import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


UpperCAmelCase_			    =							logging.get_logger(__name__)

UpperCAmelCase_			    =							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_			    =							{
    '''vocab_file''': {
        '''google/realm-cc-news-pretrained-embedder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-encoder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-scorer''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-openqa''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'''
        ),
        '''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt''',
        '''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt''',
        '''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt''',
        '''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''google/realm-cc-news-pretrained-embedder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'''
        ),
        '''google/realm-cc-news-pretrained-encoder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'''
        ),
        '''google/realm-cc-news-pretrained-scorer''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'''
        ),
        '''google/realm-cc-news-pretrained-openqa''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-nq-openqa''': (
            '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-nq-reader''': (
            '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-wq-openqa''': (
            '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-wq-reader''': (
            '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase_			    =							{
    '''google/realm-cc-news-pretrained-embedder''': 5_1_2,
    '''google/realm-cc-news-pretrained-encoder''': 5_1_2,
    '''google/realm-cc-news-pretrained-scorer''': 5_1_2,
    '''google/realm-cc-news-pretrained-openqa''': 5_1_2,
    '''google/realm-orqa-nq-openqa''': 5_1_2,
    '''google/realm-orqa-nq-reader''': 5_1_2,
    '''google/realm-orqa-wq-openqa''': 5_1_2,
    '''google/realm-orqa-wq-reader''': 5_1_2,
}

UpperCAmelCase_			    =							{
    '''google/realm-cc-news-pretrained-embedder''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-encoder''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-scorer''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-nq-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-nq-reader''': {'''do_lower_case''': True},
    '''google/realm-orqa-wq-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-wq-reader''': {'''do_lower_case''': True},
}




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    str  			=   VOCAB_FILES_NAMES
   lowerCAmelCase      :    List[Any]  			=   PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase      :    Union[str, Any]  			=   PRETRAINED_INIT_CONFIGURATION
   lowerCAmelCase      :    List[Any]  			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase      :    Optional[Any]  			=   RealmTokenizer



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



    """simple docstring"""



    super().__init__(
        _lowercase		,  tokenizer_file=_lowercase		,  do_lower_case=_lowercase		,  unk_token=_lowercase		,  sep_token=_lowercase		,  pad_token=_lowercase		,  cls_token=_lowercase		,  mask_token=_lowercase		,  tokenize_chinese_chars=_lowercase		,  strip_accents=_lowercase		,  **_lowercase		,  )

    _UpperCamelCase:     Union[str, Any]							= json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
    if (
        normalizer_state.get('''lowercase'''		,  _lowercase							) != do_lower_case
        or normalizer_state.get('''strip_accents'''		,  _lowercase							) != strip_accents
        or normalizer_state.get('''handle_chinese_chars'''		,  _lowercase							) != tokenize_chinese_chars
    ):
     _UpperCamelCase:     str							= getattr(_lowercase		,  normalizer_state.pop('''type'''							)							)
     _UpperCamelCase:     int							= do_lower_case
     _UpperCamelCase:     str							= strip_accents
     _UpperCamelCase:     Tuple							= tokenize_chinese_chars
     _UpperCamelCase:     List[Any]							= normalizer_class(**_lowercase							)

    _UpperCamelCase:     Dict							= do_lower_case



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



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= PaddingStrategy.MAX_LENGTH

    _UpperCamelCase:     str							= text
    _UpperCamelCase:     Optional[int]							= kwargs.pop('''text_pair'''		,  _lowercase							)
    _UpperCamelCase:     Tuple							= kwargs.pop('''return_tensors'''		,  _lowercase							)

    _UpperCamelCase:     str							= {
        '''input_ids''': [],
        '''attention_mask''': [],
        '''token_type_ids''': [],
    }

    for idx, candidate_text in enumerate(_lowercase							):
     if batch_text_pair is not None:
      _UpperCamelCase:     Optional[Any]							= batch_text_pair[idx]
     else:
      _UpperCamelCase:     List[Any]							= None

     _UpperCamelCase:     Union[str, Any]							= super().__call__(_lowercase		,  _lowercase		,  return_tensors=_lowercase		,  **_lowercase							)

     _UpperCamelCase:     Any							= encoded_candidates.get('''input_ids'''							)
     _UpperCamelCase:     Union[str, Any]							= encoded_candidates.get('''attention_mask'''							)
     _UpperCamelCase:     int							= encoded_candidates.get('''token_type_ids'''							)

     if encoded_input_ids is not None:
      output_data["input_ids"].append(_lowercase							)
     if encoded_attention_mask is not None:
      output_data["attention_mask"].append(_lowercase							)
     if encoded_token_type_ids is not None:
      output_data["token_type_ids"].append(_lowercase							)

    _UpperCamelCase:     Any							= {key: item for key, item in output_data.items() if len(_lowercase							) != 0}

    return BatchEncoding(_lowercase		,  tensor_type=_lowercase							)



   def   lowerCAmelCase						(	self					:  Any		,  _lowercase					:  Tuple		,  _lowercase					:  Dict=None							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

    return output



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



    """simple docstring"""



    _UpperCamelCase:     int							= [self.sep_token_id]
    _UpperCamelCase:     Optional[int]							= [self.cls_token_id]
    if token_ids_a is None:
     return len(cls + token_ids_a + sep							) * [0]
    return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]



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



    """simple docstring"""



    _UpperCamelCase:     int							= self._tokenizer.model.save(_lowercase		,  name=_lowercase							)
    return tuple(_lowercase							)
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)