






"""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_       :							List[Any]             =				logging.get_logger(__name__)






def 					_lowerCAmelCase(a   :    Dict			,      a   :    int=False  )      ->       Union[str, Any]:
	_SCREAMING_SNAKE_CASE						=[]
	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						=[(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(a   :    List[Any]			,      a   :    List[Any]			,      a   :    Optional[int]=False  )      ->       Optional[Any]:
	for i in range(config.num_hidden_layers  ):
		if base_model:
			_SCREAMING_SNAKE_CASE						=''''''
		else:
			_SCREAMING_SNAKE_CASE						='''vit.'''
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		_SCREAMING_SNAKE_CASE						=state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""  )
		_SCREAMING_SNAKE_CASE						=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						=in_proj_weight[
		    : config.hidden_size, :
		]
		_SCREAMING_SNAKE_CASE						=in_proj_bias[: config.hidden_size]
		_SCREAMING_SNAKE_CASE						=in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		_SCREAMING_SNAKE_CASE						=in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		_SCREAMING_SNAKE_CASE						=in_proj_weight[
		    -config.hidden_size :, :
		]
		_SCREAMING_SNAKE_CASE						=in_proj_bias[-config.hidden_size :]






def 					_lowerCAmelCase(a   :    Any  )      ->       Dict:
	_SCREAMING_SNAKE_CASE						=['''head.weight''', '''head.bias''']
	for k in ignore_keys:
		state_dict.pop(a_			,      a_  )






def 					_lowerCAmelCase(a   :    Tuple			,      a   :    str			,      a   :    List[str]  )      ->       Union[str, Any]:
	_SCREAMING_SNAKE_CASE						=dct.pop(a_  )
	_SCREAMING_SNAKE_CASE						=val






def 					_lowerCAmelCase()      ->       List[str]:
	_SCREAMING_SNAKE_CASE						='''http://images.cocodataset.org/val2017/000000039769.jpg'''
	_SCREAMING_SNAKE_CASE						=Image.open(requests.get(a_			,      stream=a_  ).raw  )
	return im






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

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

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

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

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

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

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


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












from __future__ import annotations





def 		__UpperCAmelCase     (	a_				,	a_				,	a_):
							snake_case_           =				list(range(len(a_)))
							snake_case_           =				[v / w for v, w in zip(a_				,	a_)]
							index.sort(key=lambda a_: ratio[i]				,	reverse=a_)

							snake_case_           =				0
							snake_case_           =				[0] * len(a_)
							for i in index:
														if weight[i] <= capacity:
																					snake_case_           =				1
																					max_value += value[i]
																					capacity -= weight[i]
														else:
																					snake_case_           =				capacity / weight[i]
																					max_value += value[i] * capacity / weight[i]
																					break

							return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()





