import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch




def        lowerCAmelCase_			(					__A							)					->    Union[str, Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  os.path.join(args.tf_model_dir,  "parameters.json"							)
      UpperCAmelCase__								=  json.loads(open(a__							).read()							)
      if not params:
            raise ValueError(
                f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""							)
      if not args.output.endswith(".pt"							):
            UpperCAmelCase__								=  args.output + ".pt"
      UpperCAmelCase__								=  OrderedDict()
      with tf.device("/CPU:0"							):
            UpperCAmelCase__								=  tf.train.load_checkpoint(args.tf_model_dir							)
            UpperCAmelCase__								=  reader.get_variable_to_shape_map()
            for key_name in shapes.keys():
                  UpperCAmelCase__								=  reader.get_tensor(a__							).astype(np.floataa							)
                  if key_name.endswith("/adam_m"							) or key_name.endswith("/adam_v"							):
                        continue
                  if key_name.startswith("pasts/"							):
                        if key_name.startswith("pasts/mlp"							):
                              UpperCAmelCase__								=  int(key_name[9]							)
                        elif key_name.startswith("pasts/out"							):
                              UpperCAmelCase__								=  8
                        UpperCAmelCase__								=  "model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                        UpperCAmelCase__								=  vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
                        UpperCAmelCase__								=  torch.tensor(a__							)
                  elif key_name.startswith("model/moe"							):
                        UpperCAmelCase__								=  int(key_name[9:].split("/"							)[0]							)
                        if key_name.endswith("/switch_gating/kernel"							):
                              UpperCAmelCase__								=  "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
                              UpperCAmelCase__								=  vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase__								=  torch.tensor(a__							)
                        elif key_name.endswith("/softmlp/kernel"							):
                              UpperCAmelCase__								=  "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
                              UpperCAmelCase__								=  vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase__								=  torch.tensor(a__							)
                        elif key_name.endswith("/wo/kernel"							) or key_name.endswith("/wi/kernel"							):
                              UpperCAmelCase__								=  key_name[-9:-7]
                              for i in range(16							):
                                    UpperCAmelCase__								=  "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
                                    UpperCAmelCase__								=  (
                                        vnp[i].transpose([1, 0]							).copy()
                                    )  # In Mesh-Tensorflow, it is one array, so it is divided
                                    UpperCAmelCase__								=  torch.tensor(a__							)
                  elif key_name.startswith("model/mlp"							):
                        UpperCAmelCase__								=  int(key_name[9:].split("/"							)[0]							)
                        if key_name.endswith("/p1/kernel"							):
                              UpperCAmelCase__								=  "model.blocks.%d.feed_forward.mlp.wi.weight" % player
                              UpperCAmelCase__								=  vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase__								=  torch.tensor(a__							)
                        elif key_name.endswith("/p1/bias"							):
                              UpperCAmelCase__								=  "model.blocks.%d.feed_forward.mlp.wi.bias" % player
                              UpperCAmelCase__								=  vnp.copy()  # same because it is one dimensional
                              UpperCAmelCase__								=  torch.tensor(a__							)
                        elif key_name.endswith("/p2/kernel"							):
                              UpperCAmelCase__								=  "model.blocks.%d.feed_forward.mlp.wo.weight" % player
                              UpperCAmelCase__								=  vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase__								=  torch.tensor(a__							)
                        elif key_name.endswith("/p2/bias"							):
                              UpperCAmelCase__								=  "model.blocks.%d.feed_forward.mlp.wo.bias" % player
                              UpperCAmelCase__								=  vnp.copy()  # same because it is one dimensional
                              UpperCAmelCase__								=  torch.tensor(a__							)
                  elif key_name.startswith("model/ln"							):
                        UpperCAmelCase__								=  int(key_name[8:].split("/"							)[0]							)
                        if key_name.endswith("/b"							):
                              UpperCAmelCase__								=  "model.blocks.%d.feed_forward.norm.bias" % player
                              UpperCAmelCase__								=  vnp.copy()  # same because it is one dimensional
                              UpperCAmelCase__								=  torch.tensor(a__							)
                        elif key_name.endswith("/g"							):
                              UpperCAmelCase__								=  "model.blocks.%d.feed_forward.norm.weight" % player
                              UpperCAmelCase__								=  vnp.copy()  # same because it is one dimensional
                              UpperCAmelCase__								=  torch.tensor(a__							)
                  elif key_name.startswith("model/att"							):
                        UpperCAmelCase__								=  int(key_name[9:].split("/"							)[0]							)
                        if key_name.endswith("/qkv/kernel"							):
                              UpperCAmelCase__								=  vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                              UpperCAmelCase__								=  state[:, 0, :, :]
                              UpperCAmelCase__								=  state[:, 1, :, :]
                              UpperCAmelCase__								=  state[:, 2, :, :]
                              UpperCAmelCase__								=  (
                                  state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]							)
                                  .transpose([1, 0]							)
                                  .copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase__								=  (
                                  state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]							)
                                  .transpose([1, 0]							)
                                  .copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase__								=  (
                                  state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]							)
                                  .transpose([1, 0]							)
                                  .copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase__								=  "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
                              UpperCAmelCase__								=  torch.tensor(a__							)
                              UpperCAmelCase__								=  "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
                              UpperCAmelCase__								=  torch.tensor(a__							)
                              UpperCAmelCase__								=  "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
                              UpperCAmelCase__								=  torch.tensor(a__							)
                        elif key_name.endswith("/o/kernel"							):
                              UpperCAmelCase__								=  "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
                              UpperCAmelCase__								=  (
                                  vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]							).transpose([1, 0]							).copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              UpperCAmelCase__								=  torch.tensor(a__							)
                  elif key_name.startswith("model/an"							):
                        UpperCAmelCase__								=  int(key_name[8:].split("/"							)[0]							)
                        if key_name.endswith("/b"							):
                              UpperCAmelCase__								=  "model.blocks.%d.self_attn.norm.bias" % player
                              UpperCAmelCase__								=  vnp.copy()  # same because it is one dimensional
                              UpperCAmelCase__								=  torch.tensor(a__							)
                        elif key_name.endswith("/g"							):
                              UpperCAmelCase__								=  "model.blocks.%d.self_attn.norm.weight" % player
                              UpperCAmelCase__								=  vnp.copy()  # same because it is one dimensional
                              UpperCAmelCase__								=  torch.tensor(a__							)
                  elif (
                      key_name.startswith("model/wte"							)
                      or key_name.startswith("model/wpe"							)
                      or key_name.startswith("model/ete"							)
                  ):
                        UpperCAmelCase__								=  {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
                            key_name[-3:]
                        ]
                        UpperCAmelCase__								=  "model.%s.weight" % nlayer
                        UpperCAmelCase__								=  vnp.copy()  # same in embedded
                        UpperCAmelCase__								=  torch.tensor(a__							)
                        if key_name.startswith("model/wte"							):
                              UpperCAmelCase__								=  "lm_head.weight"
                              UpperCAmelCase__								=  vnp.copy()  # same in embedded
                              UpperCAmelCase__								=  torch.tensor(a__							)
                  elif key_name.startswith("model/wob"							):
                        UpperCAmelCase__								=  "final_logits_bias"
                        UpperCAmelCase__								=  vnp.copy()  # same in embedded
                        UpperCAmelCase__								=  state.reshape((1, -1)							)
                        UpperCAmelCase__								=  torch.tensor(a__							)
                  elif key_name == "model/dense/kernel":
                        UpperCAmelCase__								=  "model.last_project.weight"
                        UpperCAmelCase__								=  vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
                        UpperCAmelCase__								=  torch.tensor(a__							)
                  elif key_name == "model/dense_1/bias":
                        UpperCAmelCase__								=  "model.last_project.bias"
                        UpperCAmelCase__								=  vnp.copy()  # same because it is one dimensional
                        UpperCAmelCase__								=  torch.tensor(a__							)
      torch.save(a__,  args.output							)


if __name__ == "__main__":
      UpperCamelCase__    							= argparse.ArgumentParser(
          description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
      )
      parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
      parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
      UpperCamelCase__    							= parser.parse_args()
      convert_tf_gptsan_to_pt(args)
from __future__ import annotations




def        lowerCAmelCase_			(					__A							)					->    bool:







      '''simple docstring'''


      UpperCAmelCase__								=  str(__A							)
      return n == n[::-1]




def        lowerCAmelCase_			(					__A = 1_000_000							)					->    Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__								=  0

      for i in range(1,  __A							):
            if is_palindrome(__A							) and is_palindrome(bin(__A							).split("b"							)[1]							):
                  total += i
      return total


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



