






"""simple docstring"""







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			StableDiffusionPipeline.from_pretrained(_UpperCamelCase	,      torch_dtype=torch.floataa		)

       # load LoRA weight from .safetensors
       UpperCAmelCase	    =			load_file(_UpperCamelCase		)

       UpperCAmelCase	    =			[]

       # 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	    =			key.split("""."""		)[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""		)[-1].split("""_"""		)
                     UpperCAmelCase	    =			pipeline.text_encoder
              else:
                     UpperCAmelCase	    =			key.split("""."""		)[0].split(LORA_PREFIX_UNET + """_"""		)[-1].split("""_"""		)
                     UpperCAmelCase	    =			pipeline.unet

              # find the target layer
              UpperCAmelCase	    =			layer_infos.pop(0		)
              while len(_UpperCamelCase		) > -1:
                     try:
                            UpperCAmelCase	    =			curr_layer.__getattr__(_UpperCamelCase		)
                            if len(_UpperCamelCase		) > 0:
                                   UpperCAmelCase	    =			layer_infos.pop(0		)
                            elif len(_UpperCamelCase		) == 0:
                                   break
                     except Exception:
                            if len(_UpperCamelCase		) > 0:
                                   temp_name += "_" + layer_infos.pop(0		)
                            else:
                                   UpperCAmelCase	    =			layer_infos.pop(0		)

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

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

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

       return pipeline


if __name__ == "__main__":
 lowerCAmelCase_	:    Optional[Any]            =      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.)''')

 lowerCAmelCase_	:    Union[str, Any]            =      parser.parse_args()

 lowerCAmelCase_	:    Any            =      args.base_model_path
 lowerCAmelCase_	:    List[Any]            =      args.checkpoint_path
 lowerCAmelCase_	:    str            =      args.dump_path
 lowerCAmelCase_	:    str            =      args.lora_prefix_unet
 lowerCAmelCase_	:    List[Any]            =      args.lora_prefix_text_encoder
 lowerCAmelCase_	:    int            =      args.alpha

 lowerCAmelCase_	:    Union[str, Any]            =      convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

 lowerCAmelCase_	:    Optional[int]            =      pipe.to(args.device)
 pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)








"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_	:    Any            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Any            =      {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}





class   UpperCamelCase_ (       a_  ):
  _A       :					Tuple				=	'canine'



  def __init__(      self ,		snake_case__=7_68 ,		snake_case__=12 ,		snake_case__=12 ,		snake_case__=30_72 ,		snake_case__="gelu" ,		snake_case__=0.1 ,		snake_case__=0.1 ,		snake_case__=1_63_84 ,		snake_case__=16 ,		snake_case__=0.02 ,		snake_case__=1e-12 ,		snake_case__=0 ,		snake_case__=0xE_0_0_0 ,		snake_case__=0xE_0_0_1 ,		snake_case__=4 ,		snake_case__=4 ,		snake_case__=8 ,		snake_case__=1_63_84 ,		snake_case__=1_28 ,		**snake_case__ ,		)			->			int:



         """simple docstring"""





         super().__init__(pad_token_id=snake_case__ ,		bos_token_id=snake_case__ ,		eos_token_id=snake_case__ ,		**snake_case__				)

         UpperCAmelCase	    =			max_position_embeddings
         UpperCAmelCase	    =			hidden_size
         UpperCAmelCase	    =			num_hidden_layers
         UpperCAmelCase	    =			num_attention_heads
         UpperCAmelCase	    =			intermediate_size
         UpperCAmelCase	    =			hidden_act
         UpperCAmelCase	    =			hidden_dropout_prob
         UpperCAmelCase	    =			attention_probs_dropout_prob
         UpperCAmelCase	    =			initializer_range
         UpperCAmelCase	    =			type_vocab_size
         UpperCAmelCase	    =			layer_norm_eps

         # Character config:
         UpperCAmelCase	    =			downsampling_rate
         UpperCAmelCase	    =			upsampling_kernel_size
         UpperCAmelCase	    =			num_hash_functions
         UpperCAmelCase	    =			num_hash_buckets
         UpperCAmelCase	    =			local_transformer_stride
