






import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


snake_case__      :	List[Any]       	=      {
    """169M""": 1_2,
    """430M""": 2_4,
    """1B5""": 2_4,
    """3B""": 3_2,
    """7B""": 3_2,
    """14B""": 4_0,
}

snake_case__      :	Tuple       	=      {
    """169M""": 7_6_8,
    """430M""": 1_0_2_4,
    """1B5""": 2_0_4_8,
    """3B""": 2_5_6_0,
    """7B""": 4_0_9_6,
    """14B""": 5_1_2_0,
}






def     _snake_case    (__lowercase):
   UpperCamelCase_									=     list(state_dict.keys())
   for name in state_dict_keys:
      UpperCamelCase_									=     state_dict.pop(__lowercase)
      # emb -> embedding
      if name.startswith('emb.'):
         UpperCamelCase_									=     name.replace('emb.'    ,      'embeddings.')
      # ln_0 -> pre_ln (only present at block 0)
      if name.startswith('blocks.0.ln0'):
         UpperCamelCase_									=     name.replace('blocks.0.ln0'    ,      'blocks.0.pre_ln')
      # att -> attention
      UpperCamelCase_									=     re.sub(r'blocks\.(\d+)\.att'    ,      r'blocks.\1.attention'    ,      __lowercase)
      # ffn -> feed_forward
      UpperCamelCase_									=     re.sub(r'blocks\.(\d+)\.ffn'    ,      r'blocks.\1.feed_forward'    ,      __lowercase)
      # time_mix_k -> time_mix_key and reshape
      if name.endswith('.time_mix_k'):
         UpperCamelCase_									=     name.replace('.time_mix_k'    ,      '.time_mix_key')
      # time_mix_v -> time_mix_value and reshape
      if name.endswith('.time_mix_v'):
         UpperCamelCase_									=     name.replace('.time_mix_v'    ,      '.time_mix_value')
      # time_mix_r -> time_mix_key and reshape
      if name.endswith('.time_mix_r'):
         UpperCamelCase_									=     name.replace('.time_mix_r'    ,      '.time_mix_receptance')

      if name != "head.weight":
         UpperCamelCase_									=     'rwkv.' + name

      UpperCamelCase_									=     weight
   return state_dict






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase=None    ,      __lowercase=None    ,      __lowercase=False    ,      __lowercase=None):
   # 1. If possible, build the tokenizer.
   if tokenizer_file is None:
      print('No `--tokenizer_file` provided, we will use the default tokenizer.')
      UpperCamelCase_									=     50277
      UpperCamelCase_									=     AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b')
   else:
      UpperCamelCase_									=     PreTrainedTokenizerFast(tokenizer_file=__lowercase)
      UpperCamelCase_									=     len(__lowercase)
   tokenizer.save_pretrained(__lowercase)

   # 2. Build the config
   UpperCamelCase_									=     list(NUM_HIDDEN_LAYERS_MAPPING.keys())
   if size is None:
      # Try to infer size from the checkpoint name
      for candidate in possible_sizes:
         if candidate in checkpoint_file:
            UpperCamelCase_									=     candidate
            break
      if size is None:
         raise ValueError('Could not infer the size, please provide it with the `--size` argument.')
   if size not in possible_sizes:
      raise ValueError(f"""`size` should be one of {possible_sizes}, got {size}.""")

   UpperCamelCase_									=     RwkvConfig(
       vocab_size=__lowercase    ,      num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]    ,      hidden_size=HIDEN_SIZE_MAPPING[size]    ,      )
   config.save_pretrained(__lowercase)

   # 3. Download model file then convert state_dict
   UpperCamelCase_									=     hf_hub_download(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     torch.load(__lowercase    ,      map_location='cpu')
   UpperCamelCase_									=     convert_state_dict(__lowercase)

   # 4. Split in shards and save
   UpperCamelCase_		,      UpperCamelCase_									=     shard_checkpoint(__lowercase)
   for shard_file, shard in shards.items():
      torch.save(__lowercase    ,      os.path.join(__lowercase    ,      __lowercase))

   if index is not None:
      UpperCamelCase_									=     os.path.join(__lowercase    ,      __lowercase)
      # Save the index as well
      with open(__lowercase    ,      'w'    ,      encoding='utf-8') as f:
         UpperCamelCase_									=     json.dumps(__lowercase    ,      indent=2    ,      sort_keys=__lowercase) + '\n'
         f.write(__lowercase)

      # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
      print(
          'Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.')
      UpperCamelCase_									=     list(shards.keys())

      del state_dict
      del shards
      gc.collect()

      for shard_file in shard_files:
         UpperCamelCase_									=     torch.load(os.path.join(__lowercase    ,      __lowercase))
         torch.save({k: v.cpu().clone() for k, v in state_dict.items()}    ,      os.path.join(__lowercase    ,      __lowercase))

   del state_dict
   gc.collect()

   if push_to_hub:
      if model_name is None:
         raise ValueError('Please provide a `model_name` to push the model to the Hub.')
      UpperCamelCase_									=     AutoModelForCausalLM.from_pretrained(__lowercase)
      model.push_to_hub(__lowercase    ,      max_shard_size='2GB')
      tokenizer.push_to_hub(__lowercase)


if __name__ == "__main__":
 snake_case__      :	Optional[Any]       	=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--repo_id""", default=None, type=str, required=True, help="""Repo ID from which to pull the checkpoint."""
 )
 parser.add_argument(
     """--checkpoint_file""", default=None, type=str, required=True, help="""Name of the checkpoint file in the repo."""
 )
 parser.add_argument(
     """--output_dir""", default=None, type=str, required=True, help="""Where to save the converted model."""
 )
 parser.add_argument(
     """--tokenizer_file""",
     default=None,
     type=str,
     help="""Path to the tokenizer file to use (if not provided, only the model is converted).""",
 )
 parser.add_argument(
     """--size""",
     default=None,
     type=str,
     help="""Size of the model. Will be inferred from the `checkpoint_file` if not passed.""",
 )
 parser.add_argument(
     """--push_to_hub""",
     action="""store_true""",
     help="""Push to the Hub the converted model.""",
 )
 parser.add_argument(
     """--model_name""",
     default=None,
     type=str,
     help="""Name of the pushed model on the Hub, including the username / organization.""",
 )

 snake_case__      :	Any       	=      parser.parse_args()
 convert_rmkv_checkpoint_to_hf_format(
     args.repo_id,
     args.checkpoint_file,
     args.output_dir,
     size=args.size,
     tokenizer_file=args.tokenizer_file,
     push_to_hub=args.push_to_hub,
     model_name=args.model_name,
 )













def     _snake_case    (__lowercase = 1000):
   UpperCamelCase_		,      UpperCamelCase_									=     1, 1
   UpperCamelCase_									=     2
   while True:
      UpperCamelCase_									=     0
      UpperCamelCase_									=     fa + fa
      UpperCamelCase_		,      UpperCamelCase_									=     fa, f
      index += 1
      for _ in str(__lowercase):
         i += 1
      if i == n:
         break
   return index


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





