


"""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


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

lowerCAmelCase__				    =       {
    '''169M''': 768,
    '''430M''': 1_024,
    '''1B5''': 2_048,
    '''3B''': 2_560,
    '''7B''': 4_096,
    '''14B''': 5_120,
}
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







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

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

    lowerCAmelCase					:					Optional[Any]								=							weight
  return state_dict
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]    ,				SCREAMING_SNAKE_CASE   :   Any    ,				SCREAMING_SNAKE_CASE   :   int=None    ,				SCREAMING_SNAKE_CASE   :   List[str]=None    ,				SCREAMING_SNAKE_CASE   :   Any=False    ,				SCREAMING_SNAKE_CASE   :   Any=None    ):


  '''simple docstring'''







  if tokenizer_file is None:
    print("No `--tokenizer_file` provided, we will use the default tokenizer."    )
    lowerCAmelCase					:					Dict								=							5_0_2_7_7
    lowerCAmelCase					:					Dict								=							AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"    )
  else:
    lowerCAmelCase					:					int								=							PreTrainedTokenizerFast(tokenizer_file=SCREAMING_SNAKE_CASE    )
    lowerCAmelCase					:					Tuple								=							len(SCREAMING_SNAKE_CASE    )
  tokenizer.save_pretrained(SCREAMING_SNAKE_CASE    )

  # 2. Build the config
  lowerCAmelCase					:					str								=							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					:					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}."""    )

  lowerCAmelCase					:					int								=							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
  lowerCAmelCase					:					List[str]								=							hf_hub_download(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					Optional[int]								=							torch.load(SCREAMING_SNAKE_CASE    ,				map_location="cpu"    )
  lowerCAmelCase					:					List[str]								=							convert_state_dict(SCREAMING_SNAKE_CASE    )

  # 4. Split in shards and save
  lowerCAmelCase						,  lowerCAmelCase					:					Union[str, Any]								=							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:
    lowerCAmelCase					:					Optional[Any]								=							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:
      lowerCAmelCase					:					Optional[Any]								=							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."    )
    lowerCAmelCase					:					List[str]								=							list(shards.keys()    )

    del state_dict
    del shards
    gc.collect()

    for shard_file in shard_files:
      lowerCAmelCase					:					int								=							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."    )
    lowerCAmelCase					:					Tuple								=							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__":
     lowerCAmelCase__				    =       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.''',
     )

     lowerCAmelCase__				    =       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 typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase__				    =       {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase__				    =       [
         '''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''VanForImageClassification''',
         '''VanModel''',
         '''VanPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_van import (
              VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              VanForImageClassification,
              VanModel,
              VanPreTrainedModel,
          )

else:
     import sys

     lowerCAmelCase__				    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure)





