



"""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		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]=False							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       []
     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     :      Union[str, Any]														=       [(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		( SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		List[str]=False							)    ->  str:





     '''simple docstring'''

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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      List[str]														=       ["head.weight", "head.bias"]
     for k in ignore_keys:
          state_dict.pop(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[str]							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       [
         "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		( SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		Optional[Any]		,    SCREAMING_SNAKE_CASE__      :		str							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       dct.pop(SCREAMING_SNAKE_CASE_							)
     _UpperCAmelCase     :      int														=       val

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  List[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       ViTMSNConfig()
     _UpperCAmelCase     :      List[str]														=       1_000

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

     if "s16" in checkpoint_url:
          _UpperCAmelCase     :      Optional[Any]														=       384
          _UpperCAmelCase     :      List[str]														=       1_536
          _UpperCAmelCase     :      Optional[int]														=       6
     elif "l16" in checkpoint_url:
          _UpperCAmelCase     :      List[Any]														=       1_024
          _UpperCAmelCase     :      List[Any]														=       4_096
          _UpperCAmelCase     :      Optional[int]														=       24
          _UpperCAmelCase     :      Tuple														=       16
          _UpperCAmelCase     :      int														=       0.1
     elif "b4" in checkpoint_url:
          _UpperCAmelCase     :      Tuple														=       4
     elif "l7" in checkpoint_url:
          _UpperCAmelCase     :      Union[str, Any]														=       7
          _UpperCAmelCase     :      List[Any]														=       1_024
          _UpperCAmelCase     :      Tuple														=       4_096
          _UpperCAmelCase     :      str														=       24
          _UpperCAmelCase     :      List[str]														=       16
          _UpperCAmelCase     :      str														=       0.1

     _UpperCAmelCase     :      Union[str, Any]														=       ViTMSNModel(SCREAMING_SNAKE_CASE_							)

     _UpperCAmelCase     :      List[str]														=       torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_		,    map_location="cpu"							)["target_encoder"]

     _UpperCAmelCase     :      str														=       ViTImageProcessor(size=config.image_size							)

     remove_projection_head(SCREAMING_SNAKE_CASE_							)
     _UpperCAmelCase     :      Tuple														=       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     :      int														=       "http://images.cocodataset.org/val2017/000000039769.jpg"

     _UpperCAmelCase     :      List[str]														=       Image.open(requests.get(SCREAMING_SNAKE_CASE_		,    stream=SCREAMING_SNAKE_CASE_							).raw							)
     _UpperCAmelCase     :      List[Any]														=       ViTImageProcessor(
         size=config.image_size		,    image_mean=SCREAMING_SNAKE_CASE_		,    image_std=SCREAMING_SNAKE_CASE_							)
     _UpperCAmelCase     :      Optional[int]														=       image_processor(images=SCREAMING_SNAKE_CASE_		,    return_tensors="pt"							)

     # forward pass
     torch.manual_seed(2							)
     _UpperCAmelCase     :      int														=       model(**SCREAMING_SNAKE_CASE_							)
     _UpperCAmelCase     :      str														=       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     :      int														=       torch.tensor([[-1.0_915, -1.4_876, -1.1_809]]							)
     elif "b16" in checkpoint_url:
          _UpperCAmelCase     :      List[Any]														=       torch.tensor([[14.2_889, -18.9_045, 11.7_281]]							)
     elif "l16" in checkpoint_url:
          _UpperCAmelCase     :      List[str]														=       torch.tensor([[41.5_028, -22.8_681, 45.6_475]]							)
     elif "b4" in checkpoint_url:
          _UpperCAmelCase     :      Optional[int]														=       torch.tensor([[-4.3_868, 5.2_932, -0.4_137]]							)
     else:
          _UpperCAmelCase     :      Tuple														=       torch.tensor([[-0.1_792, -0.6_465, 2.4_263]]							)

     # 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__":
 _lowerCAmelCase							:   Any				=				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							:   Union[str, Any]				=				parser.parse_args()
 convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE		:Optional[int]						=     logging.get_logger(__name__)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						"huggingface/label-files"
    UpperCamelCase_			  =						"imagenet-1k-id2label.json"
    UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
    UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
    UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    UpperCamelCase_			  =						"std_conv" if "bit" in model_name else False

    # note that when using BiT as backbone for ViT-hybrid checkpoints,
    # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
    # config.conv_layer = "std_conv_same"
    UpperCamelCase_			  =						BitConfig(
        conv_layer=SCREAMING_SNAKE_CASE_			,						num_labels=1_0_0_0			,						idalabel=SCREAMING_SNAKE_CASE_			,						labelaid=SCREAMING_SNAKE_CASE_			,						)

    return config




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[Any]:



