




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()
_UpperCAmelCase		:  Any	=   logging.get_logger()



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase = True      ):
  print(F"Converting {name}..."      )

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

    from_model.eval()
    lowercase			:int							=  LevitForImageClassificationWithTeacher(lowerCamelCase      ).eval()
    lowercase			:Optional[Any]							=  OrderedDict()

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

    lowercase			:str							=  torch.randn((2, 3, 224, 224)      )
    lowercase			:Dict							=  from_model(lowerCamelCase      )
    lowercase			:Dict							=  our_model(lowerCamelCase      ).logits

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

  lowercase			:Any							=  name
  print(lowerCamelCase      )

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

    print(F"Pushed {checkpoint_name}"      )



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase = None,	lowerCamelCase = True      ):
  lowercase			:Tuple							=  "imagenet-1k-id2label.json"
  lowercase			:int							=  1000
  lowercase			:Optional[int]							=  (1, num_labels)

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

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

  lowercase			:Any							=  partial(lowerCamelCase,	num_labels=lowerCamelCase,	idalabel=lowerCamelCase,	labelaid=lowerCamelCase      )

  lowercase			:Dict							=  {
      "levit-128S": 128,
      "levit-128": 128,
      "levit-192": 192,
      "levit-256": 256,
      "levit-384": 384,
  }

  lowercase			:Optional[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],	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__":
     _UpperCAmelCase		:  List[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",
     )

     _UpperCAmelCase		:  Tuple	=   parser.parse_args()
     _UpperCAmelCase		:  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)









from typing import Union

import fire
import torch
from tqdm import tqdm



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase = "cpu",	lowerCamelCase = None      ):
  lowercase			:Optional[Any]							=  torch.load(lowerCamelCase,	map_location=lowerCamelCase      )
  for k, v in tqdm(state_dict.items()      ):
    if not isinstance(lowerCamelCase,	torch.Tensor      ):
      raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"      )
    lowercase			:List[Any]							=  v.half()
  if save_path is None:  # overwrite src_path
    lowercase			:Optional[Any]							=  src_path
  torch.save(lowerCamelCase,	lowerCamelCase      )


if __name__ == "__main__":
     fire.Fire(convert)



