


'''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


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

__lowercase     :						Union[str, Any]	=     {
    '169M': 7_68,
    '430M': 10_24,
    '1B5': 20_48,
    '3B': 25_60,
    '7B': 40_96,
    '14B': 51_20,
}






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

										if name != "head.weight":
															__a      :	int    =  'rwkv.' + name

										__a      :	Optional[int]    =  weight
					return state_dict






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

					# 2. Build the config
					__a      :	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:
																				__a      :	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}."""       )

					__a      :	Optional[int]    =  RwkvConfig(
					    vocab_size=_SCREAMING_SNAKE_CASE						,				num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]						,				hidden_size=HIDEN_SIZE_MAPPING[size]						,				)
					config.save_pretrained(_SCREAMING_SNAKE_CASE       )

					# 3. Download model file then convert state_dict
					__a      :	List[str]    =  hf_hub_download(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
					__a      :	Union[str, Any]    =  torch.load(_SCREAMING_SNAKE_CASE						,				map_location='cpu'       )
					__a      :	Any    =  convert_state_dict(_SCREAMING_SNAKE_CASE       )

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

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

										# 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.'       )
										__a      :	Dict    =  list(shards.keys()       )

										del state_dict
										del shards
										gc.collect()

										for shard_file in shard_files:
															__a      :	int    =  torch.load(os.path.join(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )       )
															torch.save({k: v.cpu().clone() for k, v in state_dict.items()}						,				os.path.join(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )       )

					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.'       )
										__a      :	Optional[int]    =  AutoModelForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE       )
										model.push_to_hub(_SCREAMING_SNAKE_CASE						,				max_shard_size='2GB'       )
										tokenizer.push_to_hub(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
	__lowercase     :						int	=     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.',
	)

	__lowercase     :						Union[str, 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,
	)







'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int       ):
					return number & 1 == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()



