"""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()
_a					:					Dict=			logging.get_logger(__name__)
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    Tuple=False  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							int						=			[]
	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"
		__snake_case      :							Tuple						=			[(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   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    int      ,							UpperCAmelCase_   :    Optional[int]=False  )							->      Tuple:

	'''simple docstring'''





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

	'''simple docstring'''





	__snake_case      :							Dict						=			['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(UpperCAmelCase_      ,							UpperCAmelCase_  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[int]      ,							UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    Union[str, Any]  )							->      Any:

	'''simple docstring'''





	__snake_case      :							Optional[int]						=			dct.pop(UpperCAmelCase_  )
	__snake_case      :							Optional[int]						=			val
def   __UpperCAmelCase    (   )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							Tuple						=			'http://images.cocodataset.org/val2017/000000039769.jpg'
	__snake_case      :							int						=			Image.open(requests.get(UpperCAmelCase_      ,							stream=UpperCAmelCase_  ).raw  )
	return im
@torch.no_grad()
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    Optional[int]      ,							UpperCAmelCase_   :    List[Any]=True  )							->      List[Any]:

	'''simple docstring'''





	__snake_case      :							Optional[Any]						=			ViTConfig()
	# patch_size
	if model_name[-1] == "8":
		__snake_case      :							Optional[Any]						=			8
	# set labels if required
	if not base_model:
		__snake_case      :							Any						=			10_00
		__snake_case      :							List[str]						=			'huggingface/label-files'
		__snake_case      :							Tuple						=			'imagenet-1k-id2label.json'
		__snake_case      :							int						=			json.load(open(hf_hub_download(UpperCAmelCase_      ,							UpperCAmelCase_      ,							repo_type='dataset'  )      ,							'r'  )  )
		__snake_case      :							Union[str, Any]						=			{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}
		__snake_case      :							Union[str, Any]						=			idalabel
		__snake_case      :							Union[str, Any]						=			{v: k for k, v in idalabel.items()}
	# size of the architecture
	if model_name in ["dino_vits8", "dino_vits16"]:
		__snake_case      :							Tuple						=			3_84
		__snake_case      :							Optional[int]						=			15_36
		__snake_case      :							Dict						=			12
		__snake_case      :							Tuple						=			6

	# load original model from torch hub
	__snake_case      :							List[str]						=			torch.hub.load('facebookresearch/dino:main'      ,							UpperCAmelCase_  )
	original_model.eval()

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

	# load HuggingFace model
	if base_model:
		__snake_case      :							Optional[int]						=			ViTModel(UpperCAmelCase_      ,							add_pooling_layer=UpperCAmelCase_  ).eval()
	else:
		__snake_case      :							int						=			ViTForImageClassification(UpperCAmelCase_  ).eval()
	model.load_state_dict(UpperCAmelCase_  )

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

	if base_model:
		__snake_case      :							int						=			original_model(UpperCAmelCase_  )
		assert torch.allclose(UpperCAmelCase_      ,							outputs.last_hidden_state[:, 0, :]      ,							atol=1E-1  )
	else:
		__snake_case      :							List[Any]						=			original_model(UpperCAmelCase_  )
		assert logits.shape == outputs.logits.shape
		assert torch.allclose(UpperCAmelCase_      ,							outputs.logits      ,							atol=1E-3  )

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


if __name__ == "__main__":
	_a					:					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)
	_a					:					int=			parser.parse_args()
	convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)






"""simple docstring"""







def   __UpperCAmelCase    (   UpperCAmelCase_   :    list      ,							UpperCAmelCase_   :    int      ,							UpperCAmelCase_   :    int = 0      ,							UpperCAmelCase_   :    int = 0  )							->      int:

	'''simple docstring'''





	__snake_case      :							str						=			right or len(UpperCAmelCase_  ) - 1
	if left > right:
		return -1
	elif list_data[left] == key:
		return left
	elif list_data[right] == key:
		return right
	else:
		return search(UpperCAmelCase_      ,							UpperCAmelCase_      ,							left + 1      ,							right - 1  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





