



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Dict:
			A__       =    StableDiffusionPipeline.from_pretrained(UpperCAmelCase__ ,	torch_dtype=torch.floataa					)

			# load LoRA weight from .safetensors
			A__       =    load_file(UpperCAmelCase__					)

			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(UpperCAmelCase__					) > -1:
									try:
												A__       =    curr_layer.__getattr__(UpperCAmelCase__					)
												if len(UpperCAmelCase__					) > 0:
															A__       =    layer_infos.pop(0					)
												elif len(UpperCAmelCase__					) == 0:
															break
									except Exception:
												if len(UpperCAmelCase__					) > 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(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:
									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(UpperCAmelCase__ ,	UpperCAmelCase__					).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(UpperCAmelCase__ ,	UpperCAmelCase__					)

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

			return pipeline


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__						 =      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.)''')

							SCREAMING_SNAKE_CASE__						 =      parser.parse_args()

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

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

							SCREAMING_SNAKE_CASE__						 =      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


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =      {
    '''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      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		List[str]			       =    "roberta"



					def __init__(     self					:       List[str]				,					_snake_case					:       Union[str, Any]=5_02_65				,					_snake_case					:       List[Any]=7_68				,					_snake_case					:       List[str]=12				,					_snake_case					:       List[str]=12				,					_snake_case					:       Any=30_72				,					_snake_case					:       Union[str, Any]="gelu"				,					_snake_case					:       int=0.1				,					_snake_case					:       Union[str, Any]=0.1				,					_snake_case					:       Tuple=5_12				,					_snake_case					:       Union[str, Any]=2				,					_snake_case					:       Any=0.02				,					_snake_case					:       Any=1E-12				,					_snake_case					:       List[Any]=1				,					_snake_case					:       int=0				,					_snake_case					:       Any=2				,					_snake_case					:       Optional[Any]="absolute"				,					_snake_case					:       int=True				,					_snake_case					:       Any=None				,					**_snake_case					:       Any				,					):






								"""simple docstring"""


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

								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      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					@property
					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								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),
								    ]					)




