import io
import os
import unicodedata
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''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}

UpperCAmelCase_			    =							{
    '''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
    '''vocab_file''': '''vocab.txt''',
}

UpperCAmelCase_			    =							{
    '''vocab_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
    },
    '''sentencepiece_model_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
    },
}

UpperCAmelCase_			    =							{
    '''ernie-m-base''': 5_1_4,
    '''ernie-m-large''': 5_1_4,
}

UpperCAmelCase_			    =							{
    '''ernie-m-base''': {'''do_lower_case''': False},
    '''ernie-m-large''': {'''do_lower_case''': False},
}




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    List[str]  			=   ["input_ids"]

   lowerCAmelCase      :    Tuple  			=   VOCAB_FILES_NAMES
   lowerCAmelCase      :    Dict  			=   PRETRAINED_INIT_CONFIGURATION
   lowerCAmelCase      :    int  			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase      :    int  			=   PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase      :    Optional[Any]  			=   RESOURCE_FILES_NAMES



   def __init__(	self					:  List[Any]		,  _lowercase					:  Tuple		,  _lowercase					:  Any=None		,  _lowercase					:  List[str]=False		,  _lowercase					:  Any="utf8"		,  _lowercase					:  str="[UNK]"		,  _lowercase					:  Dict="[SEP]"		,  _lowercase					:  Union[str, Any]="[PAD]"		,  _lowercase					:  Tuple="[CLS]"		,  _lowercase					:  int="[MASK]"		,  _lowercase					:  Optional[Dict[str, Any]] = None		,  **_lowercase					:  Optional[int]		,  ):



    """simple docstring"""



    _UpperCamelCase:     str							= {} if sp_model_kwargs is None else sp_model_kwargs
    super().__init__(
        do_lower_case=_lowercase		,  unk_token=_lowercase		,  sep_token=_lowercase		,  pad_token=_lowercase		,  cls_token=_lowercase		,  mask_token=_lowercase		,  vocab_file=_lowercase		,  encoding=_lowercase		,  sp_model_kwargs=self.sp_model_kwargs		,  **_lowercase		,  )
    _UpperCamelCase:     Optional[Any]							= do_lower_case
    _UpperCamelCase:     str							= sentencepiece_model_ckpt
    _UpperCamelCase:     Dict							= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
    self.sp_model.Load(_lowercase							)

    # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
    if vocab_file is not None:
     _UpperCamelCase:     Any							= self.load_vocab(filepath=_lowercase							)
    else:
     _UpperCamelCase:     Optional[Any]							= {self.sp_model.id_to_piece(_lowercase							): id for id in range(self.sp_model.get_piece_size()							)}
    _UpperCamelCase:     List[Any]							= {v: k for k, v in self.vocab.items()}



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  str							):



    """simple docstring"""



    if text is None:
     return None

    _UpperCamelCase:     List[str]							= self.tokenize(_lowercase							)
    _UpperCamelCase:     List[str]							= '''''', []

    for i, ch in enumerate(_lowercase							):
     if ch in self.SP_CHAR_MAPPING:
      _UpperCamelCase:     Optional[int]							= self.SP_CHAR_MAPPING.get(_lowercase							)
     else:
      _UpperCamelCase:     List[str]							= unicodedata.normalize('''NFKC'''		,  _lowercase							)
     if self.is_whitespace(_lowercase							):
      continue
     normalized_text += ch
     char_mapping.extend([i] * len(_lowercase							)							)

    _UpperCamelCase:     str							= normalized_text, [], 0

    if self.do_lower_case:
     _UpperCamelCase:     int							= text.lower()

    for token in split_tokens:
     if token[:1] == "▁":
      _UpperCamelCase:     Union[str, Any]							= token[1:]
     _UpperCamelCase:     Optional[int]							= text[offset:].index(_lowercase							) + offset
     _UpperCamelCase:     Any							= start + len(_lowercase							)

     token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)							)
     _UpperCamelCase:     Any							= end
    return token_mapping



