




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


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

__a							     =					{
    '169M': 768,
    '430M': 1_024,
    '1B5': 2_048,
    '3B': 2_560,
    '7B': 4_096,
    '14B': 5_120,
}







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


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

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

          UpperCAmelCase_     :				Dict  =     weight
     return state_dict







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase=None					,		_lowercase=None					,		_lowercase=False					,		_lowercase=None ):

     '''simple docstring'''


     if tokenizer_file is None:
          print('''No `--tokenizer_file` provided, we will use the default tokenizer.''' )
          UpperCAmelCase_     :				Tuple  =     50277
          UpperCAmelCase_     :				Union[str, Any]  =     AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b''' )
     else:
          UpperCAmelCase_     :				Union[str, Any]  =     PreTrainedTokenizerFast(tokenizer_file=_lowercase )
          UpperCAmelCase_     :				List[Any]  =     len(_lowercase )
     tokenizer.save_pretrained(_lowercase )

     # 2. Build the config
     UpperCAmelCase_     :				List[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_     :				List[Any]  =     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=_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_     :				Dict  =     hf_hub_download(_lowercase					,		_lowercase )
     UpperCAmelCase_     :				Optional[Any]  =     torch.load(_lowercase					,		map_location='''cpu''' )
     UpperCAmelCase_     :				Optional[int]  =     convert_state_dict(_lowercase )

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

     if index is not None:
          UpperCAmelCase_     :				Any  =     os.path.join(_lowercase					,		_lowercase )
          # Save the index as well
          with open(_lowercase					,		'''w'''					,		encoding='''utf-8''' ) as f:
               UpperCAmelCase_     :				List[Any]  =     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_     :				Tuple  =     list(shards.keys() )

          del state_dict
          del shards
          gc.collect()

          for shard_file in shard_files:
               UpperCAmelCase_     :				Optional[Any]  =     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_     :				Union[str, Any]  =     AutoModelForCausalLM.from_pretrained(_lowercase )
          model.push_to_hub(_lowercase					,		max_shard_size='''2GB''' )
          tokenizer.push_to_hub(_lowercase )


if __name__ == "__main__":
     __a							     =					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.',
     )

     __a							     =					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,
     )





from functools import reduce

__a							     =					(
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)







def lowerCamelCase__  (     _lowercase = N ):

     '''simple docstring'''


     return max(
         # mypy cannot properly interpret reduce
         int(reduce(lambda _lowercase					,		_lowercase : str(int(_lowercase ) * int(_lowercase ) )					,		n[i : i + 13] ) )
         for i in range(len(_lowercase ) - 12 ) )


if __name__ == "__main__":
     print(F"""{solution() = }""")