




'''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 						snake_case_							(_a	:							Union[str, Any]    ,   _a	:							Optional[int]=False     ):
    UpperCAmelCase			       =		[]
    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"
        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 						snake_case_							(_a	:							List[Any]    ,   _a	:							List[Any]    ,   _a	:							List[str]=False     ):
    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"module.blocks.{i}.attn.qkv.weight"     )
        UpperCAmelCase			       =		state_dict.pop(F"module.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 						snake_case_							(_a	:							Optional[Any]     ):
    UpperCAmelCase			       =		['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE     )





def 						snake_case_							(_a	:							Any     ):
    # projection head is used in the self-supervised pre-training in MSN,
    # for downstream task it's not needed.
    UpperCAmelCase			       =		[
        '''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(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE     )





def 						snake_case_							(_a	:							str    ,   _a	:							str    ,   _a	:							Union[str, Any]     ):
    UpperCAmelCase			       =		dct.pop(__SCREAMING_SNAKE_CASE     )
    UpperCAmelCase			       =		val





def 						snake_case_							(_a	:							Tuple    ,   _a	:							Any     ):
    UpperCAmelCase			       =		ViTMSNConfig()
    UpperCAmelCase			       =		1_0_0_0

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

    if "s16" in checkpoint_url:
        UpperCAmelCase			       =		3_8_4
        UpperCAmelCase			       =		1_5_3_6
        UpperCAmelCase			       =		6
    elif "l16" in checkpoint_url:
        UpperCAmelCase			       =		1_0_2_4
        UpperCAmelCase			       =		4_0_9_6
        UpperCAmelCase			       =		2_4
        UpperCAmelCase			       =		1_6
        UpperCAmelCase			       =		0.1
    elif "b4" in checkpoint_url:
        UpperCAmelCase			       =		4
    elif "l7" in checkpoint_url:
        UpperCAmelCase			       =		7
        UpperCAmelCase			       =		1_0_2_4
        UpperCAmelCase			       =		4_0_9_6
        UpperCAmelCase			       =		2_4
        UpperCAmelCase			       =		1_6
        UpperCAmelCase			       =		0.1

    UpperCAmelCase			       =		ViTMSNModel(__SCREAMING_SNAKE_CASE     )

    UpperCAmelCase			       =		torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE    ,   map_location='''cpu'''     )['''target_encoder''']

    UpperCAmelCase			       =		ViTImageProcessor(size=config.image_size     )

    remove_projection_head(__SCREAMING_SNAKE_CASE     )
    UpperCAmelCase			       =		create_rename_keys(__SCREAMING_SNAKE_CASE    ,   base_model=__SCREAMING_SNAKE_CASE     )

    for src, dest in rename_keys:
        rename_key(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE     )
    read_in_q_k_v(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE    ,   base_model=__SCREAMING_SNAKE_CASE     )

    model.load_state_dict(__SCREAMING_SNAKE_CASE     )
    model.eval()

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

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

    # forward pass
    torch.manual_seed(2     )
    UpperCAmelCase			       =		model(**__SCREAMING_SNAKE_CASE     )
    UpperCAmelCase			       =		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:
        UpperCAmelCase			       =		torch.tensor([[-1.0915, -1.4876, -1.1809]]     )
    elif "b16" in checkpoint_url:
        UpperCAmelCase			       =		torch.tensor([[14.2889, -18.9045, 11.7281]]     )
    elif "l16" in checkpoint_url:
        UpperCAmelCase			       =		torch.tensor([[41.5028, -22.8681, 45.6475]]     )
    elif "b4" in checkpoint_url:
        UpperCAmelCase			       =		torch.tensor([[-4.3868, 5.2932, -0.4137]]     )
    else:
        UpperCAmelCase			       =		torch.tensor([[-0.1792, -0.6465, 2.4263]]     )

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

    print(F"Saving model to {pytorch_dump_folder_path}"     )
    model.save_pretrained(__SCREAMING_SNAKE_CASE     )

    print(F"Saving image processor to {pytorch_dump_folder_path}"     )
    image_processor.save_pretrained(__SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
   A     =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.'
   )

   A     =parser.parse_args()
   convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    1.5
	lowercase       =    int(factor * num_class_images							)
	lowercase       =    ClipClient(
	    url='https://knn.laion.ai/knn-service' ,							indice_name='laion_400m' ,							num_images=__SCREAMING_SNAKE_CASE ,							aesthetic_weight=0.1							)

	os.makedirs(F'''{class_data_dir}/images''' ,							exist_ok=__SCREAMING_SNAKE_CASE							)
	if len(list(Path(F'''{class_data_dir}/images'''							).iterdir()							)							) >= num_class_images:
		return

	while True:
		lowercase       =    client.query(text=__SCREAMING_SNAKE_CASE							)
		if len(__SCREAMING_SNAKE_CASE							) >= factor * num_class_images or num_images > 1e4:
			break
		else:
			lowercase       =    int(factor * num_images							)
			lowercase       =    ClipClient(
			    url='https://knn.laion.ai/knn-service' ,							indice_name='laion_400m' ,							num_images=__SCREAMING_SNAKE_CASE ,							aesthetic_weight=0.1 ,							)

	lowercase       =    0
	lowercase       =    0
	lowercase       =    tqdm(desc='downloading real regularization images' ,							total=__SCREAMING_SNAKE_CASE							)

	with open(F'''{class_data_dir}/caption.txt''' ,							'w'							) as fa, open(F'''{class_data_dir}/urls.txt''' ,							'w'							) as fa, open(
	    F'''{class_data_dir}/images.txt''' ,							'w'							) as fa:
		while total < num_class_images:
			lowercase       =    class_images[count]
			count += 1
			try:
				lowercase       =    requests.get(images['url']							)
				if img.status_code == 200:
					lowercase       =    Image.open(BytesIO(img.content							)							)
					with open(F'''{class_data_dir}/images/{total}.jpg''' ,							'wb'							) as f:
						f.write(img.content							)
					fa.write(images['caption'] + '\n'							)
					fa.write(images['url'] + '\n'							)
					fa.write(F'''{class_data_dir}/images/{total}.jpg''' + '\n'							)
					total += 1
					pbar.update(1							)
				else:
					continue
			except Exception:
				continue
	return






def 							UpperCAmelCase_		(							):
	lowercase       =    argparse.ArgumentParser('' ,							add_help=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--class_prompt' ,							help='text prompt to retrieve images' ,							required=__SCREAMING_SNAKE_CASE ,							type=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--class_data_dir' ,							help='path to save images' ,							required=__SCREAMING_SNAKE_CASE ,							type=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--num_class_images' ,							help='number of images to download' ,							default=200 ,							type=__SCREAMING_SNAKE_CASE							)
	return parser.parse_args()


if __name__ == "__main__":
	UpperCAmelCase	      = parse_args()
	retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)

