


'''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


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

SCREAMING_SNAKE_CASE_      =			{
    '169M': 7_68,
    '430M': 10_24,
    '1B5': 20_48,
    '3B': 25_60,
    '7B': 40_96,
    '14B': 51_20,
}




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




     __a       =   list(state_dict.keys()		)
     for name in state_dict_keys:
          __a       =   state_dict.pop(__SCREAMING_SNAKE_CASE		)
          # 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"""				,  __SCREAMING_SNAKE_CASE		)
          # ffn -> feed_forward
          __a       =   re.sub(r"""blocks\.(\d+)\.ffn"""				,  r"""blocks.\1.feed_forward"""				,  __SCREAMING_SNAKE_CASE		)
          # 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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=None				,  __SCREAMING_SNAKE_CASE=None				,  __SCREAMING_SNAKE_CASE=False				,  __SCREAMING_SNAKE_CASE=None		)				->					Tuple:
     """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=__SCREAMING_SNAKE_CASE		)
          __a       =   len(__SCREAMING_SNAKE_CASE		)
     tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE		)

     # 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=__SCREAMING_SNAKE_CASE				,  num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]				,  hidden_size=HIDEN_SIZE_MAPPING[size]				,  )
     config.save_pretrained(__SCREAMING_SNAKE_CASE		)

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

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

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

          # 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(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)		)
               torch.save({k: v.cpu().clone() for k, v in state_dict.items()}				,  os.path.join(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)		)

     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(__SCREAMING_SNAKE_CASE		)
          model.push_to_hub(__SCREAMING_SNAKE_CASE				,  max_shard_size="""2GB"""		)
          tokenizer.push_to_hub(__SCREAMING_SNAKE_CASE		)


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,
 )










'''simple docstring'''







from __future__ import annotations

import os
from typing import Any

import requests

SCREAMING_SNAKE_CASE_      =			'https://api.github.com'

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
SCREAMING_SNAKE_CASE_      =			BASE_URL + '/user'

# https://github.com/settings/tokens
SCREAMING_SNAKE_CASE_      =			os.environ.get('USER_TOKEN', '')




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					dict[Any, Any]:
     """simple docstring"""




     __a       =   {
         """Authorization""": F'''token {auth_token}''',
         """Accept""": """application/vnd.github.v3+json""",
     }
     return requests.get(__SCREAMING_SNAKE_CASE				,  headers=__SCREAMING_SNAKE_CASE		).json()


if __name__ == "__main__":  # pragma: no cover
 if USER_TOKEN:
  for key, value in fetch_github_info(USER_TOKEN).items():
   print(f"""{key}: {value}""")
 else:
  raise ValueError('\'USER_TOKEN\' field cannot be empty.')






