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()
UpperCAmelCase_			    =							logging.get_logger(__name__)




def       lowerCAmelCase_     (		lowercase:							Dict  ,					lowercase:							str=False		):







 '''simple docstring'''
 _UpperCamelCase:     Optional[Any]							= []
 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"
  _UpperCamelCase:     Any							= [(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       lowerCAmelCase_     (		lowercase:							List[Any]  ,					lowercase:							Union[str, Any]  ,					lowercase:							Optional[int]=False		):







 '''simple docstring'''


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




def       lowerCAmelCase_     (		lowercase:							str		):







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= ['''head.weight''', '''head.bias''']
 for k in ignore_keys:
  state_dict.pop(__lowerCAmelCase  ,					__lowerCAmelCase		)




def       lowerCAmelCase_     (		lowercase:							Tuple  ,					lowercase:							Optional[Any]  ,					lowercase:							List[Any]		):







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= dct.pop(__lowerCAmelCase		)
 _UpperCamelCase:     List[Any]							= val




def       lowerCAmelCase_     (		):







 '''simple docstring'''
 _UpperCamelCase:     Any							= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 _UpperCamelCase:     str							= Image.open(requests.get(__lowerCAmelCase  ,					stream=__lowerCAmelCase		).raw		)
 return im




@torch.no_grad()
def       lowerCAmelCase_     (		lowercase:							Optional[Any]  ,					lowercase:							List[str]  ,					lowercase:							List[str]=True		):







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= ViTConfig()
 # patch_size
 if model_name[-1] == "8":
  _UpperCamelCase:     Optional[Any]							= 8
 # set labels if required
 if not base_model:
  _UpperCamelCase:     int							= 1_000
  _UpperCamelCase:     Union[str, Any]							= '''huggingface/label-files'''
  _UpperCamelCase:     Union[str, Any]							= '''imagenet-1k-id2label.json'''
  _UpperCamelCase:     Optional[Any]							= json.load(open(hf_hub_download(__lowerCAmelCase  ,					__lowerCAmelCase  ,					repo_type='''dataset'''		)  ,					'''r'''		)		)
  _UpperCamelCase:     Any							= {int(__lowerCAmelCase		): v for k, v in idalabel.items()}
  _UpperCamelCase:     Dict							= idalabel
  _UpperCamelCase:     int							= {v: k for k, v in idalabel.items()}
 # size of the architecture
 if model_name in ["dino_vits8", "dino_vits16"]:
  _UpperCamelCase:     Optional[int]							= 384
  _UpperCamelCase:     str							= 1_536
  _UpperCamelCase:     List[str]							= 12
  _UpperCamelCase:     Optional[Any]							= 6

 # load original model from torch hub
 _UpperCamelCase:     Optional[int]							= torch.hub.load('''facebookresearch/dino:main'''  ,					__lowerCAmelCase		)
 original_model.eval()

 # load state_dict of original model, remove and rename some keys
 _UpperCamelCase:     int							= original_model.state_dict()
 if base_model:
  remove_classification_head_(__lowerCAmelCase		)
 _UpperCamelCase:     Optional[Any]							= 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:
  _UpperCamelCase:     str							= ViTModel(__lowerCAmelCase  ,					add_pooling_layer=__lowerCAmelCase		).eval()
 else:
  _UpperCamelCase:     Union[str, Any]							= ViTForImageClassification(__lowerCAmelCase		).eval()
 model.load_state_dict(__lowerCAmelCase		)

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

 if base_model:
  _UpperCamelCase:     Any							= original_model(__lowerCAmelCase		)
  assert torch.allclose(__lowerCAmelCase  ,					outputs.last_hidden_state[:, 0, :]  ,					atol=1E-1		)
 else:
  _UpperCamelCase:     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__":
  UpperCAmelCase_			    =							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)
  UpperCAmelCase_			    =							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       lowerCAmelCase_     (		lowercase:							Optional[int]  ,					lowercase:							Any  ,					lowercase:							str  ,					lowercase:							List[str]  ,					lowercase:							Optional[int]		)    ->				int:







 '''simple docstring'''
 # load base model
 _UpperCamelCase:     Optional[int]							= StableDiffusionPipeline.from_pretrained(lowercase  ,					torch_dtype=torch.floataa		)

 # load LoRA weight from .safetensors
 _UpperCamelCase:     List[str]							= load_file(lowercase		)

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

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

  _UpperCamelCase:     Any							= []
  if "lora_down" in key:
   pair_keys.append(key.replace('''lora_down'''  ,					'''lora_up'''		)		)
   pair_keys.append(lowercase		)
  else:
   pair_keys.append(lowercase		)
   pair_keys.append(key.replace('''lora_up'''  ,					'''lora_down'''		)		)

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

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

 return pipeline


if __name__ == "__main__":
  UpperCAmelCase_			    =							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=0.7_5, 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.)''')

  UpperCAmelCase_			    =							parser.parse_args()

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

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

  UpperCAmelCase_			    =							pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)