   @property
   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    return len(self.vocab							)



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    return dict(self.vocab		,  **self.added_tokens_encoder							)



   def __getstate__(	self					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= self.__dict__.copy()
    _UpperCamelCase:     Union[str, Any]							= None
    return state



   def __setstate__(	self					:  List[Any]		,  _lowercase					:  int							):



    """simple docstring"""



    _UpperCamelCase:     str							= d

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

    _UpperCamelCase:     str							= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
    self.sp_model.Load(self.sentencepiece_model_ckpt							)



   def   lowerCAmelCase						(	self					:  Tuple		,  _lowercase					:  Optional[int]							):



    """simple docstring"""



    return "".join((self.SP_CHAR_MAPPING.get(_lowercase		,  _lowercase							) for c in text)							)



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  str		,  _lowercase					:  Union[str, Any]=False		,  _lowercase					:  List[Any]=64		,  _lowercase					:  Optional[Any]=0.1							):



    """simple docstring"""



    if self.sp_model_kwargs.get('''enable_sampling'''							) is True:
     _UpperCamelCase:     Dict							= True
    if self.sp_model_kwargs.get('''alpha'''							) is not None:
     _UpperCamelCase:     Any							= self.sp_model_kwargs.get('''alpha'''							)
    if self.sp_model_kwargs.get('''nbest_size'''							) is not None:
     _UpperCamelCase:     int							= self.sp_model_kwargs.get('''nbest_size'''							)

    if not enable_sampling:
     _UpperCamelCase:     Union[str, Any]							= self.sp_model.EncodeAsPieces(_lowercase							)
    else:
     _UpperCamelCase:     int							= self.sp_model.SampleEncodeAsPieces(_lowercase		,  _lowercase		,  _lowercase							)
    _UpperCamelCase:     int							= []
    for pi, piece in enumerate(_lowercase							):
     if piece == SPIECE_UNDERLINE:
      if not pieces[pi + 1].startswith(_lowercase							) and pi != 0:
       new_pieces.append(_lowercase							)
       continue
      else:
       continue
     _UpperCamelCase:     Any							= 0
     for i, chunk in enumerate(_lowercase							):
      if chunk == SPIECE_UNDERLINE:
       continue
      if self.is_ch_char(_lowercase							) or self.is_punct(_lowercase							):
       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
        new_pieces.append(piece[lst_i:i]							)
       new_pieces.append(_lowercase							)
       _UpperCamelCase:     List[Any]							= i + 1
      elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
        new_pieces.append(piece[lst_i:i]							)
       _UpperCamelCase:     Union[str, Any]							= i
      elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
        new_pieces.append(piece[lst_i:i]							)
       _UpperCamelCase:     Optional[Any]							= i
     if len(_lowercase							) > lst_i:
      new_pieces.append(piece[lst_i:]							)
    return new_pieces



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  Tuple							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= ''''''.join(_lowercase							).replace(_lowercase		,  ''' '''							).strip()
    return out_string



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  str							):



    """simple docstring"""



    _UpperCamelCase:     str							= self.convert_ids_to_tokens(_lowercase							)
    _UpperCamelCase:     Dict							= ''''''.join(_lowercase							).replace(_lowercase		,  ''' '''							).strip()
    return out_string



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  Dict							):



    """simple docstring"""



    return self.vocab.get(_lowercase		,  self.vocab.get(self.unk_token							)							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  str							):



    """simple docstring"""



    return self.reverse_vocab.get(_lowercase		,  self.unk_token							)



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  List[Any]		,  _lowercase					:  Any=None							):



    """simple docstring"""



    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    _UpperCamelCase:     Any							= [self.cls_token_id]
    _UpperCamelCase:     str							= [self.sep_token_id]
    return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  int		,  _lowercase					:  Tuple=None							):



