
"""simple docstring"""

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     :    str       =			logging.get_logger(__name__)






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






def 							A						(    snake_case				:Union[str, Any]		)				->     Tuple:
 __UpperCamelCase      =	['head.weight', 'head.bias']
 for k in ignore_keys:
  state_dict.pop(snake_case    , snake_case		)






def 							A						(    snake_case				:Tuple    , snake_case				:Optional[Any]    , snake_case				:Dict		)				->     int:
 __UpperCamelCase      =	dct.pop(snake_case		)
 __UpperCamelCase      =	val






def 							A						(    )				->     Optional[Any]:
 __UpperCamelCase      =	'http://images.cocodataset.org/val2017/000000039769.jpg'
 __UpperCamelCase      =	Image.open(requests.get(snake_case    , stream=snake_case		).raw		)
 return im






@torch.no_grad()
def 							A						(    snake_case				:str    , snake_case				:Tuple    , snake_case				:Optional[int]=True		)				->     int:
 __UpperCamelCase      =	ViTConfig()
 # patch_size
 if model_name[-1] == "8":
  __UpperCamelCase      =	8
 # set labels if required
 if not base_model:
  __UpperCamelCase      =	1_0_0_0
  __UpperCamelCase      =	'huggingface/label-files'
  __UpperCamelCase      =	'imagenet-1k-id2label.json'
  __UpperCamelCase      =	json.load(open(hf_hub_download(snake_case    , snake_case    , repo_type='dataset'		)    , 'r'		)		)
  __UpperCamelCase      =	{int(snake_case		): v for k, v in idalabel.items()}
  __UpperCamelCase      =	idalabel
  __UpperCamelCase      =	{v: k for k, v in idalabel.items()}
 # size of the architecture
 if model_name in ["dino_vits8", "dino_vits16"]:
  __UpperCamelCase      =	3_8_4
  __UpperCamelCase      =	1_5_3_6
  __UpperCamelCase      =	1_2
  __UpperCamelCase      =	6

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

 # load state_dict of original model, remove and rename some keys
 __UpperCamelCase      =	original_model.state_dict()
 if base_model:
  remove_classification_head_(snake_case		)
 __UpperCamelCase      =	create_rename_keys(snake_case    , base_model=snake_case		)
 for src, dest in rename_keys:
  rename_key(snake_case    , snake_case    , snake_case		)
 read_in_q_k_v(snake_case    , snake_case    , snake_case		)

 # load HuggingFace model
 if base_model:
  __UpperCamelCase      =	ViTModel(snake_case    , add_pooling_layer=snake_case		).eval()
 else:
  __UpperCamelCase      =	ViTForImageClassification(snake_case		).eval()
 model.load_state_dict(snake_case		)

 # Check outputs on an image, prepared by ViTImageProcessor
 __UpperCamelCase      =	ViTImageProcessor()
 __UpperCamelCase      =	image_processor(images=prepare_img()    , return_tensors='pt'		)
 __UpperCamelCase      =	encoding['pixel_values']
 __UpperCamelCase      =	model(snake_case		)

 if base_model:
  __UpperCamelCase      =	original_model(snake_case		)
  assert torch.allclose(snake_case    , outputs.last_hidden_state[:, 0, :]    , atol=1e-1		)
 else:
  __UpperCamelCase      =	original_model(snake_case		)
  assert logits.shape == outputs.logits.shape
  assert torch.allclose(snake_case    , outputs.logits    , atol=1e-3		)

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


if __name__ == "__main__":
      UpperCamelCase     :    List[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)
      UpperCamelCase     :    List[str]       =			parser.parse_args()
      convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





"""simple docstring"""

import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 							A						(    snake_case				:Dict    , snake_case				:Optional[Any]    , snake_case				:List[Any]		)				->     Optional[Any]:
 __UpperCamelCase      =	OmegaConf.load(snake_case		)
 __UpperCamelCase      =	torch.load(snake_case    , map_location='cpu'		)['model']
 __UpperCamelCase      =	list(state_dict.keys()		)

 # extract state_dict for VQVAE
 __UpperCamelCase      =	{}
 __UpperCamelCase      =	'first_stage_model.'
 for key in keys:
  if key.startswith(snake_case		):
   __UpperCamelCase      =	state_dict[key]

    # extract state_dict for UNetLDM
 __UpperCamelCase      =	{}
 __UpperCamelCase      =	'model.diffusion_model.'
 for key in keys:
  if key.startswith(snake_case		):
   __UpperCamelCase      =	state_dict[key]

 __UpperCamelCase      =	config.model.params.first_stage_config.params
 __UpperCamelCase      =	config.model.params.unet_config.params

 __UpperCamelCase      =	VQModel(**snake_case		).eval()
 vqvae.load_state_dict(snake_case		)

 __UpperCamelCase      =	UNetLDMModel(**snake_case		).eval()
 unet.load_state_dict(snake_case		)

 __UpperCamelCase      =	DDIMScheduler(
     timesteps=config.model.params.timesteps    , beta_schedule='scaled_linear'    , beta_start=config.model.params.linear_start    , beta_end=config.model.params.linear_end    , clip_sample=snake_case    , )

 __UpperCamelCase      =	LDMPipeline(snake_case    , snake_case    , snake_case		)
 pipeline.save_pretrained(snake_case		)


if __name__ == "__main__":
      UpperCamelCase     :    Dict       =			argparse.ArgumentParser()
      parser.add_argument("--checkpoint_path", type=str, required=True)
      parser.add_argument("--config_path", type=str, required=True)
      parser.add_argument("--output_path", type=str, required=True)
      UpperCamelCase     :    Optional[int]       =			parser.parse_args()

      convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)



