def   __A		(			_A					):



 """simple docstring"""


 if not isinstance(_A							,				_A					) or number < 0:
  raise ValueError("Input must be a non-negative integer"					)

 __a       =  0
 while number:
  # This way we arrive at next set bit (next 1) instead of looping
  # through each bit and checking for 1s hence the
  # loop won't run 32 times it will only run the number of `1` times
  number &= number - 1
  count += 1
 return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()



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


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

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



def   __A		(			_A					):



 """simple docstring"""


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

  if name != "head.weight":
   __a       =  "rwkv." + name

  __a       =  weight
 return state_dict



def   __A		(			_A							,				_A							,				_A							,				_A=None							,				_A=None							,				_A=False							,				_A=None					):



 """simple docstring"""


 if tokenizer_file is None:
  print("No `--tokenizer_file` provided, we will use the default tokenizer."					)
  __a       =  5_0277
  __a       =  AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"					)
 else:
  __a       =  PreTrainedTokenizerFast(tokenizer_file=_A					)
  __a       =  len(_A					)
 tokenizer.save_pretrained(_A					)

 # 2. Build the config
 __a       =  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:
    __a       =  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}."""					)

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

 # 3. Download model file then convert state_dict
 __a       =  hf_hub_download(_A							,				_A					)
 __a       =  torch.load(_A							,				map_location="cpu"					)
 __a       =  convert_state_dict(_A					)

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

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

  # 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."					)
  __a       =  list(shards.keys()					)

  del state_dict
  del shards
  gc.collect()

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

 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."					)
  __a       =  AutoModelForCausalLM.from_pretrained(_A					)
  model.push_to_hub(_A							,				max_shard_size="2GB"					)
  tokenizer.push_to_hub(_A					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	int							     =		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.""",
  )

  SCREAMING_SNAKE_CASE :	Tuple							     =		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,
  )


