






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def 						_a    (      a :int )							->			List[str]:
 if "cls_token" in name:
  a		  =      name.replace('''cls_token'''	,							'''vit.embeddings.cls_token''' )
 if "mask_token" in name:
  a		  =      name.replace('''mask_token'''	,							'''decoder.mask_token''' )
 if "decoder_pos_embed" in name:
  a		  =      name.replace('''decoder_pos_embed'''	,							'''decoder.decoder_pos_embed''' )
 if "pos_embed" in name and "decoder" not in name:
  a		  =      name.replace('''pos_embed'''	,							'''vit.embeddings.position_embeddings''' )
 if "patch_embed.proj" in name:
  a		  =      name.replace('''patch_embed.proj'''	,							'''vit.embeddings.patch_embeddings.projection''' )
 if "patch_embed.norm" in name:
  a		  =      name.replace('''patch_embed.norm'''	,							'''vit.embeddings.norm''' )
 if "decoder_blocks" in name:
  a		  =      name.replace('''decoder_blocks'''	,							'''decoder.decoder_layers''' )
 if "blocks" in name:
  a		  =      name.replace('''blocks'''	,							'''vit.encoder.layer''' )
 if "attn.proj" in name:
  a		  =      name.replace('''attn.proj'''	,							'''attention.output.dense''' )
 if "attn" in name:
  a		  =      name.replace('''attn'''	,							'''attention.self''' )
 if "norm1" in name:
  a		  =      name.replace('''norm1'''	,							'''layernorm_before''' )
 if "norm2" in name:
  a		  =      name.replace('''norm2'''	,							'''layernorm_after''' )
 if "mlp.fc1" in name:
  a		  =      name.replace('''mlp.fc1'''	,							'''intermediate.dense''' )
 if "mlp.fc2" in name:
  a		  =      name.replace('''mlp.fc2'''	,							'''output.dense''' )
 if "decoder_embed" in name:
  a		  =      name.replace('''decoder_embed'''	,							'''decoder.decoder_embed''' )
 if "decoder_norm" in name:
  a		  =      name.replace('''decoder_norm'''	,							'''decoder.decoder_norm''' )
 if "decoder_pred" in name:
  a		  =      name.replace('''decoder_pred'''	,							'''decoder.decoder_pred''' )
 if "norm.weight" in name and "decoder" not in name:
  a		  =      name.replace('''norm.weight'''	,							'''vit.layernorm.weight''' )
 if "norm.bias" in name and "decoder" not in name:
  a		  =      name.replace('''norm.bias'''	,							'''vit.layernorm.bias''' )

 return name
def 						_a    (      a :Union[str, Any]	,							a :Dict )							->			str:
 for key in orig_state_dict.copy().keys():
  a		  =      orig_state_dict.pop(a )

  if "qkv" in key:
   a		  =      key.split('''.''' )
   a		  =      int(key_split[1] )
   if "decoder_blocks" in key:
    a		  =      config.decoder_hidden_size
    a		  =      '''decoder.decoder_layers.'''
    if "weight" in key:
     a		  =      val[:dim, :]
     a		  =      val[dim : dim * 2, :]
     a		  =      val[-dim:, :]
    elif "bias" in key:
     a		  =      val[:dim]
     a		  =      val[dim : dim * 2]
     a		  =      val[-dim:]
   else:
    a		  =      config.hidden_size
    a		  =      '''vit.encoder.layer.'''
    if "weight" in key:
     a		  =      val[:dim, :]
     a		  =      val[dim : dim * 2, :]
     a		  =      val[-dim:, :]
    elif "bias" in key:
     a		  =      val[:dim]
     a		  =      val[dim : dim * 2]
     a		  =      val[-dim:]

  else:
   a		  =      val

 return orig_state_dict
def 						_a    (      a :Any	,							a :int )							->			List[str]:
 a		  =      ViTMAEConfig()
 if "large" in checkpoint_url:
  a		  =      1_024
  a		  =      4_096
  a		  =      24
  a		  =      16
 elif "huge" in checkpoint_url:
  a		  =      14
  a		  =      1_280
  a		  =      5_120
  a		  =      32
  a		  =      16

 a		  =      ViTMAEForPreTraining(a )

 a		  =      torch.hub.load_state_dict_from_url(a	,							map_location='''cpu''' )['''model''']

 a		  =      ViTMAEImageProcessor(size=config.image_size )

 a		  =      convert_state_dict(a	,							a )

 model.load_state_dict(a )
 model.eval()

 a		  =      '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

 a		  =      Image.open(requests.get(a	,							stream=a ).raw )
 a		  =      ViTMAEImageProcessor(size=config.image_size )
 a		  =      image_processor(images=a	,							return_tensors='''pt''' )

