






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

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

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

        if name != "head.weight":
            _UpperCAmelCase				:      Optional[int]			  =				'''rwkv.''' + name

        _UpperCAmelCase				:      Union[str, Any]			  =				weight
    return state_dict

def      _lowerCamelCase	(						__A    :							int		,		__A    :							List[str]		,		__A    :							int		,		__A    :							str=None		,		__A    :							Dict=None		,		__A    :							Dict=False		,		__A    :							Tuple=None )      ->					Dict:
    # 1. If possible, build the tokenizer.
    if tokenizer_file is None:
        print('''No `--tokenizer_file` provided, we will use the default tokenizer.''' )
        _UpperCAmelCase				:      int			  =				50_277
        _UpperCAmelCase				:      str			  =				AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b''' )
    else:
        _UpperCAmelCase				:      Dict			  =				PreTrainedTokenizerFast(tokenizer_file=__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				len(__A )
    tokenizer.save_pretrained(__A )

    # 2. Build the config
    _UpperCAmelCase				:      Union[str, Any]			  =				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				:      Tuple			  =				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				:      Optional[int]			  =				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
    _UpperCAmelCase				:      Any			  =				hf_hub_download(__A		,		__A )
    _UpperCAmelCase				:      str			  =				torch.load(__A		,		map_location='''cpu''' )
    _UpperCAmelCase				:      List[Any]			  =				convert_state_dict(__A )

    # 4. Split in shards and save
    _UpperCAmelCase  , _UpperCAmelCase				:      Union[str, Any]			  =				shard_checkpoint(__A )
    for shard_file, shard in shards.items():
        torch.save(__A		,		os.path.join(__A		,		__A ) )

    if index is not None:
        _UpperCAmelCase				:      Optional[Any]			  =				os.path.join(__A		,		__A )
        # Save the index as well
        with open(__A		,		'''w'''		,		encoding='''utf-8''' ) as f:
            _UpperCAmelCase				:      Optional[int]			  =				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.''' )
        _UpperCAmelCase				:      Dict			  =				list(shards.keys() )

        del state_dict
        del shards
        gc.collect()

        for shard_file in shard_files:
            _UpperCAmelCase				:      List[str]			  =				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.''' )
        _UpperCAmelCase				:      List[str]			  =				AutoModelForCausalLM.from_pretrained(__A )
        model.push_to_hub(__A		,		max_shard_size='''2GB''' )
        tokenizer.push_to_hub(__A )


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












import os
from collections.abc import Iterator

def      _lowerCamelCase	(						__A    :							str = "." )      ->					Iterator[str]:
    for dir_path, dir_names, filenames in os.walk(__A ):
        _UpperCAmelCase				:      List[Any]			  =				[d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
        for filename in filenames:
            if filename == "__init__.py":
                continue
            if os.path.splitext(__A )[1] in (".py", ".ipynb"):
                yield os.path.join(__A		,		__A ).lstrip('''./''' )

def      _lowerCamelCase	(						__A    :							Dict )      ->					List[Any]:
    return f'''{i * '  '}*''' if i else "\n##"

def      _lowerCamelCase	(						__A    :							str		,		__A    :							str )      ->					str:
    _UpperCAmelCase				:      int			  =				old_path.split(os.sep )
    for i, new_part in enumerate(new_path.split(os.sep ) ):
        if (i + 1 > len(__A ) or old_parts[i] != new_part) and new_part:
            print(f'''{md_prefix(__A )} {new_part.replace('_'		,		' ' ).title()}''' )
    return new_path

def      _lowerCamelCase	(						__A    :							str = "." )      ->					None:
    _UpperCAmelCase				:      List[str]			  =				''''''
    for filepath in sorted(good_file_paths(__A ) ):
        _UpperCAmelCase  , _UpperCAmelCase				:      Optional[Any]			  =				os.path.split(__A )
        if filepath != old_path:
            _UpperCAmelCase				:      Optional[int]			  =				print_path(__A		,		__A )
        _UpperCAmelCase				:      Dict			  =				(filepath.count(os.sep ) + 1) if filepath else 0
        _UpperCAmelCase				:      Union[str, Any]			  =				f'''{filepath}/{filename}'''.replace(''' '''		,		'''%20''' )
        _UpperCAmelCase				:      List[Any]			  =				os.path.splitext(filename.replace('''_'''		,		''' ''' ).title() )[0]
        print(f'''{md_prefix(__A )} [{filename}]({url})''' )


if __name__ == "__main__":
  print_directory_md('.')




