




import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
__A        =    logging.get_logger(__name__)

__A        =    {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
__A        =    [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 			lowerCamelCase_		(       UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      Union[str, Any]      )     ->       List[Any]:





				"""simple docstring"""


				for attribute in key.split('.'      ):
								__lowerCamelCase		       =    getattr(UpperCamelCase__ ,     UpperCamelCase__      )

				if weight_type is not None:
								__lowerCamelCase		       =    getattr(UpperCamelCase__ ,     UpperCamelCase__      ).shape
				else:
								__lowerCamelCase		       =    hf_pointer.shape

				assert hf_shape == value.shape, (
				    F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
				    F""" {value.shape} for {full_name}"""
				)

				if weight_type == "weight":
								__lowerCamelCase		       =    value
				elif weight_type == "weight_g":
								__lowerCamelCase		       =    value
				elif weight_type == "weight_v":
								__lowerCamelCase		       =    value
				elif weight_type == "bias":
								__lowerCamelCase		       =    value
				else:
								__lowerCamelCase		       =    value

				logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      Tuple      )     ->       Tuple:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				__lowerCamelCase		       =    fairseq_model.state_dict()

				__lowerCamelCase		       =    hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								__lowerCamelCase		       =    False
								if "conv_layers" in name:
												load_conv_layer(
												    UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     hf_model.config.feat_extract_norm == 'group' ,     )
												__lowerCamelCase		       =    True
								else:
												for key, mapped_key in MAPPING.items():
																if key in name or key.split('w2v_model.'      )[-1] == name.split('.'      )[0]:
																				__lowerCamelCase		       =    True
																				if "*" in mapped_key:
																								__lowerCamelCase		       =    name.split(UpperCamelCase__      )[0].split('.'      )[-2]
																								__lowerCamelCase		       =    mapped_key.replace('*' ,     UpperCamelCase__      )
																				if "weight_g" in name:
																								__lowerCamelCase		       =    'weight_g'
																				elif "weight_v" in name:
																								__lowerCamelCase		       =    'weight_v'
																				elif "bias" in name and "relative_attention_bias" not in name:
																								__lowerCamelCase		       =    'bias'
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								__lowerCamelCase		       =    'weight'
																				else:
																								__lowerCamelCase		       =    None

																				set_recursively(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
																continue
								if not is_used:
												unused_weights.append(UpperCamelCase__      )

				logger.warning(F"""Unused weights: {unused_weights}"""      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      List[str]      )     ->       List[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    full_name.split('conv_layers.'      )[-1]
				__lowerCamelCase		       =    name.split('.'      )
				__lowerCamelCase		       =    int(items[0]      )
				__lowerCamelCase		       =    int(items[1]      )

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
												)
												__lowerCamelCase		       =    value
												logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
												)
												__lowerCamelCase		       =    value
												logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
												    " found."
												)
												__lowerCamelCase		       =    value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
												)
												__lowerCamelCase		       =    value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
				else:
								unused_weights.append(UpperCamelCase__      )







@torch.no_grad()
def 			lowerCamelCase_		(       UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Optional[int]=None      )     ->       Any:





				"""simple docstring"""


				__lowerCamelCase		       =    torch.load(UpperCamelCase__      )
				__lowerCamelCase		       =    WavLMConfigOrig(checkpoint['cfg']      )
				__lowerCamelCase		       =    WavLMOrig(UpperCamelCase__      )
				model.load_state_dict(checkpoint['model']      )
				model.eval()

				if config_path is not None:
								__lowerCamelCase		       =    WavLMConfig.from_pretrained(UpperCamelCase__      )
				else:
								__lowerCamelCase		       =    WavLMConfig()

				__lowerCamelCase		       =    WavLMModel(UpperCamelCase__      )

				recursively_load_weights(UpperCamelCase__ ,     UpperCamelCase__      )

				hf_wavlm.save_pretrained(UpperCamelCase__      )


if __name__ == "__main__":
					__A        =    argparse.ArgumentParser()
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
					parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
					__A        =    parser.parse_args()
					convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





"""simple docstring"""





from math import sqrt




def   _A  (  UpperCamelCase_     :       int)	->						int:
   '''simple docstring'''


   __lowercase								=						0
   for i in range(1,			int(sqrt(UpperCamelCase_) + 1)):
      if n % i == 0 and i != sqrt(UpperCamelCase_):
         total += i + n // i
      elif i == sqrt(UpperCamelCase_):
         total += i
   return total - n




def   _A  (  UpperCamelCase_     :       int = 10000)	->						int:
   '''simple docstring'''


   __lowercase								=						sum(
       i
       for i in range(1,			UpperCamelCase_)
       if sum_of_divisors(sum_of_divisors(UpperCamelCase_)) == i and sum_of_divisors(UpperCamelCase_) != i)
   return total


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




