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

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

          if name != "head.weight":
               lowercase_					:       Optional[Any]     				=					'rwkv.' + name

          lowercase_					:       Any     				=					weight
     return state_dict

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

     # 2. Build the config
     lowercase_					:       Optional[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:
                    lowercase_					:       Optional[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}.'''							)

     lowercase_					:       List[str]     				=					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
     lowercase_					:       str     				=					hf_hub_download(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
     lowercase_					:       List[str]     				=					torch.load(SCREAMING_SNAKE_CASE_  ,       map_location='cpu'							)
     lowercase_					:       str     				=					convert_state_dict(SCREAMING_SNAKE_CASE_							)

     # 4. Split in shards and save
     lowercase_				,lowercase_					:       Union[str, 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:
          lowercase_					:       Tuple     				=					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:
               lowercase_					:       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.'							)
          lowercase_					:       Union[str, Any]     				=					list(shards.keys()							)

          del state_dict
          del shards
          gc.collect()

          for shard_file in shard_files:
               lowercase_					:       Optional[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.'							)
          lowercase_					:       List[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__":
 _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,
 )





'''simple docstring'''




import math

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5  ,       int(math.sqrt(SCREAMING_SNAKE_CASE_							) + 1							)  ,       6							):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_ = 10_001							):

     try:
          lowercase_					:       List[Any]     				=					int(SCREAMING_SNAKE_CASE_							)
     except (TypeError, ValueError):
          raise TypeError('Parameter nth must be int or castable to int.'							) from None
     if nth <= 0:
          raise ValueError('Parameter nth must be greater than or equal to one.'							)
     lowercase_					:       list[int]     				=					[]
     lowercase_					:       Optional[int]     				=					2
     while len(SCREAMING_SNAKE_CASE_							) < nth:
          if is_prime(SCREAMING_SNAKE_CASE_							):
               primes.append(SCREAMING_SNAKE_CASE_							)
               num += 1
          else:
               num += 1
     return primes[len(SCREAMING_SNAKE_CASE_							) - 1]


if __name__ == "__main__":
 print(F"""{solution() = }""")