 # forward pass
 torch.manual_seed(2 )
 a		  =      model(**a )
 a		  =      outputs.logits

 if "large" in checkpoint_url:
  a		  =      torch.tensor(
      [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]] )
 elif "huge" in checkpoint_url:
  a		  =      torch.tensor(
      [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]] )
 else:
  a		  =      torch.tensor(
      [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]] )

 # verify logits
 assert torch.allclose(logits[0, :3, :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__":
     UpperCAmelCase__				   =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--checkpoint_url",
         default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
         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."
     )

     UpperCAmelCase__				   =		parser.parse_args()
     convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def 						_a    (      a :List[Any] )							->			Optional[int]:
 a		  =      []
 embed.append(
     (
         F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
         F"""stage{idx}.patch_embed.proj.weight""",
     ) )
 embed.append(
     (
         F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
         F"""stage{idx}.patch_embed.proj.bias""",
     ) )
 embed.append(
     (
         F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
         F"""stage{idx}.patch_embed.norm.weight""",
     ) )
 embed.append(
     (
         F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
         F"""stage{idx}.patch_embed.norm.bias""",
     ) )
 return embed
def 						_a    (      a :List[Any]	,							a :Optional[int] )							->			Dict:
 a		  =      []
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
         F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
         F"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
     ) )
 attention_weights.append(
     (
         F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
         F"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
     ) )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", F"""stage{idx}.blocks.{cnt}.norm1.weight""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", F"""stage{idx}.blocks.{cnt}.norm1.bias""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", F"""stage{idx}.blocks.{cnt}.norm2.weight""") )
 attention_weights.append(
     (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", F"""stage{idx}.blocks.{cnt}.norm2.bias""") )
 return attention_weights
def 						_a    (      a :Any )							->			List[Any]:
 a		  =      []
 token.append((F"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''') )
 return token
def 						_a    (      )							->			Optional[int]:
 a		  =      []
 head.append(('''layernorm.weight''', '''norm.weight''') )
 head.append(('''layernorm.bias''', '''norm.bias''') )
 head.append(('''classifier.weight''', '''head.weight''') )
 head.append(('''classifier.bias''', '''head.bias''') )
 return head
def 						_a    (      a :Tuple	,							a :Optional[int]	,							a :List[Any]	,							a :Union[str, Any] )							->			Optional[int]:
 a		  =      '''imagenet-1k-id2label.json'''
 a		  =      1_000

 a		  =      '''huggingface/label-files'''
 a		  =      num_labels
 a		  =      json.load(open(cached_download(hf_hub_url(a	,							a	,							repo_type='''dataset''' ) )	,							'''r''' ) )
 a		  =      {int(a ): v for k, v in idalabel.items()}

 a		  =      idalabel
 a		  =      {v: k for k, v in idalabel.items()}

 a		  =      a		  =      CvtConfig(num_labels=a	,							idalabel=a	,							labelaid=a )

 # For depth size 13 (13 = 1+2+10)
 if cvt_model.rsplit('''/'''	,							1 )[-1][4:6] == "13":
  a		  =      [1, 2, 10]

 # For depth size 21 (21 = 1+4+16)
 elif cvt_model.rsplit('''/'''	,							1 )[-1][4:6] == "21":
  a		  =      [1, 4, 16]

 # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
 else:
  a		  =      [2, 2, 20]
  a		  =      [3, 12, 16]
  a		  =      [192, 768, 1_024]

 a		  =      CvtForImageClassification(a )
 a		  =      AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' )
 a		  =      image_size
 a		  =      torch.load(a	,							map_location=torch.device('''cpu''' ) )

 a		  =      OrderedDict()
 a		  =      []

 for idx in range(len(config.depth ) ):
  if config.cls_token[idx]:
   a		  =      list_of_state_dict + cls_token(a )
  a		  =      list_of_state_dict + embeddings(a )
  for cnt in range(config.depth[idx] ):
   a		  =      list_of_state_dict + attention(a	,							a )

 a		  =      list_of_state_dict + final()
 for gg in list_of_state_dict:
  print(a )
 for i in range(len(a ) ):
  a		  =      original_weights[list_of_state_dict[i][1]]

 model.load_state_dict(a )
 model.save_pretrained(a )
 image_processor.save_pretrained(a )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
     UpperCAmelCase__				   =		argparse.ArgumentParser()
     parser.add_argument(
         "--cvt_model",
         default="cvt-w24",
         type=str,
         help="Name of the cvt model you'd like to convert.",
     )
     parser.add_argument(
         "--image_size",
         default=384,
         type=int,
         help="Input Image Size",
     )
     parser.add_argument(
         "--cvt_file_name",
         default=R"cvtmodels\CvT-w24-384x384-IN-22k.pth",
         type=str,
         help="Input Image Size",
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
     )

     UpperCAmelCase__				   =		parser.parse_args()
     convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)


