

"""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_     :			List[str]	  =			{
    '''169M''': 12,
    '''430M''': 24,
    '''1B5''': 24,
    '''3B''': 32,
    '''7B''': 32,
    '''14B''': 40,
}

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



def 	UpperCAmelCase__   (				A__   )      ->      Optional[Any]:




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

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

      lowerCamelCase__						=			weight
   return state_dict



def 	UpperCAmelCase__   (				A__	,    A__	,    A__	,    A__=None	,    A__=None	,    A__=False	,    A__=None   )      ->      Dict:




   """simple docstring"""
   # 1. If possible, build the tokenizer.
   if tokenizer_file is None:
      print("No `--tokenizer_file` provided, we will use the default tokenizer."   )
      lowerCamelCase__						=			5_0277
      lowerCamelCase__						=			AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"   )
   else:
      lowerCamelCase__						=			PreTrainedTokenizerFast(tokenizer_file=A__   )
      lowerCamelCase__						=			len(A__   )
   tokenizer.save_pretrained(A__   )

   # 2. Build the config
   lowerCamelCase__						=			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:
            lowerCamelCase__						=			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}.'   )

   lowerCamelCase__						=			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
   lowerCamelCase__						=			hf_hub_download(A__	,    A__   )
   lowerCamelCase__						=			torch.load(A__	,    map_location="cpu"   )
   lowerCamelCase__						=			convert_state_dict(A__   )

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

   if index is not None:
      lowerCamelCase__						=			os.path.join(A__	,    A__   )
      # Save the index as well
      with open(A__	,    "w"	,    encoding="utf-8"   ) as f:
         lowerCamelCase__						=			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."   )
      lowerCamelCase__						=			list(shards.keys()   )

      del state_dict
      del shards
      gc.collect()

      for shard_file in shard_files:
         lowerCamelCase__						=			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."   )
      lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(A__   )
      model.push_to_hub(A__	,    max_shard_size="2GB"   )
      tokenizer.push_to_hub(A__   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			Tuple	  =			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_     :			Dict	  =			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"""




# flake8: noqa
# Lint as: python3
SCREAMING_SNAKE_CASE_     :			Dict	  =			[
    '''VerificationMode''',
    '''Version''',
    '''disable_progress_bar''',
    '''enable_progress_bar''',
    '''is_progress_bar_enabled''',
    '''experimental''',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental




