



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

__UpperCAmelCase          =       {
    '''169M''': 768,
    '''430M''': 1_024,
    '''1B5''': 2_048,
    '''3B''': 2_560,
    '''7B''': 4_096,
    '''14B''': 5_120,
}
def 		UpperCamelCase	(   snake_case__       :    Optional[Any]	)	->     str:
   UpperCamelCase    :  List[Any]							=	list(state_dict.keys()	)
   for name in state_dict_keys:
      UpperCamelCase    :  int							=	state_dict.pop(SCREAMING_SNAKE_CASE__	)
      # emb -> embedding
      if name.startswith('emb.'	):
         UpperCamelCase    :  Union[str, Any]							=	name.replace('emb.'       ,     'embeddings.'	)
      # ln_0 -> pre_ln (only present at block 0)
      if name.startswith('blocks.0.ln0'	):
         UpperCamelCase    :  str							=	name.replace('blocks.0.ln0'       ,     'blocks.0.pre_ln'	)
      # att -> attention
      UpperCamelCase    :  List[str]							=	re.sub(R'blocks\.(\d+)\.att'       ,     R'blocks.\1.attention'       ,     SCREAMING_SNAKE_CASE__	)
      # ffn -> feed_forward
      UpperCamelCase    :  Dict							=	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'	):
         UpperCamelCase    :  List[str]							=	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    :  Dict							=	name.replace('.time_mix_r'       ,     '.time_mix_receptance'	)

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

      UpperCamelCase    :  List[str]							=	weight
   return state_dict
def 		UpperCamelCase	(   snake_case__       :    Any       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Tuple       ,     snake_case__       :    Optional[int]=None       ,     snake_case__       :    Optional[int]=None       ,     snake_case__       :    List[str]=False       ,     snake_case__       :    Optional[int]=None	)	->     Optional[int]:
   # 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]							=	50277
      UpperCamelCase    :  Optional[Any]							=	AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b'	)
   else:
      UpperCamelCase    :  Optional[int]							=	PreTrainedTokenizerFast(tokenizer_file=SCREAMING_SNAKE_CASE__	)
      UpperCamelCase    :  List[str]							=	len(SCREAMING_SNAKE_CASE__	)
   tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__	)

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

   UpperCamelCase    :  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
   UpperCamelCase    :  Optional[Any]							=	hf_hub_download(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__	)
   UpperCamelCase    :  str							=	torch.load(SCREAMING_SNAKE_CASE__       ,     map_location='cpu'	)
   UpperCamelCase    :  Tuple							=	convert_state_dict(SCREAMING_SNAKE_CASE__	)

   # 4. Split in shards and save
   UpperCamelCase	,     UpperCamelCase    :  int							=	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:
      UpperCamelCase    :  Union[str, Any]							=	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:
         UpperCamelCase    :  Dict							=	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.'	)
      UpperCamelCase    :  Optional[int]							=	list(shards.keys()	)

      del state_dict
      del shards
      gc.collect()

      for shard_file in shard_files:
         UpperCamelCase    :  Any							=	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.'	)
      UpperCamelCase    :  Any							=	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,
    )






from collections import deque

from .hash_table import HashTable





class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				int							,   *_UpperCamelCase :				int							,   **_UpperCamelCase :				Tuple							)      ->Tuple:
									super().__init__(*_UpperCamelCase							,   **_UpperCamelCase							)







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Dict							)      ->Tuple:
									snake_case_   =	deque([]							) if self.values[key] is None else self.values[key]
									self.values[key].appendleft(_UpperCamelCase							)
									snake_case_   =	self.values[key]







			def 				snake_case__(     self :				List[Any]							)      ->str:
									return (
									    sum(self.charge_factor - len(_UpperCamelCase							) for slot in self.values							)
									    / self.size_table
									    * self.charge_factor
									)







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Optional[int]=None							)      ->str:
									if not (
									    len(self.values[key]							) == self.charge_factor and self.values.count(_UpperCamelCase							) == 0
									):
															return key
									return super()._collision_resolution(_UpperCamelCase							,   _UpperCamelCase							)