
'''simple docstring'''






from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

A			      =      TypeVar('KEY')
A			      =      TypeVar('VAL')




@dataclass(frozen=a__    ,   slots=a__)
class     __snake_case		(   Generic[KEY, VAL]):
					_lowerCAmelCase			     =	42
					_lowerCAmelCase			     =	42





class     __snake_case		(   _Item):
					def __init__(     self      ):

									"""simple docstring"""


									super().__init__(A,    A      )




					def __bool__(     self      ):

									"""simple docstring"""


									return False





A			      =      _DeletedItem()




class     __snake_case		(   MutableMapping[KEY, VAL]):
					def __init__(     self,    A = 8,    A = 0.75      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= initial_block_size
									lowerCamelCase					:			list[_Item | None]									= [None] * initial_block_size
									assert 0.0 < capacity_factor < 1.0
									lowerCamelCase					:			List[Any]									= capacity_factor
									lowerCamelCase					:			Optional[Any]									= 0
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return hash(A      ) % len(self._buckets      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return (ind + 1) % len(self._buckets      )
					def     UpperCAmelCase_							(     self,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= self._buckets[ind]
									if not stored:
													lowerCamelCase					:			Union[str, Any]									= _Item(A,    A      )
													self._len += 1
													return True
									elif stored.key == key:
													lowerCamelCase					:			Any									= _Item(A,    A      )
													return True
									else:
													return False
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= len(self._buckets      ) * self._capacity_factor
									return len(self      ) >= int(A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									if len(self._buckets      ) <= self._initial_block_size:
													return False
									lowerCamelCase					:			int									= len(self._buckets      ) * self._capacity_factor / 2
									return len(self      ) < limit
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self._buckets
									lowerCamelCase					:			Optional[Any]									= [None] * new_size
									lowerCamelCase					:			str									= 0
									for item in old_buckets:
													if item:
																	self._add_item(item.key,    item.val      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self._resize(len(self._buckets      ) * 2      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self._resize(len(self._buckets      ) // 2      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= self._get_bucket_index(A      )
									for _ in range(len(self._buckets      )      ):
													yield ind
													lowerCamelCase					:			Dict									= self._get_next_ind(A      )
					def     UpperCAmelCase_							(     self,    A,    A      ):

									"""simple docstring"""


									for ind in self._iterate_buckets(A      ):
													if self._try_set(A,    A,    A      ):
																	break
					def __setitem__(     self,    A,    A      ):

									"""simple docstring"""


									if self._is_full():
													self._size_up()

									self._add_item(A,    A      )
					def __delitem__(     self,    A      ):

									"""simple docstring"""


									for ind in self._iterate_buckets(A      ):
													lowerCamelCase					:			int									= self._buckets[ind]
													if item is None:
																	raise KeyError(A      )
													if item is _deleted:
																	continue
													if item.key == key:
																	lowerCamelCase					:			List[str]									= _deleted
																	self._len -= 1
																	break
									if self._is_sparse():
													self._size_down()
					def __getitem__(     self,    A      ):

									"""simple docstring"""


									for ind in self._iterate_buckets(A      ):
													lowerCamelCase					:			str									= self._buckets[ind]
													if item is None:
																	break
													if item is _deleted:
																	continue
													if item.key == key:
																	return item.val
									raise KeyError(A      )
					def __len__(     self      ):

									"""simple docstring"""


									return self._len
					def __iter__(     self      ):

									"""simple docstring"""


									yield from (item.key for item in self._buckets if item)




					def __repr__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= ' ,'.join(
									    F'''{item.key}: {item.val}''' for item in self._buckets if item      )
									return F'''HashMap({val_string})'''


'''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


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

A			      =      {
    '169M': 768,
    '430M': 1024,
    '1B5': 2048,
    '3B': 2560,
    '7B': 4096,
    '14B': 5120,
}





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

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

								lowerCamelCase					:			Any									= weight
				return state_dict





def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							List[str]       ,							UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							Optional[Any]=None       ,							UpperCAmelCase__							:							Tuple=None       ,							UpperCAmelCase__							:							List[Any]=False       ,							UpperCAmelCase__							:							Dict=None):
				# 1. If possible, build the tokenizer.
				if tokenizer_file is None:
								print('No `--tokenizer_file` provided, we will use the default tokenizer.')
								lowerCamelCase					:			Dict									= 5_02_77
								lowerCamelCase					:			Union[str, Any]									= AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b')
				else:
								lowerCamelCase					:			int									= PreTrainedTokenizerFast(tokenizer_file=UpperCAmelCase__)
								lowerCamelCase					:			List[Any]									= len(UpperCAmelCase__)
				tokenizer.save_pretrained(UpperCAmelCase__)

				# 2. Build the config
				lowerCamelCase					:			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:
																lowerCamelCase					:			Union[str, 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}.''')

				lowerCamelCase					:			List[Any]									= 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
				lowerCamelCase					:			Tuple									= hf_hub_download(UpperCAmelCase__       ,							UpperCAmelCase__)
				lowerCamelCase					:			Any									= torch.load(UpperCAmelCase__       ,							map_location='cpu')
				lowerCamelCase					:			str									= convert_state_dict(UpperCAmelCase__)

				# 4. Split in shards and save
				lowerCamelCase			, lowerCamelCase					:			Optional[int]									= shard_checkpoint(UpperCAmelCase__)
				for shard_file, shard in shards.items():
								torch.save(UpperCAmelCase__       ,							os.path.join(UpperCAmelCase__       ,							UpperCAmelCase__))

				if index is not None:
								lowerCamelCase					:			Dict									= os.path.join(UpperCAmelCase__       ,							UpperCAmelCase__)
								# Save the index as well
								with open(UpperCAmelCase__       ,							'w'       ,							encoding='utf-8') as f:
												lowerCamelCase					:			int									= 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.')
								lowerCamelCase					:			List[str]									= list(shards.keys())

								del state_dict
								del shards
								gc.collect()

								for shard_file in shard_files:
												lowerCamelCase					:			Dict									= 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.')
								lowerCamelCase					:			List[Any]									= AutoModelForCausalLM.from_pretrained(UpperCAmelCase__)
								model.push_to_hub(UpperCAmelCase__       ,							max_shard_size='2GB')
								tokenizer.push_to_hub(UpperCAmelCase__)


if __name__ == "__main__":
					A			      =      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.',
					)

					A			      =      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,
					)
