
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      StableDiffusionPipeline.from_pretrained(a_			,						torch_dtype=torch.floataa				)

     # load LoRA weight from .safetensors
     UpperCAmelCase_      :							Tuple       =      load_file(a_				)

     UpperCAmelCase_      :							List[Any]       =      []

     # 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_      :							Optional[int]       =      key.split('''.'''				)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''				)[-1].split('''_'''				)
               UpperCAmelCase_      :							Optional[int]       =      pipeline.text_encoder
          else:
               UpperCAmelCase_      :							Optional[Any]       =      key.split('''.'''				)[0].split(LORA_PREFIX_UNET + '''_'''				)[-1].split('''_'''				)
               UpperCAmelCase_      :							List[str]       =      pipeline.unet

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

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

          # update weight
          if len(state_dict[pair_keys[0]].shape				) == 4:
               UpperCAmelCase_      :							Tuple       =      state_dict[pair_keys[0]].squeeze(3				).squeeze(2				).to(torch.floataa				)
               UpperCAmelCase_      :							str       =      state_dict[pair_keys[1]].squeeze(3				).squeeze(2				).to(torch.floataa				)
               curr_layer.weight.data += alpha * torch.mm(a_			,						a_				).unsqueeze(2				).unsqueeze(3				)
          else:
               UpperCAmelCase_      :							str       =      state_dict[pair_keys[0]].to(torch.floataa				)
               UpperCAmelCase_      :							Union[str, Any]       =      state_dict[pair_keys[1]].to(torch.floataa				)
               curr_layer.weight.data += alpha * torch.mm(a_			,						a_				)

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

     return pipeline


if __name__ == "__main__":
     __a          =			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=0.75, 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.)')

     __a          =			parser.parse_args()

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

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

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





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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_pegasus import PegasusTokenizer
else:
     __a          =			None


__a          =			logging.get_logger(__name__)


__a          =			'▁'

__a          =			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__a          =			{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

__a          =			{
    'google/pegasus-xsum': 512,
}

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				VOCAB_FILES_NAMES
    lowerCAmelCase        =				PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase        =				PegasusTokenizer
    lowerCAmelCase        =				['''input_ids''', '''attention_mask''']





    def __init__(				self       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE="<pad>"       ,_SCREAMING_SNAKE_CASE="</s>"       ,_SCREAMING_SNAKE_CASE="<unk>"       ,_SCREAMING_SNAKE_CASE="<mask_2>"       ,_SCREAMING_SNAKE_CASE="<mask_1>"       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=103       ,**_SCREAMING_SNAKE_CASE       ,)			->		Optional[Any]:
         UpperCAmelCase_      :							Dict       =      offset

         if additional_special_tokens is not None:
              if not isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
                   raise TypeError(
                       f'''additional_special_tokens should be of type {type(_SCREAMING_SNAKE_CASE  )}, but is'''
                       f''' {type(_SCREAMING_SNAKE_CASE  )}'''  )

              UpperCAmelCase_      :							str       =      (
                  ([mask_token_sent] + additional_special_tokens)
                  if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                  else additional_special_tokens
              )
              # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
              additional_special_tokens_extended += [
                  f'''<unk_{i}>''' for i in range(len(_SCREAMING_SNAKE_CASE  )       ,self.offset - 1  )
              ]

              if len(set(_SCREAMING_SNAKE_CASE  )  ) != len(_SCREAMING_SNAKE_CASE  ):
                   raise ValueError(
                       '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                       f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''  )
              UpperCAmelCase_      :							int       =      additional_special_tokens_extended
         else:
              UpperCAmelCase_      :							Any       =      [mask_token_sent] if mask_token_sent is not None else []
              additional_special_tokens += [f'''<unk_{i}>''' for i in range(2       ,self.offset  )]

         super().__init__(
             _SCREAMING_SNAKE_CASE       ,tokenizer_file=_SCREAMING_SNAKE_CASE       ,pad_token=_SCREAMING_SNAKE_CASE       ,eos_token=_SCREAMING_SNAKE_CASE       ,unk_token=_SCREAMING_SNAKE_CASE       ,mask_token=_SCREAMING_SNAKE_CASE       ,mask_token_sent=_SCREAMING_SNAKE_CASE       ,offset=_SCREAMING_SNAKE_CASE       ,additional_special_tokens=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE       ,)
         UpperCAmelCase_      :							str       =      vocab_file
         UpperCAmelCase_      :							Dict       =      False if not self.vocab_file else True





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Any:
         UpperCAmelCase_      :							Dict       =      set(self.all_special_ids  )  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special

         if all_special_ids != set(range(len(self.additional_special_tokens  ) + 3  )  ):
              raise ValueError(
                  '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
                  f''' {len(self.additional_special_tokens  )} additional_special_tokens, but got {all_special_ids}'''  )

         return [1 if x in all_special_ids else 0 for x in seq]





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = False  )			->		List[int]:
         if already_has_special_tokens:
              return self._special_token_mask(_SCREAMING_SNAKE_CASE  )
         elif token_ids_a is None:
              return self._special_token_mask(_SCREAMING_SNAKE_CASE  ) + [1]
         else:
              return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]





    def 							a__				(				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 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		Tuple[str]:
         if not self.can_save_slow_tokenizer:
              raise ValueError(
                  '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                  '''tokenizer.'''  )

         if not os.path.isdir(_SCREAMING_SNAKE_CASE  ):
              logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
              return
         UpperCAmelCase_      :							Optional[int]       =      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  ):
              copyfile(self.vocab_file       ,_SCREAMING_SNAKE_CASE  )

         return (out_vocab_file,)