'''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


snake_case__					:      Optional[Any]				      =  {
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

snake_case__					:      Optional[int]				      =  {
    """169M""": 768,
    """430M""": 1024,
    """1B5""": 2048,
    """3B""": 2560,
    """7B""": 4096,
    """14B""": 5120,
}


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

          if name != "head.weight":
               UpperCAmelCase_							:       List[str]       =				'rwkv.' + name

          UpperCAmelCase_							:       int       =				weight
     return state_dict


def  _lowerCamelCase   (				lowerCamelCase_ :							List[Any]     ,					lowerCamelCase_ :							Optional[Any]     ,					lowerCamelCase_ :							Optional[int]     ,					lowerCamelCase_ :							List[Any]=None     ,					lowerCamelCase_ :							Tuple=None     ,					lowerCamelCase_ :							Any=False     ,					lowerCamelCase_ :							Dict=None       ):
     # 1. If possible, build the tokenizer.
     if tokenizer_file is None:
          print('No `--tokenizer_file` provided, we will use the default tokenizer.'       )
          UpperCAmelCase_							:       Dict       =				50277
          UpperCAmelCase_							:       Any       =				AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'       )
     else:
          UpperCAmelCase_							:       str       =				PreTrainedTokenizerFast(tokenizer_file=_UpperCAmelCase       )
          UpperCAmelCase_							:       List[Any]       =				len(_UpperCAmelCase       )
     tokenizer.save_pretrained(_UpperCAmelCase       )

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

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

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

          del state_dict
          del shards
          gc.collect()

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


if __name__ == "__main__":
 snake_case__					:      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__					:      List[str]				      =  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,
 )







'''simple docstring'''


def  _lowerCamelCase   (				lowerCamelCase_ :							str       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[int]       =				[0] * len(lowerCamelCase_       )

     for i in range(1     ,					len(lowerCamelCase_       )       ):
          # use last results for better performance - dynamic programming
          UpperCAmelCase_							:       List[Any]       =				prefix_result[i - 1]
          while j > 0 and input_string[i] != input_string[j]:
               UpperCAmelCase_							:       str       =				prefix_result[j - 1]

          if input_string[i] == input_string[j]:
               j += 1
          UpperCAmelCase_							:       Any       =				j

     return prefix_result


def  _lowerCamelCase   (				lowerCamelCase_ :							str       ):



     """simple docstring"""





     return max(prefix_function(lowerCamelCase_       )       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






