






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


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

__magic_name__:					str							=     {
    "169M": 768,
    "430M": 1_024,
    "1B5": 2_048,
    "3B": 2_560,
    "7B": 4_096,
    "14B": 5_120,
}

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







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

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

												__magic_name__      :       Optional[int]       						= weight
						return state_dict

def    UpperCamelCase						(	_A,   _A,   _A,   _A=None,   _A=None,   _A=False,   _A=None			):






						"""simple docstring"""







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

						# 2. Build the config
						__magic_name__      :       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:
																								__magic_name__      :       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}.'			)

						__magic_name__      :       Optional[Any]       						= 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
						__magic_name__      :       int       						= hf_hub_download(_A,   _A			)
						__magic_name__      :       Optional[int]       						= torch.load(_A,   map_location="""cpu"""			)
						__magic_name__      :       int       						= convert_state_dict(_A			)

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

						if index is not None:
												__magic_name__      :       int       						= os.path.join(_A,   _A			)
												# Save the index as well
												with open(_A,   """w""",   encoding="""utf-8"""			) as f:
																		__magic_name__      :       str       						= 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."""			)
												__magic_name__      :       str       						= list(shards.keys()			)

												del state_dict
												del shards
												gc.collect()

												for shard_file in shard_files:
																		__magic_name__      :       Optional[int]       						= 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."""			)
												__magic_name__      :       Any       						= AutoModelForCausalLM.from_pretrained(_A			)
												model.push_to_hub(_A,   max_shard_size="""2GB"""			)
												tokenizer.push_to_hub(_A			)


if __name__ == "__main__":
		__magic_name__:					List[Any]							=     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.",
		)

		__magic_name__:					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    UpperCamelCase						(	_A = 1,   _A = 1000			):






						"""simple docstring"""







						__magic_name__      :       Optional[int]       						= 1
						__magic_name__      :       Dict       						= 0

						for divide_by_number in range(_A,   digit + 1			):
												__magic_name__      :       list[int]       						= []
												__magic_name__      :       Any       						= numerator
												for _ in range(1,   digit + 1			):
																		if now_divide in has_been_divided:
																								if longest_list_length < len(_A			):
																														__magic_name__      :       int       						= len(_A			)
																														__magic_name__      :       Dict       						= divide_by_number
																		else:
																								has_been_divided.append(_A			)
																								__magic_name__      :       Optional[int]       						= now_divide * 10 % divide_by_number

						return the_digit


# Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()