    """simple docstring"""






    if "stem.conv" in name:
        UpperCamelCase_			  =						name.replace("stem.conv"			,						"bit.embedder.convolution"			)
    if "blocks" in name:
        UpperCamelCase_			  =						name.replace("blocks"			,						"layers"			)
    if "head.fc" in name:
        UpperCamelCase_			  =						name.replace("head.fc"			,						"classifier.1"			)
    if name.startswith("norm"			):
        UpperCamelCase_			  =						"bit." + name
    if "bit" not in name and "classifier" not in name:
        UpperCamelCase_			  =						"bit.encoder." + name

    return name




def 			lowerCAmelCase(      )->						int:



    """simple docstring"""






    UpperCamelCase_			  =						"http://images.cocodataset.org/val2017/000000039769.jpg"
    UpperCamelCase_			  =						Image.open(requests.get(SCREAMING_SNAKE_CASE_			,						stream=SCREAMING_SNAKE_CASE_			).raw			)
    return im




@torch.no_grad()
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=False			)->						Tuple:



    """simple docstring"""






    UpperCamelCase_			  =						get_config(SCREAMING_SNAKE_CASE_			)

    # load original model from timm
    UpperCamelCase_			  =						create_model(SCREAMING_SNAKE_CASE_			,						pretrained=SCREAMING_SNAKE_CASE_			)
    timm_model.eval()

    # load state_dict of original model
    UpperCamelCase_			  =						timm_model.state_dict()
    for key in state_dict.copy().keys():
        UpperCamelCase_			  =						state_dict.pop(SCREAMING_SNAKE_CASE_			)
        UpperCamelCase_			  =						val.squeeze() if "head" in key else val

    # load HuggingFace model
    UpperCamelCase_			  =						BitForImageClassification(SCREAMING_SNAKE_CASE_			)
    model.eval()
    model.load_state_dict(SCREAMING_SNAKE_CASE_			)

    # create image processor
    UpperCamelCase_			  =						create_transform(**resolve_data_config({}			,						model=SCREAMING_SNAKE_CASE_			)			)
    UpperCamelCase_			  =						transform.transforms

    UpperCamelCase_			  =						{
        "bilinear": PILImageResampling.BILINEAR,
        "bicubic": PILImageResampling.BICUBIC,
        "nearest": PILImageResampling.NEAREST,
    }

    UpperCamelCase_			  =						BitImageProcessor(
        do_resize=SCREAMING_SNAKE_CASE_			,						size={"shortest_edge": timm_transforms[0].size}			,						resample=pillow_resamplings[timm_transforms[0].interpolation.value]			,						do_center_crop=SCREAMING_SNAKE_CASE_			,						crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}			,						do_normalize=SCREAMING_SNAKE_CASE_			,						image_mean=timm_transforms[-1].mean.tolist()			,						image_std=timm_transforms[-1].std.tolist()			,						)

    UpperCamelCase_			  =						prepare_img()
    UpperCamelCase_			  =						transform(SCREAMING_SNAKE_CASE_			).unsqueeze(0			)
    UpperCamelCase_			  =						processor(SCREAMING_SNAKE_CASE_			,						return_tensors="pt"			).pixel_values

    # verify pixel values
    assert torch.allclose(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)

    # verify logits
    with torch.no_grad():
        UpperCamelCase_			  =						model(SCREAMING_SNAKE_CASE_			)
        UpperCamelCase_			  =						outputs.logits

    print("Logits:"			,						logits[0, :3]			)
    print("Predicted class:"			,						model.config.idalabel[logits.argmax(-1			).item()]			)
    UpperCamelCase_			  =						timm_model(SCREAMING_SNAKE_CASE_			)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(SCREAMING_SNAKE_CASE_			,						outputs.logits			,						atol=1E-3			)
    print("Looks ok!"			)

    if pytorch_dump_folder_path is not None:
        Path(SCREAMING_SNAKE_CASE_			).mkdir(exist_ok=SCREAMING_SNAKE_CASE_			)
        print(f"Saving model {model_name} and processor to {pytorch_dump_folder_path}"			)
        model.save_pretrained(SCREAMING_SNAKE_CASE_			)
        processor.save_pretrained(SCREAMING_SNAKE_CASE_			)

    if push_to_hub:
        print(f"Pushing model {model_name} and processor to the hub"			)
        model.push_to_hub(f"ybelkada/{model_name}"			)
        processor.push_to_hub(f"ybelkada/{model_name}"			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""resnetv2_50x1_bitm""",
     type=str,
     help="""Name of the BiT timm model 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(
     """--push_to_hub""",
     action="""store_true""",
     help="""Whether to push the model to the hub.""",
 )

 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
