"""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


UpperCAmelCase__  		=						{
    """169M""": 1_2,
    """430M""": 2_4,
    """1B5""": 2_4,
    """3B""": 3_2,
    """7B""": 3_2,
    """14B""": 4_0,
}

UpperCAmelCase__  		=						{
    """169M""": 7_6_8,
    """430M""": 1_0_2_4,
    """1B5""": 2_0_4_8,
    """3B""": 2_5_6_0,
    """7B""": 4_0_9_6,
    """14B""": 5_1_2_0,
}






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      list(state_dict.keys()		)
  for name in state_dict_keys:
    _UpperCAmelCase						 =      state_dict.pop(lowercase		)
    # 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"""   ,lowercase		)
    # ffn -> feed_forward
    _UpperCAmelCase						 =      re.sub(R"""blocks\.(\d+)\.ffn"""   ,R"""blocks.\1.feed_forward"""   ,lowercase		)
    # 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     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase=None   ,lowercase=None   ,lowercase=False   ,lowercase=None		):







  """simple docstring"""
  # 1. If possible, build the tokenizer.
  if tokenizer_file is None:
    print("""No `--tokenizer_file` provided, we will use the default tokenizer."""		)
    _UpperCAmelCase						 =      5_02_77
    _UpperCAmelCase						 =      AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""		)
  else:
    _UpperCAmelCase						 =      PreTrainedTokenizerFast(tokenizer_file=lowercase		)
    _UpperCAmelCase						 =      len(lowercase		)
  tokenizer.save_pretrained(lowercase		)

  # 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=lowercase   ,num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]   ,hidden_size=HIDEN_SIZE_MAPPING[size]   ,)
  config.save_pretrained(lowercase		)

  # 3. Download model file then convert state_dict
  _UpperCAmelCase						 =      hf_hub_download(lowercase   ,lowercase		)
  _UpperCAmelCase						 =      torch.load(lowercase   ,map_location="""cpu"""		)
  _UpperCAmelCase						 =      convert_state_dict(lowercase		)

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

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

    # 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(lowercase   ,lowercase		)		)
      torch.save({k: v.cpu().clone() for k, v in state_dict.items()}   ,os.path.join(lowercase   ,lowercase		)		)

  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(lowercase		)
    model.push_to_hub(lowercase   ,max_shard_size="""2GB"""		)
    tokenizer.push_to_hub(lowercase		)


if __name__ == "__main__":
    UpperCAmelCase__  		=						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__  		=						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 abc import ABC, abstractmethod
from argparse import ArgumentParser


class        a				(		lowerCAmelCase_   ):


    @staticmethod
    @abstractmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  ArgumentParser					):
      raise NotImplementedError()


    @abstractmethod
    def      lowerCAmelCase_  (					self	:  int					):
      raise NotImplementedError()
