

'''simple docstring'''

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


lowercase__			    =			{
    "169M": 12,
    "430M": 24,
    "1B5": 24,
    "3B": 32,
    "7B": 32,
    "14B": 40,
}

lowercase__			    =			{
    "169M": 768,
    "430M": 1024,
    "1B5": 2048,
    "3B": 2560,
    "7B": 4096,
    "14B": 5120,
}




def       UpperCamelCase(     UpperCAmelCase_	):
 UpperCAmelCase       :	Optional[Any]											= list(state_dict.keys()	)
 for name in state_dict_keys:
  UpperCAmelCase       :	str											= state_dict.pop(UpperCAmelCase_	)
  # emb -> embedding
  if name.startswith('emb.'	):
   UpperCAmelCase       :	int											= name.replace('emb.'  ,       'embeddings.'	)
  # ln_0 -> pre_ln (only present at block 0)
  if name.startswith('blocks.0.ln0'	):
   UpperCAmelCase       :	int											= name.replace('blocks.0.ln0'  ,       'blocks.0.pre_ln'	)
  # att -> attention
  UpperCAmelCase       :	int											= re.sub(R'blocks\.(\d+)\.att'  ,       R'blocks.\1.attention'  ,       UpperCAmelCase_	)
  # ffn -> feed_forward
  UpperCAmelCase       :	Union[str, Any]											= re.sub(R'blocks\.(\d+)\.ffn'  ,       R'blocks.\1.feed_forward'  ,       UpperCAmelCase_	)
  # time_mix_k -> time_mix_key and reshape
  if name.endswith('.time_mix_k'	):
   UpperCAmelCase       :	Union[str, Any]											= name.replace('.time_mix_k'  ,       '.time_mix_key'	)
  # time_mix_v -> time_mix_value and reshape
  if name.endswith('.time_mix_v'	):
   UpperCAmelCase       :	Any											= name.replace('.time_mix_v'  ,       '.time_mix_value'	)
  # time_mix_r -> time_mix_key and reshape
  if name.endswith('.time_mix_r'	):
   UpperCAmelCase       :	List[str]											= name.replace('.time_mix_r'  ,       '.time_mix_receptance'	)

  if name != "head.weight":
   UpperCAmelCase       :	int											= "rwkv." + name

  UpperCAmelCase       :	Optional[Any]											= weight
 return state_dict




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_=None  ,       UpperCAmelCase_=None  ,       UpperCAmelCase_=False  ,       UpperCAmelCase_=None	):
 # 1. If possible, build the tokenizer.
 if tokenizer_file is None:
  print('No `--tokenizer_file` provided, we will use the default tokenizer.'	)
  UpperCAmelCase       :	Optional[int]											= 5_02_77
  UpperCAmelCase       :	Any											= AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'	)
 else:
  UpperCAmelCase       :	List[str]											= PreTrainedTokenizerFast(tokenizer_file=UpperCAmelCase_	)
  UpperCAmelCase       :	Tuple											= len(UpperCAmelCase_	)
 tokenizer.save_pretrained(UpperCAmelCase_	)

 # 2. Build the config
 UpperCAmelCase       :	int											= 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       :	str											= 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       :	Dict											= RwkvConfig(
     vocab_size=UpperCAmelCase_  ,       num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]  ,       hidden_size=HIDEN_SIZE_MAPPING[size]  ,       )
 config.save_pretrained(UpperCAmelCase_	)

 # 3. Download model file then convert state_dict
 UpperCAmelCase       :	Optional[Any]											= hf_hub_download(UpperCAmelCase_  ,       UpperCAmelCase_	)
 UpperCAmelCase       :	List[Any]											= torch.load(UpperCAmelCase_  ,       map_location='cpu'	)
 UpperCAmelCase       :	Optional[Any]											= convert_state_dict(UpperCAmelCase_	)

 # 4. Split in shards and save
 UpperCAmelCase       :	Optional[int]											= shard_checkpoint(UpperCAmelCase_	)
 for shard_file, shard in shards.items():
  torch.save(UpperCAmelCase_  ,       os.path.join(UpperCAmelCase_  ,       UpperCAmelCase_	)	)

 if index is not None:
  UpperCAmelCase       :	List[Any]											= os.path.join(UpperCAmelCase_  ,       UpperCAmelCase_	)
  # Save the index as well
  with open(UpperCAmelCase_  ,       'w'  ,       encoding='utf-8'	) as f:
   UpperCAmelCase       :	Tuple											= json.dumps(UpperCAmelCase_  ,       indent=2  ,       sort_keys=UpperCAmelCase_	) + "\n"
   f.write(UpperCAmelCase_	)

  # 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       :	Optional[int]											= list(shards.keys()	)

  del state_dict
  del shards
  gc.collect()

  for shard_file in shard_files:
   UpperCAmelCase       :	List[Any]											= torch.load(os.path.join(UpperCAmelCase_  ,       UpperCAmelCase_	)	)
   torch.save({k: v.cpu().clone() for k, v in state_dict.items()}  ,       os.path.join(UpperCAmelCase_  ,       UpperCAmelCase_	)	)

 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       :	str											= AutoModelForCausalLM.from_pretrained(UpperCAmelCase_	)
  model.push_to_hub(UpperCAmelCase_  ,       max_shard_size='2GB'	)
  tokenizer.push_to_hub(UpperCAmelCase_	)


if __name__ == "__main__":
 lowercase__			    =			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.",
 )

 lowercase__			    =			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,
 )










_UpperCAmelCase		:  Tuple	=   {str(digit): digit**5 for digit in range(10)}



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCamelCase      )      )



def 					UpperCAmelCase__     (   ):
  return sum(
      number
      for number in range(1000,	1000000      )
      if number == digits_fifth_powers_sum(lowerCamelCase      )      )


if __name__ == "__main__":
     print(solution())



