





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()
_snake_case  :    List[str]						    =  logging.get_logger()

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			LevitConfig,	lowerCAmelCase_      :			Path,	lowerCAmelCase_      :			bool = True	):
   print(F"""Converting {name}..."""	)

   with torch.no_grad():
      if hidden_sizes == 128:
         if name[-1] == "S":
            __lowerCAmelCase								=	timm.create_model('levit_128s',	pretrained=lowerCAmelCase_	)
         else:
            __lowerCAmelCase								=	timm.create_model('levit_128',	pretrained=lowerCAmelCase_	)
      if hidden_sizes == 192:
         __lowerCAmelCase								=	timm.create_model('levit_192',	pretrained=lowerCAmelCase_	)
      if hidden_sizes == 256:
         __lowerCAmelCase								=	timm.create_model('levit_256',	pretrained=lowerCAmelCase_	)
      if hidden_sizes == 384:
         __lowerCAmelCase								=	timm.create_model('levit_384',	pretrained=lowerCAmelCase_	)

      from_model.eval()
      __lowerCAmelCase								=	LevitForImageClassificationWithTeacher(lowerCAmelCase_	).eval()
      __lowerCAmelCase								=	OrderedDict()

      __lowerCAmelCase								=	from_model.state_dict()
      __lowerCAmelCase								=	list(from_model.state_dict().keys()	)
      __lowerCAmelCase								=	list(our_model.state_dict().keys()	)
      print(len(lowerCAmelCase_	),	len(lowerCAmelCase_	)	)
      for i in range(len(lowerCAmelCase_	)	):
         __lowerCAmelCase								=	weights[og_keys[i]]
      our_model.load_state_dict(lowerCAmelCase_	)

      __lowerCAmelCase								=	torch.randn((2, 3, 224, 224)	)
      __lowerCAmelCase								=	from_model(lowerCAmelCase_	)
      __lowerCAmelCase								=	our_model(lowerCAmelCase_	).logits

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

   __lowerCAmelCase								=	name
   print(lowerCAmelCase_	)

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

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

def   a_    ( lowerCAmelCase_      :			Path,	lowerCAmelCase_      :			str = None,	lowerCAmelCase_      :			bool = True	):
   __lowerCAmelCase								=	'imagenet-1k-id2label.json'
   __lowerCAmelCase								=	1000
   __lowerCAmelCase								=	(1, num_labels)

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

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

   __lowerCAmelCase								=	partial(lowerCAmelCase_,	num_labels=lowerCAmelCase_,	idalabel=lowerCAmelCase_,	labelaid=lowerCAmelCase_	)

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

   __lowerCAmelCase								=	{
       '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],	lowerCAmelCase_,	names_to_config[model_name],	lowerCAmelCase_,	lowerCAmelCase_	)
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(names_to_hidden_sizes[model_name],	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	)
   return config, expected_shape


if __name__ == "__main__":
     _snake_case  :    Optional[int]						    =  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',
     )

     _snake_case  :    int						    =  parser.parse_args()
     _snake_case  :    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)








def   a_    ( lowerCAmelCase_      :			int	):
   if number < 0:
      raise ValueError('number must not be negative'	)
   return number & (number - 1) == 0


if __name__ == "__main__":
     import doctest

     doctest.testmod()

