






'''simple docstring'''

import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
SCREAMING_SNAKE_CASE__											=     {
    '/attention/': '/0/SelfAttention/',
    '/self_attention/': '/0/SelfAttention/',
    '/encoder_decoder_attention/': '/1/EncDecAttention/',
    'value': 'v',
    'query': 'q',
    'key': 'k',
    'out': 'o',
    'pre_self_attention_layer_norm': '0/layer_norm',
    'pre_cross_attention_layer_norm': '1/layer_norm',
    'pre_attention_layer_norm': '0/layer_norm',  # previously 1, but seems wrong
    'token_embedder': 'shared',
    'encoder_norm': 'final_layer_norm',
    'decoder_norm': 'final_layer_norm',
    'relpos_bias/rel_embedding': 'block/0/layer/0/SelfAttention/relative_attention_bias/weight',
    'router/router_weights/w/': 'router/classifier/',
    'roer/roer_weights/w/': 'router/classifier/',
    'logits_dense': 'lm_head',
}







def 					lowercase__				(   __UpperCamelCase					)->				Any:
      # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
      # the original model
      UpperCamelCase       						=      list(s_dict.keys()					)
      for key in keys:
            UpperCamelCase       						=      R""".*/layers_(\d+)"""
            UpperCamelCase       						=      key
            if re.match(lowerCAmelCase__	,		lowerCAmelCase__					):
                  UpperCamelCase       						=      re.sub(R"""layers_(\d+)"""	,		R"""block/\1/layer"""	,		lowerCAmelCase__					)

            UpperCamelCase       						=      R"""(encoder|decoder)\/"""

            if re.match(lowerCAmelCase__	,		lowerCAmelCase__					):
                  UpperCamelCase       						=      re.match(lowerCAmelCase__	,		lowerCAmelCase__					).groups()
                  if groups[0] == "encoder":
                        UpperCamelCase       						=      re.sub(R"""/mlp/"""	,		R"""/1/mlp/"""	,		lowerCAmelCase__					)
                        UpperCamelCase       						=      re.sub(R"""/pre_mlp_layer_norm/"""	,		R"""/1/layer_norm/"""	,		lowerCAmelCase__					)

                  elif groups[0] == "decoder":
                        UpperCamelCase       						=      re.sub(R"""/mlp/"""	,		R"""/2/mlp/"""	,		lowerCAmelCase__					)
                        UpperCamelCase       						=      re.sub(R"""/pre_mlp_layer_norm/"""	,		R"""/2/layer_norm/"""	,		lowerCAmelCase__					)

        # 2. Convert other classic mappings
            for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
                  if old_key in new_key:
                        UpperCamelCase       						=      new_key.replace(lowerCAmelCase__	,		lowerCAmelCase__					)

            print(F"{key} -> {new_key}"					)
            UpperCamelCase       						=      s_dict.pop(lowerCAmelCase__					)

      if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
            UpperCamelCase       						=      s_dict[
                """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
            ].T
      if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
            UpperCamelCase       						=      s_dict[
                """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
            ].T

      # 3. Take extra care of the EXPERTS layer
      for key in list(s_dict.keys()					):
            if "expert" in key:
                  UpperCamelCase       						=      s_dict[key].shape[0]
                  UpperCamelCase       						=      s_dict[key]
                  for idx in range(lowerCAmelCase__					):
                        UpperCamelCase       						=      expert_weihts[idx]
                        print(F"{key} -> {key.replace('expert/'	,		'nested fstring'					)}"					)

                  s_dict.pop(lowerCAmelCase__					)

      return s_dict


SCREAMING_SNAKE_CASE__											=     {
    'NUM_ENCODER_LAYERS': 'num_layers',
    'NUM_DECODER_LAYERS': 'num_decoder_layers',
    'NUM_HEADS': 'num_heads',
    'HEAD_DIM': 'd_kv',
    'EMBED_DIM': 'd_model',
    'MLP_DIM': 'd_ff',
    'NUM_SELECTED_EXPERTS': 'num_selected_experts',
    'NUM_ENCODER_SPARSE_LAYERS': 'num_sparse_encoder_layers',
    'NUM_DECODER_SPARSE_LAYERS': 'num_sparse_decoder_layers',
    'dense.MlpBlock.activations': 'feed_forward_proj',
}







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Optional[int]:
      # Convert a google style config to the hugging face fromat
      import regex as re

      with open(lowerCAmelCase__	,		"""r"""					) as f:
            UpperCamelCase       						=      f.read()

      UpperCamelCase       						=      re.findall(R"""(.*) = ([0-9.]*)"""	,		lowerCAmelCase__					)
      UpperCamelCase       						=      {}
      for param, value in regex_match:
            if param in GIN_TO_CONFIG_MAPPING and value != "":
                  UpperCamelCase       						=      float(lowerCAmelCase__					) if """.""" in value else int(lowerCAmelCase__					)

      UpperCamelCase       						=      re.findall(R"""(.*activations) = \(\'(.*)\',\)"""	,		lowerCAmelCase__					)[0]
      UpperCamelCase       						=      str(activation[1]					)

      UpperCamelCase       						=      num_experts
      UpperCamelCase       						=      SwitchTransformersConfig(**lowerCAmelCase__					)
      return config







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase="./"	,		__UpperCamelCase=8					)->				Optional[Any]:
      # Initialise PyTorch model

      print(F"Loading flax weights from : {flax_checkpoint_path}"					)
      UpperCamelCase       						=      checkpoints.load_tax_checkpoint(lowerCAmelCase__					)

      if gin_file is not None:
            UpperCamelCase       						=      convert_gin_to_config(lowerCAmelCase__	,		lowerCAmelCase__					)
      else:
            UpperCamelCase       						=      SwitchTransformersConfig.from_pretrained(lowerCAmelCase__					)

      UpperCamelCase       						=      SwitchTransformersForConditionalGeneration(lowerCAmelCase__					)

      UpperCamelCase       						=      flax_params["""target"""]
      UpperCamelCase       						=      flatten_dict(lowerCAmelCase__	,		sep="""/"""					)
      UpperCamelCase       						=      rename_keys(lowerCAmelCase__					)
      UpperCamelCase       						=      unflatten_dict(lowerCAmelCase__	,		sep="""/"""					)

      # Load the flax params in the PT model
      load_flax_weights_in_pytorch_model(lowerCAmelCase__	,		lowerCAmelCase__					)

      print(F"Save PyTorch model to {pytorch_dump_path}"					)
      pt_model.save_pretrained(lowerCAmelCase__					)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--switch_t5x_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'
             ' model architecture. If not provided, a `gin_file` has to be provided.'
         ),
     )
     parser.add_argument(
         '--gin_file',
         default=None,
         type=str,
         required=False,
         help='Path to the gin config file. If not provided, a `config_file` has to be passed   ',
     )
     parser.add_argument(
         '--config_name', default=None, type=str, required=False, help='Config name of SwitchTransformers model.'
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output pytorch model.'
     )
     parser.add_argument('--num_experts', default=8, type=int, required=False, help='Number of experts')
     SCREAMING_SNAKE_CASE__											=     parser.parse_args()
     convert_flax_checkpoint_to_pytorch(
         args.switch_tax_checkpoint_path,
         args.config_name,
         args.gin_file,
         args.pytorch_dump_folder_path,
         args.num_experts,
     )








'''simple docstring'''

from math import sqrt







def 					lowercase__				(   __UpperCamelCase					)->				int:
      UpperCamelCase       						=      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 					lowercase__				(   __UpperCamelCase = 10000					)->				int:
      UpperCamelCase       						=      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())))
