




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": 768,
    "430M": 1024,
    "1B5": 2048,
    "3B": 2560,
    "7B": 4096,
    "14B": 5120,
}




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				int:
      _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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case=None			,     snake_case=None			,     snake_case=False			,     snake_case=None       )						->				List[Any]:




      if tokenizer_file is None:
            print("""No `--tokenizer_file` provided, we will use the default tokenizer."""       )
            _UpperCAmelCase      =			5_0_2_7_7
            _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      =			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__":
    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 itertools
import string
from collections.abc import Generator, Iterable




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Generator[tuple[str, ...], None, None]:
						_UpperCAmelCase      =			iter(snake_case       )
						while True:
												_UpperCAmelCase      =			tuple(itertools.islice(snake_case			,     snake_case       )       )
												if not chunk:
																		return
												yield chunk




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				str:
						_UpperCAmelCase      =			"""""".join([c.upper() for c in dirty if c in string.ascii_letters]       )
						_UpperCAmelCase      =			""""""

						if len(snake_case       ) < 2:
												return dirty

						for i in range(len(snake_case       ) - 1       ):
												clean += dirty[i]

												if dirty[i] == dirty[i + 1]:
																		clean += "X"

						clean += dirty[-1]

						if len(snake_case       ) & 1:
												clean += "X"

						return clean




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				list[str]:
						# I and J are used interchangeably to allow
						# us to use a 5x5 table (25 letters)
						_UpperCAmelCase      =			"""ABCDEFGHIKLMNOPQRSTUVWXYZ"""
						# we're using a list instead of a '2d' array because it makes the math
						# for setting up the table and doing the actual encoding/decoding simpler
						_UpperCAmelCase      =			[]

						# copy key chars into the table if they are in `alphabet` ignoring duplicates
						for char in key.upper():
												if char not in table and char in alphabet:
																		table.append(snake_case       )

    # fill the rest of the table in with the remaining alphabet chars
						for char in alphabet:
												if char not in table:
																		table.append(snake_case       )

						return table




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				str:
						_UpperCAmelCase      =			generate_table(snake_case       )
						_UpperCAmelCase      =			prepare_input(snake_case       )
						_UpperCAmelCase      =			""""""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(snake_case			,     2       ):
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )

												if rowa == rowa:
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
												elif cola == cola:
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												else:  # rectangle
																		ciphertext += table[rowa * 5 + cola]
																		ciphertext += table[rowa * 5 + cola]

						return ciphertext




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				str:
						_UpperCAmelCase      =			generate_table(snake_case       )
						_UpperCAmelCase      =			""""""

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(snake_case			,     2       ):
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )
												_UpperCAmelCase    ,					_UpperCAmelCase      =			divmod(table.index(snake_case       )			,     5       )

												if rowa == rowa:
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
												elif cola == cola:
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
												else:  # rectangle
																		plaintext += table[rowa * 5 + cola]
																		plaintext += table[rowa * 5 + cola]

						return plaintext