




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase       :       Tuple        =    logging.get_logger()




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase						,lowercase						,lowercase						,lowercase = True     )  ->   int:
   print(f"""Converting {name}..."""     )

   with torch.no_grad():
      if hidden_sizes == 128:
         if name[-1] == "S":
            snake_case      :				Union[str, Any]				=       timm.create_model("""levit_128s"""						,pretrained=_UpperCAmelCase     )
         else:
            snake_case      :				int				=       timm.create_model("""levit_128"""						,pretrained=_UpperCAmelCase     )
      if hidden_sizes == 192:
         snake_case      :				List[str]				=       timm.create_model("""levit_192"""						,pretrained=_UpperCAmelCase     )
      if hidden_sizes == 256:
         snake_case      :				List[Any]				=       timm.create_model("""levit_256"""						,pretrained=_UpperCAmelCase     )
      if hidden_sizes == 384:
         snake_case      :				int				=       timm.create_model("""levit_384"""						,pretrained=_UpperCAmelCase     )

      from_model.eval()
      snake_case      :				Optional[Any]				=       LevitForImageClassificationWithTeacher(_UpperCAmelCase     ).eval()
      snake_case      :				List[str]				=       OrderedDict()

      snake_case      :				str				=       from_model.state_dict()
      snake_case      :				Optional[Any]				=       list(from_model.state_dict().keys()     )
      snake_case      :				str				=       list(our_model.state_dict().keys()     )
      print(len(_UpperCAmelCase     )						,len(_UpperCAmelCase     )     )
      for i in range(len(_UpperCAmelCase     )     ):
         snake_case      :				List[Any]				=       weights[og_keys[i]]
      our_model.load_state_dict(_UpperCAmelCase     )

      snake_case      :				int				=       torch.randn((2, 3, 224, 224)     )
      snake_case      :				Optional[int]				=       from_model(_UpperCAmelCase     )
      snake_case      :				Any				=       our_model(_UpperCAmelCase     ).logits

   assert torch.allclose(_UpperCAmelCase						,_UpperCAmelCase     ), "The model logits don't match the original one."

   snake_case      :				Optional[Any]				=       name
   print(_UpperCAmelCase     )

   if push_to_hub:
      our_model.save_pretrained(save_directory / checkpoint_name     )
      snake_case      :				int				=       LevitImageProcessor()
      image_processor.save_pretrained(save_directory / checkpoint_name     )

      print(f"""Pushed {checkpoint_name}"""     )




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase = None						,lowercase = True     )  ->   List[str]:
   snake_case      :				Optional[int]				=       'imagenet-1k-id2label.json'
   snake_case      :				Tuple				=       1000
   snake_case      :				Union[str, Any]				=       (1, num_labels)

   snake_case      :				List[str]				=       'huggingface/label-files'
   snake_case      :				int				=       num_labels
   snake_case      :				Tuple				=       json.load(open(hf_hub_download(_UpperCAmelCase						,_UpperCAmelCase						,repo_type="""dataset"""     )						,"""r"""     )     )
   snake_case      :				List[Any]				=       {int(_UpperCAmelCase     ): v for k, v in idalabel.items()}

   snake_case      :				List[Any]				=       idalabel
   snake_case      :				Union[str, Any]				=       {v: k for k, v in idalabel.items()}

   snake_case      :				Union[str, Any]				=       partial(_UpperCAmelCase						,num_labels=_UpperCAmelCase						,idalabel=_UpperCAmelCase						,labelaid=_UpperCAmelCase     )

   snake_case      :				int				=       {
       'levit-128S': 128,
       'levit-128': 128,
       'levit-192': 192,
       'levit-256': 256,
       'levit-384': 384,
   }

   snake_case      :				int				=       {
       'levit-128S': ImageNetPreTrainedConfig(
           hidden_sizes=[128, 256, 384]						,num_attention_heads=[4, 6, 8]						,depths=[2, 3, 4]						,key_dim=[16, 16, 16]						,drop_path_rate=0						,),
       'levit-128': ImageNetPreTrainedConfig(
           hidden_sizes=[128, 256, 384]						,num_attention_heads=[4, 8, 12]						,depths=[4, 4, 4]						,key_dim=[16, 16, 16]						,drop_path_rate=0						,),
       'levit-192': ImageNetPreTrainedConfig(
           hidden_sizes=[192, 288, 384]						,num_attention_heads=[3, 5, 6]						,depths=[4, 4, 4]						,key_dim=[32, 32, 32]						,drop_path_rate=0						,),
       'levit-256': ImageNetPreTrainedConfig(
           hidden_sizes=[256, 384, 512]						,num_attention_heads=[4, 6, 8]						,depths=[4, 4, 4]						,key_dim=[32, 32, 32]						,drop_path_rate=0						,),
       'levit-384': ImageNetPreTrainedConfig(
           hidden_sizes=[384, 512, 768]						,num_attention_heads=[6, 9, 12]						,depths=[4, 4, 4]						,key_dim=[32, 32, 32]						,drop_path_rate=0.1						,),
   }

   if model_name:
      convert_weight_and_push(
          names_to_hidden_sizes[model_name]						,_UpperCAmelCase						,names_to_config[model_name]						,_UpperCAmelCase						,_UpperCAmelCase     )
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(names_to_hidden_sizes[model_name]						,_UpperCAmelCase						,_UpperCAmelCase						,_UpperCAmelCase						,_UpperCAmelCase     )
   return config, expected_shape


if __name__ == "__main__":
       lowerCamelCase       :       Union[str, Any]        =    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model_name',
           default=None,
           type=str,
           help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path',
           default='levit-dump-folder/',
           type=Path,
           required=False,
           help='Path to the output PyTorch model directory.',
       )
       parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
       parser.add_argument(
           '--no-push_to_hub',
           dest='push_to_hub',
           action='store_false',
           help='Do not push model and image processor to the hub',
       )

       lowerCamelCase       :       Optional[Any]        =    parser.parse_args()
       lowerCamelCase       :       Path        =    args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)












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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     int:
    lowerCamelCase__    :      Optional[int]					 =		[]
    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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Tuple:
    lowerCamelCase__    :      Tuple					 =		[]
    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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Tuple:
    lowerCamelCase__    :      Union[str, Any]					 =		[]
    token.append((F"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token') )
    return token


def        SCREAMING_SNAKE_CASE      (       )			->     str:
    lowerCamelCase__    :      str					 =		[]
    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        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[int]:
    lowerCamelCase__    :      Tuple					 =		'imagenet-1k-id2label.json'
    lowerCamelCase__    :      Union[str, Any]					 =		1000

    lowerCamelCase__    :      Optional[Any]					 =		'huggingface/label-files'
    lowerCamelCase__    :      Any					 =		num_labels
    lowerCamelCase__    :      Dict					 =		json.load(open(cached_download(hf_hub_url(_UpperCAmelCase						,	_UpperCAmelCase						,	repo_type='dataset' ) )						,	'r' ) )
    lowerCamelCase__    :      int					 =		{int(_UpperCAmelCase ): v for k, v in idalabel.items()}

    lowerCamelCase__    :      Tuple					 =		idalabel
    lowerCamelCase__    :      List[Any]					 =		{v: k for k, v in idalabel.items()}

    lowerCamelCase__    :      List[str]					 =		CvtConfig(num_labels=_UpperCAmelCase						,	idalabel=_UpperCAmelCase						,	labelaid=_UpperCAmelCase )

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

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

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        lowerCamelCase__    :      Optional[Any]					 =		[2, 2, 20]
        lowerCamelCase__    :      Optional[int]					 =		[3, 12, 16]
        lowerCamelCase__    :      str					 =		[192, 768, 1024]

    lowerCamelCase__    :      Any					 =		CvtForImageClassification(_UpperCAmelCase )
    lowerCamelCase__    :      Optional[Any]					 =		AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
    lowerCamelCase__    :      Tuple					 =		image_size
    lowerCamelCase__    :      List[str]					 =		torch.load(_UpperCAmelCase						,	map_location=torch.device('cpu' ) )

    lowerCamelCase__    :      Optional[int]					 =		OrderedDict()
    lowerCamelCase__    :      Tuple					 =		[]

    for idx in range(len(config.depth ) ):
        if config.cls_token[idx]:
            lowerCamelCase__    :      Optional[Any]					 =		list_of_state_dict + cls_token(_UpperCAmelCase )
        lowerCamelCase__    :      str					 =		list_of_state_dict + embeddings(_UpperCAmelCase )
        for cnt in range(config.depth[idx] ):
            lowerCamelCase__    :      str					 =		list_of_state_dict + attention(_UpperCAmelCase						,	_UpperCAmelCase )

    lowerCamelCase__    :      int					 =		list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_UpperCAmelCase )
    for i in range(len(_UpperCAmelCase ) ):
        lowerCamelCase__    :      str					 =		original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_UpperCAmelCase )
    model.save_pretrained(_UpperCAmelCase )
    image_processor.save_pretrained(_UpperCAmelCase )


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

if __name__ == "__main__":
    _UpperCAmelCase				:       List[str]	   =				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=3_84,
        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				:       List[str]	   =				parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





