

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": 1024,
    "1B5": 2048,
    "3B": 2560,
    "7B": 4096,
    "14B": 5120,
}
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  list(state_dict.keys()			)
  for name in state_dict_keys:
    UpperCAmelCase					      =  state_dict.pop(__A			)
    # emb -> embedding
    if name.startswith("emb."			):
      UpperCAmelCase					      =  name.replace("emb."   ,       "embeddings."			)
    # ln_0 -> pre_ln (only present at block 0)
    if name.startswith("blocks.0.ln0"			):
      UpperCAmelCase					      =  name.replace("blocks.0.ln0"   ,       "blocks.0.pre_ln"			)
    # att -> attention
    UpperCAmelCase					      =  re.sub(r"blocks\.(\d+)\.att"   ,       r"blocks.\1.attention"   ,       __A			)
    # ffn -> feed_forward
    UpperCAmelCase					      =  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"			):
      UpperCAmelCase					      =  name.replace(".time_mix_k"   ,       ".time_mix_key"			)
    # time_mix_v -> time_mix_value and reshape
    if name.endswith(".time_mix_v"			):
      UpperCAmelCase					      =  name.replace(".time_mix_v"   ,       ".time_mix_value"			)
    # time_mix_r -> time_mix_key and reshape
    if name.endswith(".time_mix_r"			):
      UpperCAmelCase					      =  name.replace(".time_mix_r"   ,       ".time_mix_receptance"			)

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

    UpperCAmelCase					      =  weight
  return state_dict
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A=None   ,       __A=None   ,       __A=False   ,       __A=None			):
  # 1. If possible, build the tokenizer.
  if tokenizer_file is None:
    print("No `--tokenizer_file` provided, we will use the default tokenizer."			)
    UpperCAmelCase					      =  50277
    UpperCAmelCase					      =  AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"			)
  else:
    UpperCAmelCase					      =  PreTrainedTokenizerFast(tokenizer_file=__A			)
    UpperCAmelCase					      =  len(__A			)
  tokenizer.save_pretrained(__A			)

  # 2. Build the config
  UpperCAmelCase					      =  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					      =  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					      =  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
  UpperCAmelCase					      =  hf_hub_download(__A   ,       __A			)
  UpperCAmelCase					      =  torch.load(__A   ,       map_location="cpu"			)
  UpperCAmelCase					      =  convert_state_dict(__A			)

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

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

    del state_dict
    del shards
    gc.collect()

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


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,
      )






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase__       = logging.get_logger(__name__)

lowerCAmelCase__       = {
    "facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json",
}






class 						__magic_name__    (       _snake_case	,						_snake_case    ):
 UpperCAmelCase							    =						"""convnextv2"""


 def __init__(						self		: Optional[Any]			,						lowerCAmelCase__		: List[Any]=3			,						lowerCAmelCase__		: str=4			,						lowerCAmelCase__		: Dict=4			,						lowerCAmelCase__		: Optional[Any]=None			,						lowerCAmelCase__		: Optional[Any]=None			,						lowerCAmelCase__		: str="gelu"			,						lowerCAmelCase__		: Optional[int]=0.02			,						lowerCAmelCase__		: Dict=1e-1_2			,						lowerCAmelCase__		: str=0.0			,						lowerCAmelCase__		: str=2_2_4			,						lowerCAmelCase__		: int=None			,						lowerCAmelCase__		: List[Any]=None			,						**lowerCAmelCase__		: List[Any]			,						)    ->      List[Any]:
   super().__init__(**lowerCAmelCase__    )

   UpperCAmelCase					      =  num_channels
   UpperCAmelCase					      =  patch_size
   UpperCAmelCase					      =  num_stages
   UpperCAmelCase					      =  [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
   UpperCAmelCase					      =  [3, 3, 9, 3] if depths is None else depths
   UpperCAmelCase					      =  hidden_act
   UpperCAmelCase					      =  initializer_range
   UpperCAmelCase					      =  layer_norm_eps
   UpperCAmelCase					      =  drop_path_rate
   UpperCAmelCase					      =  image_size
   UpperCAmelCase					      =  ["stem"] + [f"stage{idx}" for idx in range(1			,						len(self.depths    ) + 1    )]
   UpperCAmelCase      ,	UpperCAmelCase					      =  get_aligned_output_features_output_indices(
       out_features=lowerCAmelCase__			,						out_indices=lowerCAmelCase__			,						stage_names=self.stage_names    )



