


'''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 __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: Union[str, Any]=False				)      ->     Optional[int]:







     '''simple docstring'''






     _a						=		[]
     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						=		[(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 __UpperCamelCase   (			__lowerCamelCase		: Any      ,  __lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: Optional[int]=False				)      ->     Any:







     '''simple docstring'''






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






def __UpperCamelCase   (			__lowerCamelCase		: Tuple				)      ->     Any:







     '''simple docstring'''






     _a						=		["head.weight", "head.bias"]
     for k in ignore_keys:
          state_dict.pop(__lowerCamelCase      ,  __lowerCamelCase				)






def __UpperCamelCase   (			__lowerCamelCase		: Any				)      ->     Union[str, Any]:







     '''simple docstring'''






     _a						=		[
         "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 __UpperCamelCase   (			__lowerCamelCase		: Union[str, Any]      ,  __lowerCamelCase		: List[str]      ,  __lowerCamelCase		: Union[str, Any]				)      ->     str:







     '''simple docstring'''






     _a						=		dct.pop(__lowerCamelCase				)
     _a						=		val






def __UpperCamelCase   (			__lowerCamelCase		: List[str]      ,  __lowerCamelCase		: Any				)      ->     Any:







     '''simple docstring'''






     _a						=		ViTMSNConfig()
     _a						=		1000

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

     if "s16" in checkpoint_url:
          _a						=		384
          _a						=		1536
          _a						=		6
     elif "l16" in checkpoint_url:
          _a						=		1024
          _a						=		4096
          _a						=		24
          _a						=		16
          _a						=		0.1
     elif "b4" in checkpoint_url:
          _a						=		4
     elif "l7" in checkpoint_url:
          _a						=		7
          _a						=		1024
          _a						=		4096
          _a						=		24
          _a						=		16
          _a						=		0.1

     _a						=		ViTMSNModel(__lowerCamelCase				)

     _a						=		torch.hub.load_state_dict_from_url(__lowerCamelCase      ,  map_location="cpu"				)["target_encoder"]

     _a						=		ViTImageProcessor(size=config.image_size				)

     remove_projection_head(__lowerCamelCase				)
     _a						=		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()

     _a						=		"http://images.cocodataset.org/val2017/000000039769.jpg"

     _a						=		Image.open(requests.get(__lowerCamelCase      ,  stream=__lowerCamelCase				).raw				)
     _a						=		ViTImageProcessor(
         size=config.image_size      ,  image_mean=__lowerCamelCase      ,  image_std=__lowerCamelCase				)
     _a						=		image_processor(images=__lowerCamelCase      ,  return_tensors="pt"				)

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

     # 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__":
   lowercase__				     =		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__				     =		parser.parse_args()
   convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				'''convbert'''






     def __init__(			self     ,							__UpperCamelCase=30_522     ,							__UpperCamelCase=768     ,							__UpperCamelCase=12     ,							__UpperCamelCase=12     ,							__UpperCamelCase=3_072     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=512     ,							__UpperCamelCase=2     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=1e-12     ,							__UpperCamelCase=1     ,							__UpperCamelCase=0     ,							__UpperCamelCase=2     ,							__UpperCamelCase=768     ,							__UpperCamelCase=2     ,							__UpperCamelCase=9     ,							__UpperCamelCase=1     ,							__UpperCamelCase=None     ,							**__UpperCamelCase     ,							)       ->    Optional[int]:
          super().__init__(
              pad_token_id=__UpperCamelCase     ,							bos_token_id=__UpperCamelCase     ,							eos_token_id=__UpperCamelCase     ,							**__UpperCamelCase     ,							)

          _a						=		vocab_size
          _a						=		hidden_size
          _a						=		num_hidden_layers
          _a						=		num_attention_heads
          _a						=		intermediate_size
          _a						=		hidden_act
          _a						=		hidden_dropout_prob
          _a						=		attention_probs_dropout_prob
          _a						=		max_position_embeddings
          _a						=		type_vocab_size
          _a						=		initializer_range
          _a						=		layer_norm_eps
          _a						=		embedding_size
          _a						=		head_ratio
          _a						=		conv_kernel_size
          _a						=		num_groups
          _a						=		classifier_dropout







class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     @property
     def  a_					(			self		)       ->    Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _a						=		{0: "batch", 1: "choice", 2: "sequence"}
          else:
               _a						=		{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("token_type_ids", dynamic_axis),
              ]		)

