





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



def A_     (		A__    )  ->		Optional[int]:
       a__						:     List[str]			=    ['head.weight', 'head.bias']
       for k in ignore_keys:
              state_dict.pop(A__		,				A__    )



def A_     (		A__    )  ->		Optional[int]:
       # projection head is used in the self-supervised pre-training in MSN,
       # for downstream task it's not needed.
       a__						:     Dict			=    [
           '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 A_     (		A__		,				A__		,				A__    )  ->		int:
       a__						:     List[Any]			=    dct.pop(A__    )
       a__						:     Union[str, Any]			=    val



def A_     (		A__		,				A__    )  ->		Dict:
       a__						:     Tuple			=    ViTMSNConfig()
       a__						:     Dict			=    1000

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

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

       a__						:     Dict			=    ViTMSNModel(A__    )

       a__						:     Optional[Any]			=    torch.hub.load_state_dict_from_url(A__		,				map_location='cpu'    )['target_encoder']

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

       remove_projection_head(A__    )
       a__						:     str			=    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()

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

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

       # forward pass
       torch.manual_seed(2    )
       a__						:     str			=    model(**A__    )
       a__						:     int			=    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:
              a__						:     List[Any]			=    torch.tensor([[-1.09_15, -1.48_76, -1.18_09]]    )
       elif "b16" in checkpoint_url:
              a__						:     List[Any]			=    torch.tensor([[14.28_89, -18.90_45, 11.72_81]]    )
       elif "l16" in checkpoint_url:
              a__						:     Tuple			=    torch.tensor([[41.50_28, -22.86_81, 45.64_75]]    )
       elif "b4" in checkpoint_url:
              a__						:     Dict			=    torch.tensor([[-4.38_68, 5.29_32, -0.41_37]]    )
       else:
              a__						:     Optional[Any]			=    torch.tensor([[-0.17_92, -0.64_65, 2.42_63]]    )

       # 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				:	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."""
    )

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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase				:	str      						=  {
    """configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Optional[Any]      						=  ["""VisionEncoderDecoderModel"""]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	Optional[int]      						=  ["""TFVisionEncoderDecoderModel"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase				:	List[str]      						=  ["""FlaxVisionEncoderDecoderModel"""]

if TYPE_CHECKING:
    from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
    import sys

    lowercase				:	Optional[int]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


