




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


_UpperCAmelCase		:  Tuple	=   {
    "169M": 12,
    "430M": 24,
    "1B5": 24,
    "3B": 32,
    "7B": 32,
    "14B": 40,
}

_UpperCAmelCase		:  Any	=   {
    "169M": 768,
    "430M": 1024,
    "1B5": 2048,
    "3B": 2560,
    "7B": 4096,
    "14B": 5120,
}



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

    if name != "head.weight":
      lowercase			:int							=  "rwkv." + name

    lowercase			:Optional[Any]							=  weight
  return state_dict



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

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

  lowercase			:Dict							=  RwkvConfig(
      vocab_size=lowerCamelCase,	num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size],	hidden_size=HIDEN_SIZE_MAPPING[size],	)
  config.save_pretrained(lowerCamelCase      )

  # 3. Download model file then convert state_dict
  lowercase			:Optional[Any]							=  hf_hub_download(lowerCamelCase,	lowerCamelCase      )
  lowercase			:List[Any]							=  torch.load(lowerCamelCase,	map_location="cpu"      )
  lowercase			:Optional[Any]							=  convert_state_dict(lowerCamelCase      )

  # 4. Split in shards and save
  lowercase		,			lowercase			:Optional[int]							=  shard_checkpoint(lowerCamelCase      )
  for shard_file, shard in shards.items():
    torch.save(lowerCamelCase,	os.path.join(lowerCamelCase,	lowerCamelCase      )      )

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

    # 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."      )
    lowercase			:Optional[int]							=  list(shards.keys()      )

    del state_dict
    del shards
    gc.collect()

    for shard_file in shard_files:
      lowercase			:List[Any]							=  torch.load(os.path.join(lowerCamelCase,	lowerCamelCase      )      )
      torch.save({k: v.cpu().clone() for k, v in state_dict.items()},	os.path.join(lowerCamelCase,	lowerCamelCase      )      )

  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."      )
    lowercase			:str							=  AutoModelForCausalLM.from_pretrained(lowerCamelCase      )
    model.push_to_hub(lowerCamelCase,	max_shard_size="2GB"      )
    tokenizer.push_to_hub(lowerCamelCase      )


if __name__ == "__main__":
     _UpperCAmelCase		:  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.",
     )

     _UpperCAmelCase		:  Optional[int]	=   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 typing import TYPE_CHECKING

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


_UpperCAmelCase		:  int	=   {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  List[str]	=   [
         "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "SwinForImageClassification",
         "SwinForMaskedImageModeling",
         "SwinModel",
         "SwinPreTrainedModel",
         "SwinBackbone",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  int	=   [
         "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFSwinForImageClassification",
         "TFSwinForMaskedImageModeling",
         "TFSwinModel",
         "TFSwinPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swin import (
              SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwinBackbone,
              SwinForImageClassification,
              SwinForMaskedImageModeling,
              SwinModel,
              SwinPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_swin import (
              TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
              TFSwinModel,
              TFSwinPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase		:  int	=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



