



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase    :		Optional[int]		      =    logging.get_logger(__name__)

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase=False   )     ->     List[str]:
					SCREAMING_SNAKE_CASE							:     Tuple						=				[]
					for i in range(config.num_hidden_layers   ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')   )
										rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')   )
										rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')   )
										rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')   )
										rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')   )
										rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')   )
										rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')   )
										rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')   )
										rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')   )
										rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')   )

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        ('cls_token', 'vit.embeddings.cls_token'),
					        ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
					        ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
					        ('pos_embed', 'vit.embeddings.position_embeddings'),
					    ]   )

					if base_model:
										# layernorm + pooler
										rename_keys.extend(
										    [
										        ('norm.weight', 'layernorm.weight'),
										        ('norm.bias', 'layernorm.bias'),
										    ]   )

										# if just the base model, we should remove "vit" from all keys that start with "vit"
										SCREAMING_SNAKE_CASE							:     List[str]						=				[(pair[0], pair[1][4:]) if pair[1].startswith('vit'   ) else pair for pair in rename_keys]
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ('norm.weight', 'vit.layernorm.weight'),
										        ('norm.bias', 'vit.layernorm.bias'),
										        ('head.weight', 'classifier.weight'),
										        ('head.bias', 'classifier.bias'),
										    ]   )

					return rename_keys

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase=False   )     ->     List[str]:
					for i in range(config.num_hidden_layers   ):
										if base_model:
															SCREAMING_SNAKE_CASE							:     int						=				''
										else:
															SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				'vit.'
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										SCREAMING_SNAKE_CASE							:     Dict						=				state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''   )
										SCREAMING_SNAKE_CASE							:     Dict						=				state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''   )
										# next, add query, keys and values (in that order) to the state dict
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				in_proj_weight[
										    : config.hidden_size, :
										]
										SCREAMING_SNAKE_CASE							:     int						=				in_proj_bias[: config.hidden_size]
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										SCREAMING_SNAKE_CASE							:     str						=				in_proj_weight[
										    -config.hidden_size :, :
										]
										SCREAMING_SNAKE_CASE							:     int						=				in_proj_bias[-config.hidden_size :]

def        __a			(   __lowerCAmelCase   )     ->     List[str]:
					SCREAMING_SNAKE_CASE							:     Tuple						=				['head.weight', 'head.bias']
					for k in ignore_keys:
										state_dict.pop(__lowerCAmelCase      ,	__lowerCAmelCase   )

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Any:
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				dct.pop(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     List[Any]						=				val

def        __a			(   )     ->     List[Any]:
					SCREAMING_SNAKE_CASE							:     Tuple						=				'http://images.cocodataset.org/val2017/000000039769.jpg'
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				Image.open(requests.get(__lowerCAmelCase      ,	stream=__lowerCAmelCase   ).raw   )
					return im

@torch.no_grad()
def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase=True   )     ->     int:
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				ViTConfig()
					# patch_size
					if model_name[-1] == "8":
										SCREAMING_SNAKE_CASE							:     List[Any]						=				8
					# set labels if required
					if not base_model:
										SCREAMING_SNAKE_CASE							:     List[str]						=				1000
										SCREAMING_SNAKE_CASE							:     Dict						=				'huggingface/label-files'
										SCREAMING_SNAKE_CASE							:     List[str]						=				'imagenet-1k-id2label.json'
										SCREAMING_SNAKE_CASE							:     int						=				json.load(open(hf_hub_download(__lowerCAmelCase      ,	__lowerCAmelCase      ,	repo_type='dataset'   )      ,	'r'   )   )
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				{int(__lowerCAmelCase   ): v for k, v in idalabel.items()}
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				idalabel
										SCREAMING_SNAKE_CASE							:     str						=				{v: k for k, v in idalabel.items()}
					# size of the architecture
					if model_name in ["dino_vits8", "dino_vits16"]:
										SCREAMING_SNAKE_CASE							:     Any						=				384
										SCREAMING_SNAKE_CASE							:     str						=				1536
										SCREAMING_SNAKE_CASE							:     str						=				12
										SCREAMING_SNAKE_CASE							:     Any						=				6

