

"""simple docstring"""




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()
SCREAMING_SNAKE_CASE_     :			Dict	  =							logging.get_logger()



def 	UpperCAmelCase__   (				A__	,    A__	,    A__	,    A__	,    A__ = True   )      ->      Tuple:




   """simple docstring"""
   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 	UpperCAmelCase__   (				A__	,    A__ = None	,    A__ = True   )      ->      Union[str, Any]:




   """simple docstring"""
   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__":
     SCREAMING_SNAKE_CASE_     :			Tuple	  =							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''',
     )

     SCREAMING_SNAKE_CASE_     :			List[Any]	  =							parser.parse_args()
     SCREAMING_SNAKE_CASE_     :			Dict	  =							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"""




def 	UpperCAmelCase__   (				)      ->      int:




   """simple docstring"""
   return 1



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else two_pence(x - 2   ) + one_pence()



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else five_pence(x - 5   ) + two_pence(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else ten_pence(x - 10   ) + five_pence(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else twenty_pence(x - 20   ) + ten_pence(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else fifty_pence(x - 50   ) + twenty_pence(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else one_pound(x - 100   ) + fifty_pence(A__   )



def 	UpperCAmelCase__   (				A__   )      ->      int:




   """simple docstring"""
   return 0 if x < 0 else two_pound(x - 200   ) + one_pound(A__   )



def 	UpperCAmelCase__   (				A__ = 200   )      ->      int:




   """simple docstring"""
   return two_pound(A__   )


if __name__ == "__main__":
     print(solution(int(input().strip())))




