






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__       		=  logging.get_logger()






def 	UpperCamelCase	(							snake_case__  : int						,snake_case__  : str						,snake_case__  : LevitConfig						,snake_case__  : Path						,snake_case__  : bool = True	):




     '''simple docstring'''






     print(f'''Converting {name}...'''	)

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

          from_model.eval()
          __snake_case      :List[Any] 							=    LevitForImageClassificationWithTeacher(__snake_case	).eval()
          __snake_case      :Union[str, Any] 							=    OrderedDict()

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

          __snake_case      :Dict 							=    torch.randn((2, 3, 224, 224)	)
          __snake_case      :Optional[int] 							=    from_model(__snake_case	)
          __snake_case      :Optional[int] 							=    our_model(__snake_case	).logits

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

     __snake_case      :str 							=    name
     print(__snake_case	)

     if push_to_hub:
          our_model.save_pretrained(save_directory / checkpoint_name	)
          __snake_case      :Optional[int] 							=    LevitImageProcessor()
          image_processor.save_pretrained(save_directory / checkpoint_name	)

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






def 	UpperCamelCase	(							snake_case__  : Path						,snake_case__  : str = None						,snake_case__  : bool = True	):




     '''simple docstring'''






     __snake_case      :str 							=    """imagenet-1k-id2label.json"""
     __snake_case      :Optional[Any] 							=    1000
     __snake_case      :Any 							=    (1, num_labels)

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

     __snake_case      :str 							=    idalabel
     __snake_case      :Optional[int] 							=    {v: k for k, v in idalabel.items()}

     __snake_case      :int 							=    partial(__snake_case						,num_labels=__snake_case						,idalabel=__snake_case						,labelaid=__snake_case	)

     __snake_case      :Optional[Any] 							=    {
         """levit-128S""": 128,
         """levit-128""": 128,
         """levit-192""": 192,
         """levit-256""": 256,
         """levit-384""": 384,
     }

     __snake_case      :Optional[Any] 							=    {
         """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]						,__snake_case						,names_to_config[model_name]						,__snake_case						,__snake_case	)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(names_to_hidden_sizes[model_name]						,__snake_case						,__snake_case						,__snake_case						,__snake_case	)
     return config, expected_shape


if __name__ == "__main__":
     lowerCamelCase__       		=  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__       		=  parser.parse_args()
     lowerCamelCase__       		=  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)






"""simple docstring"""
import numpy as np

def 				_snake_case      (   __snake_case							:      np.ndarray   ):




   """simple docstring"""







   return 1 / (1 + np.exp(-vector   ))

def 				_snake_case      (   __snake_case							:      np.ndarray   ):




   """simple docstring"""







   return vector * sigmoid(__snake_case   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

