





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_	(      lowerCAmelCase:	Any				)->		Tuple:
  _snake_case   :				str														=	list(state_dict.keys()				)
  for name in state_dict_keys:
    _snake_case   :				List[Any]														=	state_dict.pop(lowerCAmelCase				)
    # emb -> embedding
    if name.startswith('emb.'				):
      _snake_case   :				List[Any]														=	name.replace('emb.'    ,							'embeddings.'				)
    # ln_0 -> pre_ln (only present at block 0)
    if name.startswith('blocks.0.ln0'				):
      _snake_case   :				Union[str, Any]														=	name.replace('blocks.0.ln0'    ,							'blocks.0.pre_ln'				)
    # att -> attention
    _snake_case   :				Dict														=	re.sub(R'blocks\.(\d+)\.att'    ,							R'blocks.\1.attention'    ,							lowerCAmelCase				)
    # ffn -> feed_forward
    _snake_case   :				Dict														=	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'				):
      _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   :				Tuple														=	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[Any]														=	name.replace('.time_mix_r'    ,							'.time_mix_receptance'				)

    if name != "head.weight":
      _snake_case   :				str														=	'rwkv.' + name

    _snake_case   :				Optional[int]														=	weight
  return state_dict


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple    ,							lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	Any    ,							lowerCAmelCase:	Any=None    ,							lowerCAmelCase:	Union[str, Any]=None    ,							lowerCAmelCase:	Optional[Any]=False    ,							lowerCAmelCase:	Optional[Any]=None				)->		Union[str, Any]:
  # 1. If possible, build the tokenizer.
  if tokenizer_file is None:
    print('No `--tokenizer_file` provided, we will use the default tokenizer.'				)
    _snake_case   :				int														=	5_02_77
    _snake_case   :				int														=	AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'				)
  else:
    _snake_case   :				List[Any]														=	PreTrainedTokenizerFast(tokenizer_file=lowerCAmelCase				)
    _snake_case   :				Tuple														=	len(lowerCAmelCase				)
  tokenizer.save_pretrained(lowerCAmelCase				)

  # 2. Build the config
  _snake_case   :				Union[str, Any]														=	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   :				Optional[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}."""				)

  _snake_case   :				Optional[Any]														=	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
  _snake_case   :				int														=	hf_hub_download(lowerCAmelCase    ,							lowerCAmelCase				)
  _snake_case   :				Dict														=	torch.load(lowerCAmelCase    ,							map_location='cpu'				)
  _snake_case   :				str														=	convert_state_dict(lowerCAmelCase				)

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

  if index is not None:
    _snake_case   :				int														=	os.path.join(lowerCAmelCase    ,							lowerCAmelCase				)
    # Save the index as well
    with open(lowerCAmelCase    ,							'w'    ,							encoding='utf-8'				) as f:
      _snake_case   :				Any														=	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.'				)
    _snake_case   :				int														=	list(shards.keys()				)

    del state_dict
    del shards
    gc.collect()

    for shard_file in shard_files:
      _snake_case   :				Optional[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.'				)
    _snake_case   :				Optional[Any]														=	AutoModelForCausalLM.from_pretrained(lowerCAmelCase				)
    model.push_to_hub(lowerCAmelCase    ,							max_shard_size='2GB'				)
    tokenizer.push_to_hub(lowerCAmelCase				)


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,
      )











import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCAmelCase_						   =    logging.get_logger(__name__)


lowerCAmelCase_						   =    {name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}


def     lowerCamelCase_	(      lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	Dict    ,							lowerCAmelCase:	Union[str, Any]				)->		Optional[int]:
  if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
    raise ValueError(F"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()				)}."""				)

  if tokenizer_name is None:
    _snake_case   :				Tuple														=	TOKENIZER_CLASSES
  else:
    _snake_case   :				Union[str, Any]														=	{tokenizer_name: getattr(lowerCAmelCase    ,							tokenizer_name + 'Fast'				)}

  logger.info(F"""Loading tokenizer classes: {tokenizer_names}"""				)

  for tokenizer_name in tokenizer_names:
    _snake_case   :				Dict														=	TOKENIZER_CLASSES[tokenizer_name]

    _snake_case   :				Optional[Any]														=	True
    if checkpoint_name is None:
      _snake_case   :				Union[str, Any]														=	list(tokenizer_class.max_model_input_sizes.keys()				)
    else:
      _snake_case   :				Optional[int]														=	[checkpoint_name]

    logger.info(F"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""				)

    for checkpoint in checkpoint_names:
      logger.info(F"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""				)

      # Load tokenizer
      _snake_case   :				str														=	tokenizer_class.from_pretrained(lowerCAmelCase    ,							force_download=lowerCAmelCase				)

      # Save fast tokenizer
      logger.info(F"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""				)

      # For organization names we create sub-directories
      if "/" in checkpoint:
        _snake_case							,					_snake_case   :				Tuple														=	checkpoint.split('/'				)
        _snake_case   :				int														=	os.path.join(lowerCAmelCase    ,							lowerCAmelCase				)
      elif add_prefix:
        _snake_case   :				Dict														=	checkpoint
        _snake_case   :				Optional[Any]														=	dump_path
      else:
        _snake_case   :				str														=	None
        _snake_case   :				Union[str, Any]														=	dump_path

      logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""				)

      if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()				)[0]:
        _snake_case   :				Optional[Any]														=	list(tokenizer.pretrained_vocab_files_map.values()				)[0][checkpoint]
        _snake_case   :				Optional[int]														=	file_path.split(lowerCAmelCase				)[-1][0]
        if next_char == "/":
          _snake_case   :				Union[str, Any]														=	os.path.join(lowerCAmelCase    ,							lowerCAmelCase				)
          _snake_case   :				str														=	None

        logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""				)

      _snake_case   :				Optional[int]														=	tokenizer.save_pretrained(
          lowerCAmelCase    ,							legacy_format=lowerCAmelCase    ,							filename_prefix=lowerCAmelCase				)
      logger.info(F"""=> File names {file_names}"""				)

      for file_name in file_names:
        if not file_name.endswith('tokenizer.json'				):
          os.remove(lowerCAmelCase				)
          logger.info(F"""=> removing {file_name}"""				)


if __name__ == "__main__":
      lowerCAmelCase_						   =    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--dump_path""", default=None, type=str, required=True, help="""Path to output generated fast tokenizer files."""
      )
      parser.add_argument(
          """--tokenizer_name""",
          default=None,
          type=str,
          help=(
              F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
              """download and convert all the checkpoints from AWS."""
          ),
      )
      parser.add_argument(
          """--checkpoint_name""",
          default=None,
          type=str,
          help="""Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.""",
      )
      parser.add_argument(
          """--force_download""",
          action="""store_true""",
          help="""Re-download checkpoints.""",
      )
      lowerCAmelCase_						   =    parser.parse_args()

      convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)




