



'''simple docstring'''





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}



class 						UpperCAmelCase_					( lowercase__):
							lowerCamelCase__         =	'sew-d'







							def __init__(     self,    __a=32,    __a=768,    __a=12,    __a=12,    __a=3072,    __a=2,    __a=512,    __a=256,    __a=True,    __a=True,    __a=("p2c", "c2p"),    __a="layer_norm",    __a="gelu_python",    __a=0.1,    __a=0.1,    __a=0.1,    __a=0.0,    __a=0.1,    __a=0.02,    __a=1E-7,    __a=1E-5,    __a="group",    __a="gelu",    __a=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512),    __a=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1),    __a=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1),    __a=False,    __a=128,    __a=16,    __a=True,    __a=0.05,    __a=10,    __a=2,    __a=0.0,    __a=10,    __a=0,    __a="mean",    __a=False,    __a=False,    __a=256,    __a=0,    __a=1,    __a=2,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__lowerCamelCase,    pad_token_id=__lowerCamelCase,    bos_token_id=__lowerCamelCase,    eos_token_id=__lowerCamelCase)
														_lowerCAmelCase  :							Any				     =					hidden_size
														_lowerCAmelCase  :							Union[str, Any]				     =					feat_extract_norm
														_lowerCAmelCase  :							List[str]				     =					feat_extract_activation
														_lowerCAmelCase  :							Any				     =					list(__lowerCamelCase)
														_lowerCAmelCase  :							Union[str, Any]				     =					list(__lowerCamelCase)
														_lowerCAmelCase  :							int				     =					list(__lowerCamelCase)
														_lowerCAmelCase  :							List[str]				     =					conv_bias
														_lowerCAmelCase  :							Union[str, Any]				     =					num_conv_pos_embeddings
														_lowerCAmelCase  :							int				     =					num_conv_pos_embedding_groups
														_lowerCAmelCase  :							Union[str, Any]				     =					len(self.conv_dim)
														_lowerCAmelCase  :							Optional[Any]				     =					num_hidden_layers
														_lowerCAmelCase  :							Any				     =					intermediate_size
														_lowerCAmelCase  :							Optional[int]				     =					squeeze_factor
														_lowerCAmelCase  :							List[str]				     =					max_position_embeddings
														_lowerCAmelCase  :							List[str]				     =					position_buckets
														_lowerCAmelCase  :							List[str]				     =					share_att_key
														_lowerCAmelCase  :							int				     =					relative_attention
														_lowerCAmelCase  :							Dict				     =					norm_rel_ebd
														_lowerCAmelCase  :							Optional[int]				     =					list(__lowerCamelCase)
														_lowerCAmelCase  :							Any				     =					hidden_act
														_lowerCAmelCase  :							List[str]				     =					num_attention_heads
														_lowerCAmelCase  :							Any				     =					hidden_dropout
														_lowerCAmelCase  :							List[Any]				     =					attention_dropout
														_lowerCAmelCase  :							Any				     =					activation_dropout
														_lowerCAmelCase  :							List[str]				     =					feat_proj_dropout
														_lowerCAmelCase  :							int				     =					final_dropout
														_lowerCAmelCase  :							Tuple				     =					layer_norm_eps
														_lowerCAmelCase  :							List[Any]				     =					feature_layer_norm_eps
														_lowerCAmelCase  :							List[str]				     =					initializer_range
														_lowerCAmelCase  :							List[str]				     =					vocab_size

														if (
														    (len(self.conv_stride) != self.num_feat_extract_layers)
														    or (len(self.conv_kernel) != self.num_feat_extract_layers)
														    or (len(self.conv_dim) != self.num_feat_extract_layers)
														):
																					raise ValueError(
																					    "Configuration for convolutional layers is incorrect."
																					    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
																					    f"but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)"
																					    f"= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.")

														# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
														_lowerCAmelCase  :							List[str]				     =					apply_spec_augment
														_lowerCAmelCase  :							Optional[int]				     =					mask_time_prob
														_lowerCAmelCase  :							Optional[Any]				     =					mask_time_length
														_lowerCAmelCase  :							Union[str, Any]				     =					mask_time_min_masks
														_lowerCAmelCase  :							List[Any]				     =					mask_feature_prob
														_lowerCAmelCase  :							Optional[Any]				     =					mask_feature_length
														_lowerCAmelCase  :							Any				     =					mask_feature_min_masks

														# ctc loss
														_lowerCAmelCase  :							Dict				     =					ctc_loss_reduction
														_lowerCAmelCase  :							List[Any]				     =					ctc_zero_infinity

														# sequence classification
														_lowerCAmelCase  :							Optional[Any]				     =					use_weighted_layer_sum
														_lowerCAmelCase  :							Union[str, Any]				     =					classifier_proj_size







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return functools.reduce(operator.mul,    self.conv_stride,    1)





def        A       (      _lowerCamelCase = 1_000_000						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					1
							_lowerCAmelCase  :							Optional[Any]				     =					1
							_lowerCAmelCase  :							List[str]				     =					{1: 1}

							for inputa in range(2       ,				_lowerCamelCase						):
														_lowerCAmelCase  :							int				     =					0
														_lowerCAmelCase  :							Any				     =					inputa

														while True:
																					if number in counters:
																												counter += counters[number]
																												break
																					if number % 2 == 0:
																												number //= 2
																												counter += 1
																					else:
																												_lowerCAmelCase  :							Any				     =					(3 * number) + 1
																												counter += 1

														if inputa not in counters:
																					_lowerCAmelCase  :							Tuple				     =					counter

														if counter > pre_counter:
																					_lowerCAmelCase  :							Union[str, Any]				     =					inputa
																					_lowerCAmelCase  :							Union[str, Any]				     =					counter
							return largest_number


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