






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= StableDiffusionPipeline.from_pretrained(lowerCAmelCase__					,			torch_dtype=torch.floataa )

					# load LoRA weight from .safetensors
					_A								= load_file(lowerCAmelCase__ )

					_A								= []

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

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

										_A								= []
										if "lora_down" in key:
															pair_keys.append(key.replace('''lora_down'''					,			'''lora_up''' ) )
															pair_keys.append(lowerCAmelCase__ )
										else:
															pair_keys.append(lowerCAmelCase__ )
															pair_keys.append(key.replace('''lora_up'''					,			'''lora_down''' ) )

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

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

					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)











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A      =				logging.get_logger(__name__)

__A      =				{
    'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
    'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
    'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
    'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
    'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
    'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							A_						  =					"roberta"

							def __init__(  self:   Dict		,     __A:   int=5_02_65		,     __A:   Union[str, Any]=7_68		,     __A:   Union[str, Any]=12		,     __A:   str=12		,     __A:   int=30_72		,     __A:   str="gelu"		,     __A:   Union[str, Any]=0.1		,     __A:   int=0.1		,     __A:   Optional[int]=5_12		,     __A:   Union[str, Any]=2		,     __A:   str=0.02		,     __A:   str=1e-12		,     __A:   Any=1		,     __A:   str=0		,     __A:   Any=2		,     __A:   Optional[int]="absolute"		,     __A:   Optional[Any]=True		,     __A:   Union[str, Any]=None		,     **__A:   List[str]		,     )    ->				Dict:
												super().__init__(pad_token_id=__A		,     bos_token_id=__A		,     eos_token_id=__A		,     **__A   )

												_A								= vocab_size
												_A								= hidden_size
												_A								= num_hidden_layers
												_A								= num_attention_heads
												_A								= hidden_act
												_A								= intermediate_size
												_A								= hidden_dropout_prob
												_A								= attention_probs_dropout_prob
												_A								= max_position_embeddings
												_A								= type_vocab_size
												_A								= initializer_range
												_A								= layer_norm_eps
												_A								= position_embedding_type
												_A								= use_cache
												_A								= classifier_dropout





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							@property
							def       __A (  self:   Dict   )    ->				Mapping[str, Mapping[int, str]]:
												if self.task == "multiple-choice":
																	_A								= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	_A								= {0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												    ]   )



