



'''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


_UpperCamelCase											=		logging.get_logger(__name__)


_UpperCamelCase											=		'''▁'''

_UpperCamelCase											=		{'''vocab_file''': '''spiece.model'''}

_UpperCamelCase											=		{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

_UpperCamelCase											=		{
    '''google/reformer-crime-and-punishment''': 524288,
}






class    lowerCamelCase_       (							__snake_case	):
  """simple docstring"""




  a_				   =VOCAB_FILES_NAMES
  a_				   =PRETRAINED_VOCAB_FILES_MAP
  a_				   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_				   =["""input_ids""", """attention_mask"""]




  def __init__(					self :      List[Any]     ,      _a :      Union[str, Any]     ,      _a :      List[Any]="</s>"     ,      _a :      List[str]="<unk>"     ,      _a :      List[str]=[]     ,      _a :      Optional[Dict[str, Any]] = None     ,      **_a :      str     ,      )   ->   Any:
    __lowerCamelCase			:			Dict       			=					{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        eos_token=_SCREAMING_SNAKE_CASE     ,      unk_token=_SCREAMING_SNAKE_CASE     ,      additional_special_tokens=_SCREAMING_SNAKE_CASE     ,      sp_model_kwargs=self.sp_model_kwargs     ,      **_SCREAMING_SNAKE_CASE     ,      )

    __lowerCamelCase			:			List[str]       			=					vocab_file
    __lowerCamelCase			:			str       			=					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
    self.sp_model.Load(_SCREAMING_SNAKE_CASE  )




  @property
  def 		_lowercase	(					self :      List[Any]  )   ->   Union[str, Any]:
    return self.sp_model.get_piece_size()




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




  def __setstate__(					self :      Optional[int]     ,      _a :      int  )   ->   List[Any]:
    __lowerCamelCase			:			Any       			=					d

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

    __lowerCamelCase			:			List[Any]       			=					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
    self.sp_model.Load(self.vocab_file  )




  def 		_lowercase	(					self :      List[Any]     ,      _a :      str  )   ->   Optional[int]:
    return self.sp_model.encode(_SCREAMING_SNAKE_CASE     ,      out_type=_SCREAMING_SNAKE_CASE  )




  def 		_lowercase	(					self :      List[Any]     ,      _a :      Any  )   ->   List[Any]:
    return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE  )




  def 		_lowercase	(					self :      List[str]     ,      _a :      List[str]  )   ->   Dict:




    if index < self.sp_model.get_piece_size():
      __lowerCamelCase			:			str       			=					self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE  )
    return token




  def 		_lowercase	(					self :      int     ,      _a :      Dict  )   ->   Optional[Any]:
    __lowerCamelCase			:			Optional[Any]       			=					[]
    __lowerCamelCase			:			Optional[Any]       			=					''
    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			:			Any       			=					[]
      else:
        current_sub_tokens.append(_SCREAMING_SNAKE_CASE  )
    out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE  )
    return out_string.strip()




  def 		_lowercase	(					self :      Dict     ,      _a :      str     ,      _a :      Optional[str] = None  )   ->   Union[str, Any]:




    if not os.path.isdir(_SCREAMING_SNAKE_CASE  ):
      logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
      return
    __lowerCamelCase			:			Union[str, Any]       			=					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			:			Dict       			=					self.sp_model.serialized_model_proto()
        fi.write(_SCREAMING_SNAKE_CASE  )

    return (out_vocab_file,)














import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__   :  str        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			int:
      """simple docstring"""

      UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')
      if "model" in sd.keys():
            UpperCamelCase					       =  torch.load(_UpperCamelCase       ,							map_location='cpu')['model']

      # pop unnecessary weights
      UpperCamelCase					       =  [
          'decoder.version',
          'decoder.output_projection.weight',
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  {
          'decoder.project_in_dim.weight': 'decoder.project_in.weight',
          'decoder.project_out_dim.weight': 'decoder.project_out.weight',
          'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
          'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  UpperCamelCase					       =  sd.pop(_UpperCamelCase)

      UpperCamelCase					       =  list(sd.keys())
      for key in keys:
            if ".qkv_proj." in key:
                  UpperCamelCase					       =  sd[key]
                  # We split QKV in separate Q,K,V

                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.q_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.k_proj.')
                  UpperCamelCase					       =  key.replace('.qkv_proj.'       ,							'.v_proj.')

                  UpperCamelCase					       =  value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  torch.split(_UpperCamelCase       ,							depth // 3       ,							dim=0)

                  UpperCamelCase					       =  q
                  UpperCamelCase					       =  k
                  UpperCamelCase					       =  v
                  del sd[key]

      return sd
@torch.no_grad()
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=None) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  load_checkpoint(_UpperCamelCase)

      if config is not None:
            UpperCamelCase					       =  OPTConfig.from_pretrained(_UpperCamelCase)
      else:
            UpperCamelCase					       =  OPTConfig()

      UpperCamelCase					       =  OPTModel(_UpperCamelCase).half().eval()
      model.load_state_dict(_UpperCamelCase)

      # Check results
      Path(_UpperCamelCase).mkdir(exist_ok=_UpperCamelCase)
      model.save_pretrained(_UpperCamelCase)


if __name__ == "__main__":
       __magic_name__   :  Any        =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--fairseq_path''',
           type=str,
           help=(
               '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
               ''' https://huggingface.co/models?other=opt_metasq'''
           ),
       )
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
       __magic_name__   :  str        =							parser.parse_args()
       convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



