



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


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

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



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

  if name != "head.weight":
   _snake_case    :  List[str]					=					"""rwkv.""" + name

  _snake_case    :  Optional[Any]					=					weight
 return state_dict



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	Any=None				,       SCREAMING_SNAKE_CASE__       :	List[Any]=None				,       SCREAMING_SNAKE_CASE__       :	Any=False				,       SCREAMING_SNAKE_CASE__       :	int=None				)  ->     str:
 # 1. If possible, build the tokenizer.
 if tokenizer_file is None:
  print("""No `--tokenizer_file` provided, we will use the default tokenizer."""				)
  _snake_case    :  Dict					=					50_277
  _snake_case    :  Optional[int]					=					AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""				)
 else:
  _snake_case    :  Any					=					PreTrainedTokenizerFast(tokenizer_file=SCREAMING_SNAKE_CASE__				)
  _snake_case    :  Optional[Any]					=					len(SCREAMING_SNAKE_CASE__				)
 tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__				)

 # 2. Build the config
 _snake_case    :  Optional[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:
    _snake_case    :  int					=					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}.'''				)

 _snake_case    :  Union[str, Any]					=					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
 _snake_case    :  Any					=					hf_hub_download(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Union[str, Any]					=					torch.load(SCREAMING_SNAKE_CASE__				,       map_location="""cpu"""				)
 _snake_case    :  Dict					=					convert_state_dict(SCREAMING_SNAKE_CASE__				)

 # 4. Split in shards and save
 _snake_case	,    _snake_case    :  int					=					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:
  _snake_case    :  Optional[int]					=					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:
   _snake_case    :  Union[str, 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."""				)
  _snake_case    :  Tuple					=					list(shards.keys()				)

  del state_dict
  del shards
  gc.collect()

  for shard_file in shard_files:
   _snake_case    :  List[str]					=					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."""				)
  _snake_case    :  Optional[int]					=					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__":
  a__    =	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.""",
  )

  a__    =	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,
  )





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		["""image_processor""", """tokenizer"""]
   snake_case_					: str          =		"""ChineseCLIPImageProcessor"""
   snake_case_					: Tuple          =		("""BertTokenizer""", """BertTokenizerFast""")

   def __init__(					self							:			Any				,		lowerCAmelCase							:			Dict=None				,		lowerCAmelCase							:			List[Any]=None				,		**lowerCAmelCase							:			str)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  List[str]					=					None
    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""				,		lowerCAmelCase				,		)
     _snake_case    :  Tuple					=					kwargs.pop("""feature_extractor""")

    _snake_case    :  Dict					=					image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
     raise ValueError("""You need to specify an `image_processor`.""")
    if tokenizer is None:
     raise ValueError("""You need to specify a `tokenizer`.""")

    super().__init__(lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Optional[int]					=					self.image_processor

   def __call__(					self							:			List[Any]				,		lowerCAmelCase							:			str=None				,		lowerCAmelCase							:			Optional[int]=None				,		lowerCAmelCase							:			List[Any]=None				,		**lowerCAmelCase							:			Optional[int])				->				Optional[Any]:

    """simple docstring"""







    if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none.""")

    if text is not None:
     _snake_case    :  Dict					=					self.tokenizer(lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase)

    if images is not None:
     _snake_case    :  Any					=					self.image_processor(lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase)

    if text is not None and images is not None:
     _snake_case    :  Optional[Any]					=					image_features.pixel_values
     return encoding
    elif text is not None:
     return encoding
    else:
     return BatchEncoding(data=dict(**lowerCAmelCase)				,		tensor_type=lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Dict				,		*lowerCAmelCase							:			Tuple				,		**lowerCAmelCase							:			Any)				->				Optional[Any]:

    """simple docstring"""







    return self.tokenizer.batch_decode(*lowerCAmelCase				,		**lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Tuple				,		*lowerCAmelCase							:			Tuple				,		**lowerCAmelCase							:			int)				->				str:

    """simple docstring"""







    return self.tokenizer.decode(*lowerCAmelCase				,		**lowerCAmelCase)

   @property
   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				List[str]:

    """simple docstring"""







    _snake_case    :  Dict					=					self.tokenizer.model_input_names
    _snake_case    :  int					=					self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


   @property
   def      UpperCamelCase_  (					self							:			str)				->				Tuple:

    """simple docstring"""







    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,		lowerCAmelCase				,		)
    return self.image_processor_class
