


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   :					Optional[Any]	=      {
    "169M": 12,
    "430M": 24,
    "1B5": 24,
    "3B": 32,
    "7B": 32,
    "14B": 40,
}

A   :					List[str]	=      {
    "169M": 768,
    "430M": 1024,
    "1B5": 2048,
    "3B": 2560,
    "7B": 4096,
    "14B": 5120,
}

def    lowercase_	(	_A    :					List[str]		):

						"""simple docstring"""




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

												if name != "head.weight":
																		lowerCamelCase__	:       Any					=						"rwkv." + name

												lowerCamelCase__	:       Union[str, Any]					=						weight
						return state_dict

def    lowercase_	(	_A    :					List[Any]					,      _A    :					List[str]					,      _A    :					int					,      _A    :					Dict=None					,      _A    :					Tuple=None					,      _A    :					Union[str, Any]=False					,      _A    :					str=None		):

						"""simple docstring"""




						if tokenizer_file is None:
												print("No `--tokenizer_file` provided, we will use the default tokenizer."		)
												lowerCamelCase__	:       str					=						50277
												lowerCamelCase__	:       int					=						AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"		)
						else:
												lowerCamelCase__	:       Tuple					=						PreTrainedTokenizerFast(tokenizer_file=__A		)
												lowerCamelCase__	:       Any					=						len(__A		)
						tokenizer.save_pretrained(__A		)

						# 2. Build the config
						lowerCamelCase__	:       List[str]					=						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:
																								lowerCamelCase__	:       Optional[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}."		)

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

						# 3. Download model file then convert state_dict
						lowerCamelCase__	:       Tuple					=						hf_hub_download(__A					,      __A		)
						lowerCamelCase__	:       List[str]					=						torch.load(__A					,      map_location="cpu"		)
						lowerCamelCase__	:       List[str]					=						convert_state_dict(__A		)

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

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

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

												del state_dict
												del shards
												gc.collect()

												for shard_file in shard_files:
																		lowerCamelCase__	:       Union[str, Any]					=						torch.load(os.path.join(__A					,      __A		)		)
																		torch.save({k: v.cpu().clone() for k, v in state_dict.items()}					,      os.path.join(__A					,      __A		)		)

						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."		)
												lowerCamelCase__	:       Union[str, Any]					=						AutoModelForCausalLM.from_pretrained(__A		)
												model.push_to_hub(__A					,      max_shard_size="2GB"		)
												tokenizer.push_to_hub(__A		)


if __name__ == "__main__":
				A   :					List[str]	=      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   :					Optional[Any]	=      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,
				)






def    lowercase_	(	_A    :					int					,      _A    :					int		):

      """simple docstring"""




      if a < 0 or b < 0:
            raise ValueError("the value of both inputs must be positive"		)

      lowerCamelCase__	:       List[str]					=						str(bin(_A		)		)[2:]  # remove the leading "0b"
      lowerCamelCase__	:       List[Any]					=						str(bin(_A		)		)[2:]  # remove the leading "0b"

      lowerCamelCase__	:       List[Any]					=						max(len(_A		)					,      len(_A		)		)

      return "0b" + "".join(
          str(int(char_a != char_b		)		)
          for char_a, char_b in zip(a_binary.zfill(_A		)					,      b_binary.zfill(_A		)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


