



'''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


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

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






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

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

												_UpperCamelCase :		Any			    =       weight
						return state_dict






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=False  ,  UpperCAmelCase_=None							):
						# 1. If possible, build the tokenizer.
						if tokenizer_file is None:
												print('No `--tokenizer_file` provided, we will use the default tokenizer.'							)
												_UpperCamelCase :		Union[str, Any]			    =       5_0_2_7_7
												_UpperCamelCase :		Optional[int]			    =       AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'							)
						else:
												_UpperCamelCase :		Optional[int]			    =       PreTrainedTokenizerFast(tokenizer_file=UpperCAmelCase_							)
												_UpperCamelCase :		Optional[int]			    =       len(UpperCAmelCase_							)
						tokenizer.save_pretrained(UpperCAmelCase_							)

						# 2. Build the config
						_UpperCamelCase :		Tuple			    =       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 :		List[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 :		Optional[int]			    =       RwkvConfig(
						    vocab_size=UpperCAmelCase_  ,  num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]  ,  hidden_size=HIDEN_SIZE_MAPPING[size]  ,  )
						config.save_pretrained(UpperCAmelCase_							)

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

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

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

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

												del state_dict
												del shards
												gc.collect()

												for shard_file in shard_files:
																		_UpperCamelCase :		List[str]			    =       torch.load(os.path.join(UpperCAmelCase_  ,  UpperCAmelCase_							)							)
																		torch.save({k: v.cpu().clone() for k, v in state_dict.items()}  ,  os.path.join(UpperCAmelCase_  ,  UpperCAmelCase_							)							)

						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 :		int			    =       AutoModelForCausalLM.from_pretrained(UpperCAmelCase_							)
												model.push_to_hub(UpperCAmelCase_  ,  max_shard_size='2GB'							)
												tokenizer.push_to_hub(UpperCAmelCase_							)


if __name__ == "__main__":
					snake_case_			:     Optional[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.',
					)

					snake_case_			:     Optional[int]  	=  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'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser






class 							lowercase__     (							lowercase       ):





	@staticmethod
	@abstractmethod
	def      UpperCamelCase_  (			lowerCamelCase__ :    ArgumentParser			):







							'''simple docstring'''
							raise NotImplementedError()





	@abstractmethod
	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							raise NotImplementedError()


