


'''simple docstring'''

import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Dict=False		):





	"""simple docstring"""







	__magic_name__	:   Dict					     =					[]
	for i in range(config.num_hidden_layers		):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((f'module.blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight')		)
		rename_keys.append((f'module.blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias')		)
		rename_keys.append(
		    (f'module.blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight')		)
		rename_keys.append((f'module.blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias')		)
		rename_keys.append((f'module.blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight')		)
		rename_keys.append((f'module.blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias')		)
		rename_keys.append((f'module.blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight')		)
		rename_keys.append((f'module.blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias')		)
		rename_keys.append((f'module.blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight')		)
		rename_keys.append((f'module.blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias')		)

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

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

		# if just the base model, we should remove "vit" from all keys that start with "vit"
		__magic_name__	:   Dict					     =					[(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      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     Any=False		):





	"""simple docstring"""







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





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		):





	"""simple docstring"""







	__magic_name__	:   List[Any]					     =					['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(lowerCAmelCase		,		lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	__magic_name__	:   Tuple					     =					[
	    'module.fc.fc1.weight',
	    'module.fc.fc1.bias',
	    'module.fc.bn1.weight',
	    'module.fc.bn1.bias',
	    'module.fc.bn1.running_mean',
	    'module.fc.bn1.running_var',
	    'module.fc.bn1.num_batches_tracked',
	    'module.fc.fc2.weight',
	    'module.fc.fc2.bias',
	    'module.fc.bn2.weight',
	    'module.fc.bn2.bias',
	    'module.fc.bn2.running_mean',
	    'module.fc.bn2.running_var',
	    'module.fc.bn2.num_batches_tracked',
	    'module.fc.fc3.weight',
	    'module.fc.fc3.bias',
	]
	for k in ignore_keys:
		state_dict.pop(lowerCAmelCase		,		lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     Dict		):





	"""simple docstring"""







	__magic_name__	:   Dict					     =					dct.pop(lowerCAmelCase		)
	__magic_name__	:   List[Any]					     =					val





def lowerCamelCase      (      lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     Optional[Any]		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					ViTMSNConfig()
	__magic_name__	:   List[Any]					     =					1000

	__magic_name__	:   Optional[int]					     =					'datasets/huggingface/label-files'
	__magic_name__	:   Optional[Any]					     =					'imagenet-1k-id2label.json'
	__magic_name__	:   Any					     =					json.load(open(hf_hub_download(lowerCAmelCase		,		lowerCAmelCase		)		,		'r'		)		)
	__magic_name__	:   Optional[int]					     =					{int(lowerCAmelCase		): v for k, v in idalabel.items()}
	__magic_name__	:   List[Any]					     =					idalabel
	__magic_name__	:   int					     =					{v: k for k, v in idalabel.items()}

	if "s16" in checkpoint_url:
		__magic_name__	:   str					     =					384
		__magic_name__	:   Dict					     =					1536
		__magic_name__	:   str					     =					6
	elif "l16" in checkpoint_url:
		__magic_name__	:   Tuple					     =					1024
		__magic_name__	:   List[str]					     =					4096
		__magic_name__	:   Union[str, Any]					     =					24
		__magic_name__	:   Optional[int]					     =					16
		__magic_name__	:   List[str]					     =					0.1
	elif "b4" in checkpoint_url:
		__magic_name__	:   Optional[int]					     =					4
	elif "l7" in checkpoint_url:
		__magic_name__	:   Tuple					     =					7
		__magic_name__	:   str					     =					1024
		__magic_name__	:   int					     =					4096
		__magic_name__	:   List[str]					     =					24
		__magic_name__	:   Tuple					     =					16
		__magic_name__	:   Dict					     =					0.1

	__magic_name__	:   Optional[int]					     =					ViTMSNModel(lowerCAmelCase		)

	__magic_name__	:   Dict					     =					torch.hub.load_state_dict_from_url(lowerCAmelCase		,		map_location='cpu'		)['target_encoder']

	__magic_name__	:   Union[str, Any]					     =					ViTImageProcessor(size=config.image_size		)

	remove_projection_head(lowerCAmelCase		)
	__magic_name__	:   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		,		base_model=lowerCAmelCase		)

	model.load_state_dict(lowerCAmelCase		)
	model.eval()

	__magic_name__	:   Tuple					     =					'http://images.cocodataset.org/val2017/000000039769.jpg'

	__magic_name__	:   Optional[Any]					     =					Image.open(requests.get(lowerCAmelCase		,		stream=lowerCAmelCase		).raw		)
	__magic_name__	:   Optional[Any]					     =					ViTImageProcessor(
	    size=config.image_size		,		image_mean=lowerCAmelCase		,		image_std=lowerCAmelCase		)
	__magic_name__	:   Any					     =					image_processor(images=lowerCAmelCase		,		return_tensors='pt'		)

	# forward pass
	torch.manual_seed(2		)
	__magic_name__	:   str					     =					model(**lowerCAmelCase		)
	__magic_name__	:   Tuple					     =					outputs.last_hidden_state

	# The following Colab Notebook was used to generate these outputs:
	# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
	if "s16" in checkpoint_url:
		__magic_name__	:   Dict					     =					torch.tensor([[-1.0915, -1.4876, -1.1809]]		)
	elif "b16" in checkpoint_url:
		__magic_name__	:   Tuple					     =					torch.tensor([[14.2889, -18.9045, 11.7281]]		)
	elif "l16" in checkpoint_url:
		__magic_name__	:   str					     =					torch.tensor([[41.5028, -22.8681, 45.6475]]		)
	elif "b4" in checkpoint_url:
		__magic_name__	:   Union[str, Any]					     =					torch.tensor([[-4.3868, 5.2932, -0.4137]]		)
	else:
		__magic_name__	:   Optional[Any]					     =					torch.tensor([[-0.1792, -0.6465, 2.4263]]		)

	# verify logits
	assert torch.allclose(last_hidden_state[:, 0, :3]		,		lowerCAmelCase		,		atol=1e-4		)

	print(f'Saving model 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				:str        =      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
							    type=str,
							    help='''URL of the checkpoint you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
							)

							lowerCAmelCase				:str        =      parser.parse_args()
							convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	if not isinstance(lowerCAmelCase		,		lowerCAmelCase		):
		__magic_name__	:   int					     =					f'Input value of [number={number}] must be an integer'
		raise TypeError(lowerCAmelCase		)
	if number < 0:
		return False
	__magic_name__	:   Tuple					     =					number * number
	while number > 0:
		if number % 10 != number_square % 10:
			return False
		number //= 10
		number_square //= 10
	return True


if __name__ == "__main__":
							import doctest

							doctest.testmod()