



'''simple docstring'''



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def 					UpperCAmelCase   (						A  :				Dict					,		A  :				Dict					,		A  :				str					,		A  :				Union[str, Any]					,		A  :				str				):
							# load base model
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			StableDiffusionPipeline.from_pretrained(A					,		torch_dtype=torch.floataa				)

							# load LoRA weight from .safetensors
							SCREAMING_SNAKE_CASE					:      Any     					=			load_file(A				)

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

														# find the target layer
														SCREAMING_SNAKE_CASE					:      Any     					=			layer_infos.pop(0				)
														while len(A				) > -1:
																					try:
																												SCREAMING_SNAKE_CASE					:      str     					=			curr_layer.__getattr__(A				)
																												if len(A				) > 0:
																																			SCREAMING_SNAKE_CASE					:      Tuple     					=			layer_infos.pop(0				)
																												elif len(A				) == 0:
																																			break
																					except Exception:
																												if len(A				) > 0:
																																			temp_name += "_" + layer_infos.pop(0				)
																												else:
																																			SCREAMING_SNAKE_CASE					:      Dict     					=			layer_infos.pop(0				)

														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			[]
														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:
																					SCREAMING_SNAKE_CASE					:      int     					=			state_dict[pair_keys[0]].squeeze(3				).squeeze(2				).to(torch.floataa				)
																					SCREAMING_SNAKE_CASE					:      str     					=			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:
																					SCREAMING_SNAKE_CASE					:      Dict     					=			state_dict[pair_keys[0]].to(torch.floataa				)
																					SCREAMING_SNAKE_CASE					:      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__":
							lowerCAmelCase_     :       List[Any]								=						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_     :       List[Any]								=						parser.parse_args()

							lowerCAmelCase_     :       Dict								=						args.base_model_path
							lowerCAmelCase_     :       Dict								=						args.checkpoint_path
							lowerCAmelCase_     :       Any								=						args.dump_path
							lowerCAmelCase_     :       Optional[Any]								=						args.lora_prefix_unet
							lowerCAmelCase_     :       Optional[Any]								=						args.lora_prefix_text_encoder
							lowerCAmelCase_     :       Tuple								=						args.alpha

							lowerCAmelCase_     :       Any								=						convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

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









'''simple docstring'''



import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def 					UpperCAmelCase   (						A  :				Union[str, Any]					,		A  :				Optional[int]					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			1.5
							SCREAMING_SNAKE_CASE					:      str     					=			int(factor * num_class_images				)
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ClipClient(
							    url='''https://knn.laion.ai/knn-service'''					,		indice_name='''laion_400m'''					,		num_images=A					,		aesthetic_weight=0.1				)

							os.makedirs(F"""{class_data_dir}/images"""					,		exist_ok=A				)
							if len(list(Path(F"""{class_data_dir}/images"""				).iterdir()				)				) >= num_class_images:
														return

							while True:
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			client.query(text=A				)
														if len(A				) >= factor * num_class_images or num_images > 1e4:
																					break
														else:
																					SCREAMING_SNAKE_CASE					:      Optional[int]     					=			int(factor * num_images				)
																					SCREAMING_SNAKE_CASE					:      str     					=			ClipClient(
																					    url='''https://knn.laion.ai/knn-service'''					,		indice_name='''laion_400m'''					,		num_images=A					,		aesthetic_weight=0.1					,		)

							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			0
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			0
							SCREAMING_SNAKE_CASE					:      Tuple     					=			tqdm(desc='''downloading real regularization images'''					,		total=A				)

							with open(F"""{class_data_dir}/caption.txt"""					,		'''w'''				) as fa, open(F"""{class_data_dir}/urls.txt"""					,		'''w'''				) as fa, open(
							    F"""{class_data_dir}/images.txt"""					,		'''w'''				) as fa:
														while total < num_class_images:
																					SCREAMING_SNAKE_CASE					:      int     					=			class_images[count]
																					count += 1
																					try:
																												SCREAMING_SNAKE_CASE					:      int     					=			requests.get(images['''url''']				)
																												if img.status_code == 200:
																																			SCREAMING_SNAKE_CASE					:      List[str]     					=			Image.open(BytesIO(img.content				)				)
																																			with open(F"""{class_data_dir}/images/{total}.jpg"""					,		'''wb'''				) as f:
																																										f.write(img.content				)
																																			fa.write(images['''caption'''] + '''\n'''				)
																																			fa.write(images['''url'''] + '''\n'''				)
																																			fa.write(F"""{class_data_dir}/images/{total}.jpg""" + '''\n'''				)
																																			total += 1
																																			pbar.update(1				)
																												else:
																																			continue
																					except Exception:
																												continue
							return
def 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			argparse.ArgumentParser(''''''					,		add_help=A				)
							parser.add_argument('''--class_prompt'''					,		help='''text prompt to retrieve images'''					,		required=A					,		type=A				)
							parser.add_argument('''--class_data_dir'''					,		help='''path to save images'''					,		required=A					,		type=A				)
							parser.add_argument('''--num_class_images'''					,		help='''number of images to download'''					,		default=200					,		type=A				)
							return parser.parse_args()


if __name__ == "__main__":
							lowerCAmelCase_     :       List[str]								=						parse_args()
							retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)




