



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""": 1_024,
    """1B5""": 2_048,
    """3B""": 2_560,
    """7B""": 4_096,
    """14B""": 5_120,
}





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

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

      __lowerCamelCase    				=     weight
   return state_dict





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

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

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

   if index is not None:
      __lowerCamelCase    				=     os.path.join(_UpperCamelCase			,_UpperCamelCase				)
      # Save the index as well
      with open(_UpperCamelCase			,'''w'''			,encoding='''utf-8'''				) as f:
         __lowerCamelCase    				=     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(shards.keys()				)

      del state_dict
      del shards
      gc.collect()

      for shard_file in shard_files:
         __lowerCamelCase    				=     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    				=     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,
  )











import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
  import torch


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


a_								=       logging.get_logger(__name__)
a_								=       {"""vocab_file""": """spiece.model"""}

a_								=       {
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

a_								=       {
    """AI-Sweden/gpt-sw3-126m""": 2_048,
    """AI-Sweden/gpt-sw3-350m""": 2_048,
    """AI-Sweden/gpt-sw3-1.6b""": 2_048,
    """AI-Sweden/gpt-sw3-6.7b""": 2_048,
    """AI-Sweden/gpt-sw3-20b""": 2_048,
}



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	VOCAB_FILES_NAMES
 lowerCAmelCase__							       =	PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase__							       =	["""input_ids""", """attention_mask"""]




 def __init__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase=False						,					__UpperCAmelCase=False						,					__UpperCAmelCase=False						,					__UpperCAmelCase=None						,					__UpperCAmelCase=None						,					__UpperCAmelCase=None						,					__UpperCAmelCase=None						,					__UpperCAmelCase = None						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     {} if sp_model_kwargs is None else sp_model_kwargs

    __lowerCamelCase    				=     kwargs.get('''name_or_path'''					)
    if name_or_path is None:
       logger.warning(
           '''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
           ''' you are testing the model, this can safely be ignored'''					)
       __lowerCamelCase    				=     '''None'''

    # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
    __lowerCamelCase    				=     '''<|endoftext|>''' if eos_token is None else eos_token
    __lowerCamelCase    				=     '''<unk>''' if unk_token is None else unk_token
    if "gpt-sw3-7b" in name_or_path:
       __lowerCamelCase    				=     unk_token if pad_token is None else pad_token
       __lowerCamelCase    				=     eos_token if bos_token is None else bos_token
    else:
       __lowerCamelCase    				=     '''<pad>''' if pad_token is None else pad_token
       __lowerCamelCase    				=     '''<s>''' if bos_token is None else bos_token

    super().__init__(
        do_lower_case=__UpperCAmelCase						,					remove_space=__UpperCAmelCase						,					keep_accents=__UpperCAmelCase						,					bos_token=__UpperCAmelCase						,					eos_token=__UpperCAmelCase						,					unk_token=__UpperCAmelCase						,					pad_token=__UpperCAmelCase						,					sp_model_kwargs=self.sp_model_kwargs						,					**__UpperCAmelCase						,					)

    __lowerCamelCase    				=     do_lower_case
    __lowerCamelCase    				=     remove_space
    __lowerCamelCase    				=     keep_accents
    __lowerCamelCase    				=     vocab_file

    __lowerCamelCase    				=     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
    self.sp_model.Load(__UpperCAmelCase					)

    # Used for whitespace normalization in input texts
    # fmt : off
    __lowerCamelCase    				=     {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
    # fmt : on

    # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
    __lowerCamelCase    				=     re.compile(
        F"""[{"".join(map(__UpperCAmelCase						,					list(range(0						,					9					)					) + list(range(11						,					32					)					) + list(range(127						,					160					)					) + [160, 173, 8203]					)					)}]"""					)




 def __getstate__(    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.__dict__.copy()
    __lowerCamelCase    				=     None
    return state




 def __setstate__(    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     d

    # for backward compatibility
    if not hasattr(self						,					'''sp_model_kwargs'''					):
       __lowerCamelCase    				=     {}

    __lowerCamelCase    				=     spm.SentencePieceProcessor(**self.sp_model_kwargs					)
    self.sp_model.Load(self.vocab_file					)




 @property
 # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    return len(self.sp_model					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.non_printing_characters_re.sub(''''''						,					__UpperCAmelCase					)

    # Normalize whitespaces
    __lowerCamelCase    				=     ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]					)

    # NFC Unicode normalization
    __lowerCamelCase    				=     unicodedata.normalize('''NFC'''						,					__UpperCAmelCase					)
    return text




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     self.preprocess_text(__UpperCAmelCase					)
    return self.sp_model.encode(__UpperCAmelCase						,					out_type=__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    return self.sp_model.PieceToId(__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    return self.sp_model.IdToPiece(__UpperCAmelCase					)




 @staticmethod
 def 							lowerCamelCase   (    __UpperCAmelCase					):



    '''simple docstring'''
    return out_string




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     []
    __lowerCamelCase    				=     ''''''
    __lowerCamelCase    				=     False
    for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
          # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
          if not prev_is_special:
             out_string += " "

          out_string += self.sp_model.decode(__UpperCAmelCase					) + token
          __lowerCamelCase    				=     True
          __lowerCamelCase    				=     []
       else:
          current_sub_tokens.append(__UpperCAmelCase					)
          __lowerCamelCase    				=     False
    out_string += self.sp_model.decode(__UpperCAmelCase					)

    return out_string




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     {self.convert_ids_to_tokens(__UpperCAmelCase					): i for i in range(self.vocab_size					)}
    vocab.update(self.added_tokens_encoder					)
    return vocab




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase = None					):



    '''simple docstring'''
    if not os.path.isdir(__UpperCAmelCase					):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
       return
    __lowerCamelCase    				=     os.path.join(
        __UpperCAmelCase						,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

    if os.path.abspath(self.vocab_file					) != os.path.abspath(__UpperCAmelCase					) and os.path.isfile(self.vocab_file					):
       copyfile(self.vocab_file						,					__UpperCAmelCase					)
    elif not os.path.isfile(self.vocab_file					):
       with open(__UpperCAmelCase						,					'''wb'''					) as fi:
          __lowerCamelCase    				=     self.sp_model.serialized_model_proto()
          fi.write(__UpperCAmelCase					)

    return (out_vocab_file,)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase = False					):



    '''simple docstring'''

    if isinstance(__UpperCAmelCase						,					__UpperCAmelCase					):
       __lowerCamelCase    				=     self.preprocess_text(__UpperCAmelCase					)
       __lowerCamelCase    				=     self.sp_model.encode(__UpperCAmelCase					)
    else:
       __lowerCamelCase    				=     [self.preprocess_text(__UpperCAmelCase					) for t in text]
       __lowerCamelCase    				=     self.sp_model.encode(__UpperCAmelCase					)

    if return_tensors is True or return_tensors == "pt":
       __lowerCamelCase    				=     torch.tensor(__UpperCAmelCase					)

    return token_ids




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    return self.sp_model.decode(__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
    __lowerCamelCase    				=     (
        F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(__UpperCAmelCase					) + F"""{self.bos_token}Bot:"""
    )
    return self.encode(text=__UpperCAmelCase					)






