

'''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()
lowercase__			    =			logging.get_logger()




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_ = True	):
 print(F"""Converting {name}..."""	)

 with torch.no_grad():
  if hidden_sizes == 1_28:
   if name[-1] == "S":
    UpperCAmelCase       :	List[Any]											= timm.create_model('levit_128s'  ,       pretrained=lowerCAmelCase_	)
   else:
    UpperCAmelCase       :	Union[str, Any]											= timm.create_model('levit_128'  ,       pretrained=lowerCAmelCase_	)
  if hidden_sizes == 1_92:
   UpperCAmelCase       :	Optional[int]											= timm.create_model('levit_192'  ,       pretrained=lowerCAmelCase_	)
  if hidden_sizes == 2_56:
   UpperCAmelCase       :	Tuple											= timm.create_model('levit_256'  ,       pretrained=lowerCAmelCase_	)
  if hidden_sizes == 3_84:
   UpperCAmelCase       :	Any											= timm.create_model('levit_384'  ,       pretrained=lowerCAmelCase_	)

  from_model.eval()
  UpperCAmelCase       :	Optional[int]											= LevitForImageClassificationWithTeacher(lowerCAmelCase_	).eval()
  UpperCAmelCase       :	List[Any]											= OrderedDict()

  UpperCAmelCase       :	Optional[Any]											= from_model.state_dict()
  UpperCAmelCase       :	Any											= list(from_model.state_dict().keys()	)
  UpperCAmelCase       :	Tuple											= list(our_model.state_dict().keys()	)
  print(len(lowerCAmelCase_	)  ,       len(lowerCAmelCase_	)	)
  for i in range(len(lowerCAmelCase_	)	):
   UpperCAmelCase       :	Optional[int]											= weights[og_keys[i]]
  our_model.load_state_dict(lowerCAmelCase_	)

  UpperCAmelCase       :	Optional[Any]											= torch.randn((2, 3, 2_24, 2_24)	)
  UpperCAmelCase       :	Union[str, Any]											= from_model(lowerCAmelCase_	)
  UpperCAmelCase       :	List[Any]											= our_model(lowerCAmelCase_	).logits

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

 UpperCAmelCase       :	Optional[Any]											= name
 print(lowerCAmelCase_	)

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

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




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_ = None  ,       UpperCAmelCase_ = True	):
 UpperCAmelCase       :	Dict											= 'imagenet-1k-id2label.json'
 UpperCAmelCase       :	Any											= 10_00
 UpperCAmelCase       :	List[Any]											= (1, num_labels)

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

 UpperCAmelCase       :	str											= idalabel
 UpperCAmelCase       :	int											= {v: k for k, v in idalabel.items()}

 UpperCAmelCase       :	str											= partial(lowerCAmelCase_  ,       num_labels=lowerCAmelCase_  ,       idalabel=lowerCAmelCase_  ,       labelaid=lowerCAmelCase_	)

 UpperCAmelCase       :	List[Any]											= {
     'levit-128S': 1_28,
     'levit-128': 1_28,
     'levit-192': 1_92,
     'levit-256': 2_56,
     'levit-384': 3_84,
 }

 UpperCAmelCase       :	Tuple											= {
     'levit-128S': ImageNetPreTrainedConfig(
         hidden_sizes=[1_28, 2_56, 3_84]  ,       num_attention_heads=[4, 6, 8]  ,       depths=[2, 3, 4]  ,       key_dim=[16, 16, 16]  ,       drop_path_rate=0  ,       ),
     'levit-128': ImageNetPreTrainedConfig(
         hidden_sizes=[1_28, 2_56, 3_84]  ,       num_attention_heads=[4, 8, 12]  ,       depths=[4, 4, 4]  ,       key_dim=[16, 16, 16]  ,       drop_path_rate=0  ,       ),
     'levit-192': ImageNetPreTrainedConfig(
         hidden_sizes=[1_92, 2_88, 3_84]  ,       num_attention_heads=[3, 5, 6]  ,       depths=[4, 4, 4]  ,       key_dim=[32, 32, 32]  ,       drop_path_rate=0  ,       ),
     'levit-256': ImageNetPreTrainedConfig(
         hidden_sizes=[2_56, 3_84, 5_12]  ,       num_attention_heads=[4, 6, 8]  ,       depths=[4, 4, 4]  ,       key_dim=[32, 32, 32]  ,       drop_path_rate=0  ,       ),
     'levit-384': ImageNetPreTrainedConfig(
         hidden_sizes=[3_84, 5_12, 7_68]  ,       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__":
 lowercase__			    =			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",
 )

 lowercase__			    =			parser.parse_args()
 lowercase__			    =			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_      :			str,	lowerCAmelCase_      :			int	):
   return [sentence[i : i + ngram_size] for i in range(len(lowerCAmelCase_	) - ngram_size + 1	)]


if __name__ == "__main__":
     from doctest import testmod

     testmod()

