


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)  ->    List[Any]:
       # load base model
       lowercase__:       Dict        =       StableDiffusionPipeline.from_pretrained(snake_case      ,							torch_dtype=torch.floataa					)

       # load LoRA weight from .safetensors
       lowercase__:       int        =       load_file(snake_case					)

       lowercase__:       str        =       []

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

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

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

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

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

       return pipeline


if __name__ == "__main__":
 __lowerCAmelCase			=							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			=							parser.parse_args()

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

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

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






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


__lowerCAmelCase			=							logging.get_logger(__name__)

__lowerCAmelCase			=							{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}





class   __a      (   __UpperCamelCase				):
 __lowercase			:							Any       					=			'pegasus'
 __lowercase			:							Union[str, Any]       					=			['past_key_values']
 __lowercase			:							Any       					=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
 def __init__(       self    ,			lowerCAmelCase__=50_265    ,			lowerCAmelCase__=1_024    ,			lowerCAmelCase__=12    ,			lowerCAmelCase__=4_096    ,			lowerCAmelCase__=16    ,			lowerCAmelCase__=12    ,			lowerCAmelCase__=4_096    ,			lowerCAmelCase__=16    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__="gelu"    ,			lowerCAmelCase__=1_024    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0_2    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=False    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=1    ,			lowerCAmelCase__=1    ,			**lowerCAmelCase__    ,			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       int        =       vocab_size
        lowercase__:       Optional[int]        =       max_position_embeddings
        lowercase__:       List[str]        =       d_model
        lowercase__:       Optional[Any]        =       encoder_ffn_dim
        lowercase__:       Optional[Any]        =       encoder_layers
        lowercase__:       Union[str, Any]        =       encoder_attention_heads
        lowercase__:       Optional[int]        =       decoder_ffn_dim
        lowercase__:       Tuple        =       decoder_layers
        lowercase__:       Union[str, Any]        =       decoder_attention_heads
        lowercase__:       Dict        =       dropout
        lowercase__:       List[str]        =       attention_dropout
        lowercase__:       List[str]        =       activation_dropout
        lowercase__:       Optional[int]        =       activation_function
        lowercase__:       Dict        =       init_std
        lowercase__:       Optional[Any]        =       encoder_layerdrop
        lowercase__:       List[str]        =       decoder_layerdrop
        lowercase__:       Union[str, Any]        =       use_cache
        lowercase__:       Any        =       encoder_layers
        lowercase__:       List[str]        =       scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=lowerCAmelCase__    ,			eos_token_id=lowerCAmelCase__    ,			is_encoder_decoder=lowerCAmelCase__    ,			decoder_start_token_id=lowerCAmelCase__    ,			forced_eos_token_id=lowerCAmelCase__    ,			**lowerCAmelCase__    ,			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return self.encoder_attention_heads
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return self.d_model


