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







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			Dict:
	_snake_case     		=	['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(_A    ,       _A			)







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	_snake_case     		=	[
	    '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(_A    ,       _A			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			int:
	_snake_case     		=	dct.pop(_A			)
	_snake_case     		=	val







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			Any:
	_snake_case     		=	ViTMSNConfig()
	_snake_case     		=	1_000

	_snake_case     		=	'datasets/huggingface/label-files'
	_snake_case     		=	'imagenet-1k-id2label.json'
	_snake_case     		=	json.load(open(hf_hub_download(_A    ,       _A			)    ,       'r'			)			)
	_snake_case     		=	{int(_A			): v for k, v in idalabel.items()}
	_snake_case     		=	idalabel
	_snake_case     		=	{v: k for k, v in idalabel.items()}

	if "s16" in checkpoint_url:
		_snake_case     		=	384
		_snake_case     		=	1_536
		_snake_case     		=	6
	elif "l16" in checkpoint_url:
		_snake_case     		=	1_024
		_snake_case     		=	4_096
		_snake_case     		=	24
		_snake_case     		=	16
		_snake_case     		=	0.1
	elif "b4" in checkpoint_url:
		_snake_case     		=	4
	elif "l7" in checkpoint_url:
		_snake_case     		=	7
		_snake_case     		=	1_024
		_snake_case     		=	4_096
		_snake_case     		=	24
		_snake_case     		=	16
		_snake_case     		=	0.1

	_snake_case     		=	ViTMSNModel(_A			)

	_snake_case     		=	torch.hub.load_state_dict_from_url(_A    ,       map_location='cpu'			)['target_encoder']

	_snake_case     		=	ViTImageProcessor(size=config.image_size			)

	remove_projection_head(_A			)
	_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    ,       base_model=_A			)

	model.load_state_dict(_A			)
	model.eval()

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

	_snake_case     		=	Image.open(requests.get(_A    ,       stream=_A			).raw			)
	_snake_case     		=	ViTImageProcessor(
	    size=config.image_size    ,       image_mean=_A    ,       image_std=_A			)
	_snake_case     		=	image_processor(images=_A    ,       return_tensors='pt'			)

	# forward pass
	torch.manual_seed(2			)
	_snake_case     		=	model(**_A			)
	_snake_case     		=	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:
		_snake_case     		=	torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]]			)
	elif "b16" in checkpoint_url:
		_snake_case     		=	torch.tensor([[14.2_889, -18.9_045, 11.7_281]]			)
	elif "l16" in checkpoint_url:
		_snake_case     		=	torch.tensor([[41.5_028, -22.8_681, 45.6_475]]			)
	elif "b4" in checkpoint_url:
		_snake_case     		=	torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]]			)
	else:
		_snake_case     		=	torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]]			)

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

	print(F'Saving model 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__":
					lowercase					:							int   =      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."
					)

					lowercase					:							int   =      parser.parse_args()
					convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



'''simple docstring'''







from abc import ABC, abstractmethod
from argparse import ArgumentParser


class 		__UpperCAmelCase   (      _lowerCamelCase		):


							@staticmethod
							@abstractmethod
							def 				lowerCamelCase       ( lowerCAmelCase_						):





								"""simple docstring"""






								raise NotImplementedError()




							@abstractmethod
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								raise NotImplementedError()

