bert_model.embeddings.position_ids''': model.question_encoder.bert_model.embeddings.position_ids}
						for key, value in saved_state.model_dict.items():
										if key.startswith(__UpperCamelCase							):
														A__     :							List[Any]   				=key[len(__UpperCamelCase							) :]
														if not key.startswith('''encode_proj.'''							):
																		A__     :							Tuple   				='''bert_model.''' + key
														A__     :							str   				=value
						encoder.load_state_dict(__UpperCamelCase							)
						return model







class 			a      (      UpperCamelCase_   ):






		def       lowerCAmelCase_  (  self							)->			int:
						'''simple docstring'''


						A__     :							Optional[int]   				=DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''							)[0]							)							)
						print(F'Loading DPR reader from {self.src_file}'							)
						A__     :							Dict   				=load_states_from_checkpoint(self.src_file							)
						# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
						A__     :							Optional[Any]   				={
						    '''encoder.bert_model.embeddings.position_ids''': model.span_predictor.encoder.bert_model.embeddings.position_ids
						}
						for key, value in saved_state.model_dict.items():
										if key.startswith('''encoder.'''							) and not key.startswith('''encoder.encode_proj'''							):
														A__     :							List[Any]   				='''encoder.bert_model.''' + key[len('''encoder.'''							) :]
										A__     :							Optional[int]   				=value
						model.span_predictor.load_state_dict(__UpperCamelCase							)
						return model







def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							Optional[int]:
				A__     :							Optional[int]   				=Path(snake_case_	)
				dest_dir.mkdir(exist_ok=snake_case_	)

				A__     :							Tuple   				=DPRState.from_type(snake_case_,	src_file=snake_case_	)
				A__     :							str   				=dpr_state.load_dpr_model()
				model.save_pretrained(snake_case_	)
				model.from_pretrained(snake_case_	)  # sanity check


if __name__ == "__main__":
						__lowerCamelCase       :       Optional[Any]				   =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--type", type=str, help="Type of the component to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
						)
						parser.add_argument(
						    "--src",
						    type=str,
						    help=(
						        "Path to the dpr checkpoint file. They can be downloaded from the official DPR repo"
						        " https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the"
						        " 'retriever' checkpoints."
						    ),
						)
						parser.add_argument("--dest", type=str, default=None, help="Path to the output PyTorch model directory.")
						__lowerCamelCase       :       Tuple				   =			parser.parse_args()

						__lowerCamelCase       :       Optional[Any]				   =			Path(args.src)
						__lowerCamelCase       :       Optional[int]				   =			F"converted-{src_file.name}" if args.dest is None else args.dest
						__lowerCamelCase       :       Optional[Any]				   =			Path(dest_dir)
						assert src_file.exists()
						assert (
						    args.type is not None
						), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
						convert(args.type, src_file, dest_dir)



