


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''': 1024,
    '''1B5''': 2048,
    '''3B''': 2560,
    '''7B''': 4096,
    '''14B''': 5120,
}
def 				UpperCAmelCase_					(							__snake_case						)     ->			List[Any]:







 """simple docstring"""


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

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

  _lowercase							      =weight
 return state_dict
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=None     ,						__snake_case=None     ,						__snake_case=False     ,						__snake_case=None						)     ->			int:







 """simple docstring"""


 if tokenizer_file is None:
  print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''						)
  _lowercase							      =50277
  _lowercase							      =AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''						)
 else:
  _lowercase							      =PreTrainedTokenizerFast(tokenizer_file=__snake_case						)
  _lowercase							      =len(__snake_case						)
 tokenizer.save_pretrained(__snake_case						)

 # 2. Build the config
 _lowercase							      =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							      =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							      =RwkvConfig(
     vocab_size=__snake_case     ,						num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]     ,						hidden_size=HIDEN_SIZE_MAPPING[size]     ,						)
 config.save_pretrained(__snake_case						)

 # 3. Download model file then convert state_dict
 _lowercase							      =hf_hub_download(__snake_case     ,						__snake_case						)
 _lowercase							      =torch.load(__snake_case     ,						map_location='''cpu'''						)
 _lowercase							      =convert_state_dict(__snake_case						)

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

 if index is not None:
  _lowercase							      =os.path.join(__snake_case     ,						__snake_case						)
  # Save the index as well
  with open(__snake_case     ,						'''w'''     ,						encoding='''utf-8'''						) as f:
   _lowercase							      =json.dumps(__snake_case     ,						indent=2     ,						sort_keys=__snake_case						) + '''\n'''
   f.write(__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							      =list(shards.keys()						)

  del state_dict
  del shards
  gc.collect()

  for shard_file in shard_files:
   _lowercase							      =torch.load(os.path.join(__snake_case     ,						__snake_case						)						)
   torch.save({k: v.cpu().clone() for k, v in state_dict.items()}     ,						os.path.join(__snake_case     ,						__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							      =AutoModelForCausalLM.from_pretrained(__snake_case						)
  model.push_to_hub(__snake_case     ,						max_shard_size='''2GB'''						)
  tokenizer.push_to_hub(__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,
  )





'''simple docstring'''



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A__		:					Tuple												=  logging.get_logger(__name__)

A__		:					List[str]												=  {"""vocab_file""": """spm_char.model"""}

A__		:					str												=  {
    """vocab_file""": {
        """microsoft/speecht5_asr""": """https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model""",
        """microsoft/speecht5_tts""": """https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model""",
        """microsoft/speecht5_vc""": """https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model""",
    }
}

A__		:					List[Any]												=  {
    """microsoft/speecht5_asr""": 1024,
    """microsoft/speecht5_tts""": 1024,
    """microsoft/speecht5_vc""": 1024,
}





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					Optional[Any]							     =			VOCAB_FILES_NAMES
    lowerCamelCase  :					Any							     =			PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  :					Union[str, Any]							     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  :					int							     =			['input_ids', 'attention_mask']







    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_="<s>"			,     SCREAMING_SNAKE_CASE_="</s>"			,     SCREAMING_SNAKE_CASE_="<unk>"			,     SCREAMING_SNAKE_CASE_="<pad>"			,     SCREAMING_SNAKE_CASE_ = None			,     **SCREAMING_SNAKE_CASE_			,     )				->     None:
         __lowerCamelCase   :	Any								=		{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=SCREAMING_SNAKE_CASE_			,     eos_token=SCREAMING_SNAKE_CASE_			,     unk_token=SCREAMING_SNAKE_CASE_			,     pad_token=SCREAMING_SNAKE_CASE_			,     sp_model_kwargs=self.sp_model_kwargs			,     **SCREAMING_SNAKE_CASE_			,     )

         __lowerCamelCase   :	List[Any]								=		vocab_file

         __lowerCamelCase   :	str								=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(SCREAMING_SNAKE_CASE_	)







    @property
    def       lowercase_				(							self	)				->     List[str]:
         return self.sp_model.get_piece_size()







    def       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	str								=		{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	): i for i in range(self.vocab_size	)}
         vocab.update(self.added_tokens_encoder	)
         return vocab







    def __getstate__(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	Dict								=		self.__dict__.copy()
         __lowerCamelCase   :	int								=		None
         return state







    def __setstate__(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         __lowerCamelCase   :	Union[str, Any]								=		d

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

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







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[str]:
         return self.sp_model.encode(SCREAMING_SNAKE_CASE_			,     out_type=SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     str:
         return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     Dict:
         __lowerCamelCase   :	Any								=		self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_	)
         return token







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[Any]:
         __lowerCamelCase   :	Union[str, Any]								=		[]
         __lowerCamelCase   :	str								=		''
         for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                   out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	) + token
                   __lowerCamelCase   :	int								=		[]
              else:
                   current_sub_tokens.append(SCREAMING_SNAKE_CASE_	)
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_	)
         return out_string.strip()







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None	)				->     List[int]:
         if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None			,     SCREAMING_SNAKE_CASE_ = False	)				->     List[int]:
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=SCREAMING_SNAKE_CASE_			,     token_ids_a=SCREAMING_SNAKE_CASE_			,     already_has_special_tokens=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		[1]
         if token_ids_a is None:
              return ([0] * len(SCREAMING_SNAKE_CASE_	)) + suffix_ones
         return ([0] * len(SCREAMING_SNAKE_CASE_	)) + ([0] * len(SCREAMING_SNAKE_CASE_	)) + suffix_ones







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = None	)				->     Tuple[str]:
         if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
              return
         __lowerCamelCase   :	Tuple								=		os.path.join(
             SCREAMING_SNAKE_CASE_			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

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

         return (out_vocab_file,)