					# load original model from torch hub
					SCREAMING_SNAKE_CASE							:     Dict						=				torch.hub.load('facebookresearch/dino:main'      ,	__lowerCAmelCase   )
					original_model.eval()

					# load state_dict of original model, remove and rename some keys
					SCREAMING_SNAKE_CASE							:     List[Any]						=				original_model.state_dict()
					if base_model:
										remove_classification_head_(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				create_rename_keys(__lowerCAmelCase      ,	base_model=__lowerCAmelCase   )
					for src, dest in rename_keys:
										rename_key(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )
					read_in_q_k_v(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )

					# load HuggingFace model
					if base_model:
										SCREAMING_SNAKE_CASE							:     List[str]						=				ViTModel(__lowerCAmelCase      ,	add_pooling_layer=__lowerCAmelCase   ).eval()
					else:
										SCREAMING_SNAKE_CASE							:     Any						=				ViTForImageClassification(__lowerCAmelCase   ).eval()
					model.load_state_dict(__lowerCAmelCase   )

					# Check outputs on an image, prepared by ViTImageProcessor
					SCREAMING_SNAKE_CASE							:     List[Any]						=				ViTImageProcessor()
					SCREAMING_SNAKE_CASE							:     str						=				image_processor(images=prepare_img()      ,	return_tensors='pt'   )
					SCREAMING_SNAKE_CASE							:     str						=				encoding['pixel_values']
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				model(__lowerCAmelCase   )

					if base_model:
										SCREAMING_SNAKE_CASE							:     List[str]						=				original_model(__lowerCAmelCase   )
										assert torch.allclose(__lowerCAmelCase      ,	outputs.last_hidden_state[:, 0, :]      ,	atol=1E-1   )
					else:
										SCREAMING_SNAKE_CASE							:     Optional[int]						=				original_model(__lowerCAmelCase   )
										assert logits.shape == outputs.logits.shape
										assert torch.allclose(__lowerCAmelCase      ,	outputs.logits      ,	atol=1E-3   )

					Path(__lowerCAmelCase   ).mkdir(exist_ok=__lowerCAmelCase   )
					print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''   )
					model.save_pretrained(__lowerCAmelCase   )
					print(F'''Saving image processor to {pytorch_dump_folder_path}'''   )
					image_processor.save_pretrained(__lowerCAmelCase   )


if __name__ == "__main__":
			_lowerCamelCase    :		Union[str, Any]		      =    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--model_name""",
			    default="""dino_vitb16""",
			    type=str,
			    help="""Name of the model trained with DINO you'd like to convert.""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
			)
			parser.add_argument(
			    """--base_model""",
			    action="""store_true""",
			    help="""Whether to only convert the base model (no projection head weights).""",
			)

			parser.set_defaults(base_model=True)
			_lowerCamelCase    :		int		      =    parser.parse_args()
			convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     str:
					# load base model
					SCREAMING_SNAKE_CASE							:     Dict						=				StableDiffusionPipeline.from_pretrained(__lowerCAmelCase      ,	torch_dtype=torch.floataa   )

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

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

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

										SCREAMING_SNAKE_CASE							:     str						=				[]
										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:
															SCREAMING_SNAKE_CASE							:     Any						=				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(__lowerCAmelCase      ,	__lowerCAmelCase   ).unsqueeze(2   ).unsqueeze(3   )
										else:
															SCREAMING_SNAKE_CASE							:     List[Any]						=				state_dict[pair_keys[0]].to(torch.floataa   )
															SCREAMING_SNAKE_CASE							:     Optional[int]						=				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__":
			_lowerCamelCase    :		Tuple		      =    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    :		List[str]		      =    args.base_model_path
			_lowerCamelCase    :		str		      =    args.checkpoint_path
			_lowerCamelCase    :		Dict		      =    args.dump_path
			_lowerCamelCase    :		Any		      =    args.lora_prefix_unet
			_lowerCamelCase    :		List[Any]		      =    args.lora_prefix_text_encoder
			_lowerCamelCase    :		List[Any]		      =    args.alpha

			_lowerCamelCase    :		Optional[Any]		      =    convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

			_lowerCamelCase    :		Optional[Any]		      =    pipe.to(args.device)
			pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)