'''simple docstring'''
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def  _UpperCamelCase (     _a	:	int				,     _a	:	str				,     _a	:	int				,     _a	:	Dict				,     _a	:	int			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  StableDiffusionPipeline.from_pretrained(_a				,     torch_dtype=torch.floataa			)

    # load LoRA weight from .safetensors
    __UpperCamelCase	:	List[Any]		    =  load_file(_a			)

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

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

        __UpperCamelCase	:	List[str]		    =  []
        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	:	Any		    =  state_dict[pair_keys[0]].squeeze(3			).squeeze(2			).to(torch.floataa			)
            __UpperCamelCase	:	Union[str, Any]		    =  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	:	int		    =  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)




'''simple docstring'''
import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						LayoutLMTokenizer
     SCREAMING_SNAKE_CASE__		   =						LayoutLMTokenizerFast
     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						True







     def lowerCAmelCase     (		self    ):
         super().setUp()

         __UpperCamelCase	:	List[str]		    =  [
             '[UNK]',
             '[CLS]',
             '[SEP]',
             'want',
             '##want',
             '##ed',
             'wa',
             'un',
             'runn',
             '##ing',
             ',',
             'low',
             'lowest',
         ]
         __UpperCamelCase	:	Union[str, Any]		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']    )
         with open(self.vocab_file     ,			'w'     ,			encoding='utf-8'    ) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         return LayoutLMTokenizer.from_pretrained(self.tmpdirname     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  'UNwant\u00E9d,running'
         __UpperCamelCase	:	Union[str, Any]		    =  'unwanted, running'
         return input_text, output_text







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  self.tokenizer_class(self.vocab_file    )

         __UpperCamelCase	:	Optional[Any]		    =  tokenizer.tokenize('UNwant\u00E9d,running'    )
         self.assertListEqual(_lowerCamelCase     ,			['un', '##want', '##ed', ',', 'runn', '##ing']    )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase    )     ,			[7, 4, 5, 1_0, 8, 9]    )







     def lowerCAmelCase     (		self    ):
         pass



