


'''simple docstring'''


import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       Dict								=      ArgumentParser(
		    description=(
		        "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
		    )   )

		# Optional arguments for the launch helper
		parser.add_argument("--num_cores"      , type=__lowerCAmelCase      , default=1      , help="Number of TPU cores to use (1 or 8)."   )

		# positional
		parser.add_argument(
		    "training_script"      , type=__lowerCAmelCase      , help=(
		        "The full path to the single TPU training "
		        "program/script to be launched in parallel, "
		        "followed by all the arguments for the "
		        "training script"
		    )      , )

		# rest from the training program
		parser.add_argument("training_script_args"      , nargs=__lowerCAmelCase   )

		return parser.parse_args()






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       List[str]								=      parse_args()

		# Import training_script as a module.
		_UpperCAmelCase		:       Optional[Any]								=      Path(args.training_script   )
		sys.path.append(str(script_fpath.parent.resolve()   )   )
		_UpperCAmelCase		:       Any								=      script_fpath.stem
		_UpperCAmelCase		:       Tuple								=      importlib.import_module(__lowerCAmelCase   )

		# Patch sys.argv
		_UpperCAmelCase		:       Union[str, Any]								=      [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores   )]

		xmp.spawn(mod._mp_fn      , args=()      , nprocs=args.num_cores   )


if __name__ == "__main__":
						main()







'''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


lowerCamelCase__    	=     {
    '169M': 12,
    '430M': 24,
    '1B5': 24,
    '3B': 32,
    '7B': 32,
    '14B': 40,
}

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






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

				if name != "head.weight":
						_UpperCAmelCase		:       List[str]								=      "rwkv." + name

				_UpperCAmelCase		:       Optional[Any]								=      weight
		return state_dict






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase=None      , __lowerCAmelCase=None      , __lowerCAmelCase=False      , __lowerCAmelCase=None   ):
		# 1. If possible, build the tokenizer.
		if tokenizer_file is None:
				print("No `--tokenizer_file` provided, we will use the default tokenizer."   )
				_UpperCAmelCase		:       str								=      50_277
				_UpperCAmelCase		:       Optional[Any]								=      AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"   )
		else:
				_UpperCAmelCase		:       Tuple								=      PreTrainedTokenizerFast(tokenizer_file=__lowerCAmelCase   )
				_UpperCAmelCase		:       List[Any]								=      len(__lowerCAmelCase   )
		tokenizer.save_pretrained(__lowerCAmelCase   )

		# 2. Build the config
		_UpperCAmelCase		:       Optional[int]								=      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		:       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}."""   )

		_UpperCAmelCase		:       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
		_UpperCAmelCase		:       str								=      hf_hub_download(__lowerCAmelCase      , __lowerCAmelCase   )
		_UpperCAmelCase		:       Optional[int]								=      torch.load(__lowerCAmelCase      , map_location="cpu"   )
		_UpperCAmelCase		:       Any								=      convert_state_dict(__lowerCAmelCase   )

		# 4. Split in shards and save
		_UpperCAmelCase					,    _UpperCAmelCase		:       List[str]								=      shard_checkpoint(__lowerCAmelCase   )
		for shard_file, shard in shards.items():
				torch.save(__lowerCAmelCase      , os.path.join(__lowerCAmelCase      , __lowerCAmelCase   )   )

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

				del state_dict
				del shards
				gc.collect()

				for shard_file in shard_files:
						_UpperCAmelCase		:       Union[str, 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."   )
				_UpperCAmelCase		:       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,
						)



