


'''simple docstring'''




import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
UpperCAmelCase_      :		Any          =		logging.getLogger(__name__)


UpperCAmelCase_      :		int          =		'Hello world! cécé herlolip'


UpperCAmelCase_      :		Optional[int]          =		namedtuple(
    "BertAbsConfig",
    [
        "temp_dir",
        "large",
        "use_bert_emb",
        "finetune_bert",
        "encoder",
        "share_emb",
        "max_pos",
        "enc_layers",
        "enc_hidden_size",
        "enc_heads",
        "enc_ff_size",
        "enc_dropout",
        "dec_layers",
        "dec_hidden_size",
        "dec_heads",
        "dec_ff_size",
        "dec_dropout",
    ],
)


def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   _a					:  List[str]						=  BertAbsConfig(
       temp_dir='.'		,	finetune_bert=A		,	large=A		,	share_emb=A		,	use_bert_emb=A		,	encoder='bert'		,	max_pos=5_1_2		,	enc_layers=6		,	enc_hidden_size=5_1_2		,	enc_heads=8		,	enc_ff_size=5_1_2		,	enc_dropout=0.2		,	dec_layers=6		,	dec_hidden_size=7_6_8		,	dec_heads=8		,	dec_ff_size=2_0_4_8		,	dec_dropout=0.2		,	)
   _a					:  str						=  torch.load(A		,	lambda A		,	A      : storage      )
   _a					:  int						=  AbsSummarizer(A		,	torch.device('cpu'      )		,	A      )
   original.eval()

   _a					:  str						=  BertAbsSummarizer(A		,	torch.device('cpu'      )      )
   new_model.eval()

   # -------------------
   # Convert the weights
   # -------------------

   logging.info('convert the model'      )
   new_model.bert.load_state_dict(original.bert.state_dict()      )
   new_model.decoder.load_state_dict(original.decoder.state_dict()      )
   new_model.generator.load_state_dict(original.generator.state_dict()      )

   # ----------------------------------
   # Make sure the outpus are identical
   # ----------------------------------

   logging.info('Make sure that the models\' outputs are identical'      )
   _a					:  str						=  BertTokenizer.from_pretrained('bert-base-uncased'      )

   # prepare the model inputs
   _a					:  List[str]						=  tokenizer.encode('This is sample éàalj\'-.'      )
   encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(A      ))      )
   _a					:  Optional[int]						=  torch.tensor(A      ).unsqueeze(0      )
   _a					:  int						=  tokenizer.encode('This is sample 3 éàalj\'-.'      )
   decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(A      ))      )
   _a					:  Optional[Any]						=  torch.tensor(A      ).unsqueeze(0      )

   # failsafe to make sure the weights reset does not affect the
   # loaded weights.
   assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight      )      ) == 0

   # forward pass
   _a					:  Optional[int]						=  encoder_input_ids
   _a					:  Dict						=  decoder_input_ids
   _a					:  List[str]						=  None
   _a					:  str						=  None
   _a					:  Dict						=  None
   _a					:  str						=  None
   _a					:  Tuple						=  None

   # The original model does not apply the geneator layer immediatly but rather in
   # the beam search (where it combines softmax + linear layer). Since we already
   # apply the softmax in our generation process we only apply the linear layer here.
   # We make sure that the outputs of the full stack are identical
   _a					:  int						=  original(A		,	A		,	A		,	A		,	A		,	A		,	A      )[0]
   _a					:  Any						=  original.generator(A      )

   _a					:  Union[str, Any]						=  new_model(
       A		,	A		,	A		,	A		,	A      )[0]
   _a					:  int						=  new_model.generator(A      )

   _a					:  Union[str, Any]						=  torch.max(torch.abs(output_converted_model - output_original_model      )      ).item()
   print('Maximum absolute difference beween weights: {:.2f}'.format(A      )      )
   _a					:  Union[str, Any]						=  torch.max(torch.abs(output_converted_generator - output_original_generator      )      ).item()
   print('Maximum absolute difference beween weights: {:.2f}'.format(A      )      )

   _a					:  str						=  torch.allclose(A		,	A		,	atol=1E-3      )
   if are_identical:
      logging.info('all weights are equal up to 1e-3'      )
   else:
      raise ValueError('the weights are different. The new model is likely different from the original one.'      )

   # The model has been saved with torch.save(model) and this is bound to the exact
   # directory structure. We save the state_dict instead.
   logging.info('saving the model\'s state dictionary'      )
   torch.save(
       new_model.state_dict()		,	'./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin'      )


if __name__ == "__main__":
 UpperCAmelCase_      :		List[str]          =		argparse.ArgumentParser()
 parser.add_argument(
     "--bertabs_checkpoint_path",
     default=None,
     type=str,
     required=True,
     help="Path the official PyTorch dump.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default=None,
     type=str,
     required=True,
     help="Path to the output PyTorch model.",
 )
 UpperCAmelCase_      :		Tuple          =		parser.parse_args()

 convert_bertabs_checkpoints(
     args.bertabs_checkpoint_path,
     args.pytorch_dump_folder_path,
 )






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				Any       ,       __magic_name__	:				List[str]       ,       __magic_name__	:				Optional[int]       ,       __magic_name__	:				Tuple	)  ->    List[Any]:
		"""simple docstring"""




		lowercase__	     =						StableDiffusionPipeline.from_pretrained(__magic_name__       ,       torch_dtype=torch.floataa	)

		# load LoRA weight from .safetensors
		lowercase__	     =						load_file(__magic_name__	)

		lowercase__	     =						[]

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

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

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

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

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

		return pipeline


if __name__ == "__main__":
	A			:						int        =  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			:						str        =  parser.parse_args()

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

	A			:						List[str]        =  convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

	A			:						int        =  pipe.to(args.device)
	pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