    """simple docstring"""



    if offset_mapping_a is None:
     return [(0, 0)] + offset_mapping_a + [(0, 0)]

    return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  int		,  _lowercase					:  Union[str, Any]=None		,  _lowercase					:  List[str]=False							):



    """simple docstring"""



    if already_has_special_tokens:
     if token_ids_a is not None:
      raise ValueError(
          '''You should not supply a second sequence if the provided sequence of '''
          '''ids is already formatted with special tokens for the model.'''							)
     return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

    if token_ids_a is not None:
     return [1] + ([0] * len(_lowercase							)) + [1, 1] + ([0] * len(_lowercase							)) + [1]
    return [1] + ([0] * len(_lowercase							)) + [1]



   def   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  List[int]		,  _lowercase					:  Optional[List[int]] = None							):



    """simple docstring"""



    if token_ids_a is None:
     # [CLS] X [SEP]
     return (len(_lowercase							) + 2) * [0]

    # [CLS] A [SEP] [SEP] B [SEP]
    return [0] * (len(_lowercase							) + 1) + [1] * (len(_lowercase							) + 3)



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



    if "\u4e00" <= char <= "\u9fff":
     return True
    return False



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  List[str]							):



    """simple docstring"""



    if ("a" <= char <= "z") or ("A" <= char <= "Z"):
     return True
    return False



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  List[Any]							):



    """simple docstring"""



    if char in ",;:.?!~，；：。？！《》【】":
     return True
    return False



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  List[Any]							):



    """simple docstring"""



    if char == " " or char == "\t" or char == "\n" or char == "\r":
     return True
    if len(_lowercase							) == 1:
     _UpperCamelCase:     Dict							= unicodedata.category(_lowercase							)
     if cat == "Zs":
      return True
    return False



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase:     str							= {}
    with io.open(_lowercase		,  '''r'''		,  encoding='''utf-8'''							) as f:
     for index, line in enumerate(_lowercase							):
      _UpperCamelCase:     List[Any]							= line.rstrip('''\n'''							)
      _UpperCamelCase:     Union[str, Any]							= int(_lowercase							)

    return token_to_idx



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  str		,  _lowercase					:  Optional[str] = None							):



    """simple docstring"""



    _UpperCamelCase:     Tuple							= 0
    if os.path.isdir(_lowercase							):
     _UpperCamelCase:     Optional[int]							= os.path.join(
         _lowercase		,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
    else:
     _UpperCamelCase:     Any							= (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
    with open(_lowercase		,  '''w'''		,  encoding='''utf-8'''							) as writer:
     for token, token_index in sorted(self.vocab.items()		,  key=lambda _lowercase							: kv[1]							):
      if index != token_index:
       logger.warning(
           f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
           ''' Please check that the vocabulary is not corrupted!'''							)
       _UpperCamelCase:     int							= token_index
      writer.write(token + '''\n'''							)
      index += 1

    _UpperCamelCase:     Optional[Any]							= os.path.join(_lowercase		,  '''sentencepiece.bpe.model'''							)
    with open(_lowercase		,  '''wb'''							) as fi:
     _UpperCamelCase:     Optional[Any]							= self.sp_model.serialized_model_proto()
     fi.write(_lowercase							)

    return (vocab_file,)
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def       lowerCAmelCase_     (		lowercase:							Optional[int]  ,					lowercase:							Any  ,					lowercase:							str  ,					lowercase:							List[str]  ,					lowercase:							Optional[int]		)    ->				int:







 '''simple docstring'''
 # load base model
 _UpperCamelCase:     Optional[int]							= StableDiffusionPipeline.from_pretrained(lowercase  ,					torch_dtype=torch.floataa		)

 # load LoRA weight from .safetensors
 _UpperCamelCase:     List[str]							= load_file(lowercase		)

 _UpperCamelCase:     Optional[int]							= []

