


'''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


UpperCAmelCase__  			=				{
    '''169M''': 1_2,
    '''430M''': 2_4,
    '''1B5''': 2_4,
    '''3B''': 3_2,
    '''7B''': 3_2,
    '''14B''': 4_0,
}

UpperCAmelCase__  			=				{
    '''169M''': 7_6_8,
    '''430M''': 1_0_2_4,
    '''1B5''': 2_0_4_8,
    '''3B''': 2_5_6_0,
    '''7B''': 4_0_9_6,
    '''14B''': 5_1_2_0,
}






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Tuple ):






			"""simple docstring"""




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

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

						__A=	weight
			return state_dict






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str,_SCREAMING_SNAKE_CASE					: Union[str, Any],_SCREAMING_SNAKE_CASE					: Optional[int],_SCREAMING_SNAKE_CASE					: Optional[int]=None,_SCREAMING_SNAKE_CASE					: Union[str, Any]=None,_SCREAMING_SNAKE_CASE					: Dict=False,_SCREAMING_SNAKE_CASE					: str=None ):






			"""simple docstring"""




			if tokenizer_file is None:
						print('No `--tokenizer_file` provided, we will use the default tokenizer.' )
						__A=	5_0277
						__A=	AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b' )
			else:
						__A=	PreTrainedTokenizerFast(tokenizer_file=_SCREAMING_SNAKE_CASE )
						__A=	len(_SCREAMING_SNAKE_CASE )
			tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )

			# 2. Build the config
			__A=	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=	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=	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=	hf_hub_download(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			__A=	torch.load(_SCREAMING_SNAKE_CASE,map_location='cpu' )
			__A=	convert_state_dict(_SCREAMING_SNAKE_CASE )

			# 4. Split in shards and save
			__A,	__A=	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=	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=	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=	list(shards.keys() )

						del state_dict
						del shards
						gc.collect()

						for shard_file in shard_files:
									__A=	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=	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__":
					UpperCAmelCase__  			=				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.''',
					)

					UpperCAmelCase__  			=				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 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			return int((input_a, input_a).count(1 ) != 0 )






def 						UpperCAmelCase__(				):






			"""simple docstring"""




			assert or_gate(0,0 ) == 0
			assert or_gate(0,1 ) == 1
			assert or_gate(1,0 ) == 1
			assert or_gate(1,1 ) == 1


if __name__ == "__main__":
					print(or_gate(0, 1))
					print(or_gate(1, 0))
					print(or_gate(0, 0))
					print(or_gate(1, 1))