 # directly update weight in diffusers model
 for key in state_dict:
  # it is suggested to print out the key, it usually will be something like below
  # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

  # as we have set the alpha beforehand, so just skip
  if ".alpha" in key or key in visited:
   continue

  if "text" in key:
   _UpperCamelCase:     int							= key.split('''.'''		)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''		)[-1].split('''_'''		)
   _UpperCamelCase:     str							= pipeline.text_encoder
  else:
   _UpperCamelCase:     Optional[Any]							= key.split('''.'''		)[0].split(LORA_PREFIX_UNET + '''_'''		)[-1].split('''_'''		)
   _UpperCamelCase:     Any							= pipeline.unet

  # find the target layer
  _UpperCamelCase:     Union[str, Any]							= layer_infos.pop(0		)
  while len(lowercase		) > -1:
   try:
    _UpperCamelCase:     Union[str, Any]							= curr_layer.__getattr__(lowercase		)
    if len(lowercase		) > 0:
     _UpperCamelCase:     Tuple							= layer_infos.pop(0		)
    elif len(lowercase		) == 0:
     break
   except Exception:
    if len(lowercase		) > 0:
     temp_name += "_" + layer_infos.pop(0		)
    else:
     _UpperCamelCase:     List[Any]							= layer_infos.pop(0		)

  _UpperCamelCase:     Any							= []
  if "lora_down" in key:
   pair_keys.append(key.replace('''lora_down'''  ,					'''lora_up'''		)		)
   pair_keys.append(lowercase		)
  else:
   pair_keys.append(lowercase		)
   pair_keys.append(key.replace('''lora_up'''  ,					'''lora_down'''		)		)

  # update weight
  if len(state_dict[pair_keys[0]].shape		) == 4:
   _UpperCamelCase:     str							= state_dict[pair_keys[0]].squeeze(3		).squeeze(2		).to(torch.floataa		)
   _UpperCamelCase:     Optional[int]							= state_dict[pair_keys[1]].squeeze(3		).squeeze(2		).to(torch.floataa		)
   curr_layer.weight.data += alpha * torch.mm(lowercase  ,					lowercase		).unsqueeze(2		).unsqueeze(3		)
  else:
   _UpperCamelCase:     str							= state_dict[pair_keys[0]].to(torch.floataa		)
   _UpperCamelCase:     Dict							= state_dict[pair_keys[1]].to(torch.floataa		)
   curr_layer.weight.data += alpha * torch.mm(lowercase  ,					lowercase		)

  # update visited list
  for item in pair_keys:
   visited.append(lowercase		)

 return pipeline


if __name__ == "__main__":
  UpperCAmelCase_			    =							argparse.ArgumentParser()

  parser.add_argument(
      '''--base_model_path''', default=None, type=str, required=True, help='''Path to the base model in diffusers format.'''
  )
  parser.add_argument(
      '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
  )
  parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
  parser.add_argument(
      '''--lora_prefix_unet''', default='''lora_unet''', type=str, help='''The prefix of UNet weight in safetensors'''
  )
  parser.add_argument(
      '''--lora_prefix_text_encoder''',
      default='''lora_te''',
      type=str,
      help='''The prefix of text encoder weight in safetensors''',
  )
  parser.add_argument('''--alpha''', default=0.7_5, type=float, help='''The merging ratio in W = W0 + alpha * deltaW''')
  parser.add_argument(
      '''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.'''
  )
  parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')

  UpperCAmelCase_			    =							parser.parse_args()

  UpperCAmelCase_			    =							args.base_model_path
  UpperCAmelCase_			    =							args.checkpoint_path
  UpperCAmelCase_			    =							args.dump_path
  UpperCAmelCase_			    =							args.lora_prefix_unet
  UpperCAmelCase_			    =							args.lora_prefix_text_encoder
  UpperCAmelCase_			    =							args.alpha

  UpperCAmelCase_			    =							convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  UpperCAmelCase_			    =							pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)