

'''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()
A_     :   Union[str, Any]       						=							logging.get_logger()





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case = True ):
  print(F'''Converting {name}...''' )

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

    from_model.eval()
    SCREAMING_SNAKE_CASE:int						      =					LevitForImageClassificationWithTeacher(snake_case ).eval()
    SCREAMING_SNAKE_CASE:str						      =					OrderedDict()

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

    SCREAMING_SNAKE_CASE:int						      =					torch.randn((2, 3, 224, 224) )
    SCREAMING_SNAKE_CASE:Tuple						      =					from_model(snake_case )
    SCREAMING_SNAKE_CASE:str						      =					our_model(snake_case ).logits

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

  SCREAMING_SNAKE_CASE:int						      =					name
  print(snake_case )

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

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





def    A_  ( snake_case      ,       snake_case = None      ,       snake_case = True ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					"""imagenet-1k-id2label.json"""
  SCREAMING_SNAKE_CASE:str						      =					1000
  SCREAMING_SNAKE_CASE:int						      =					(1, num_labels)

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

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					idalabel
  SCREAMING_SNAKE_CASE:Any						      =					{v: k for k, v in idalabel.items()}

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					partial(snake_case      ,       num_labels=snake_case      ,       idalabel=snake_case      ,       labelaid=snake_case )

  SCREAMING_SNAKE_CASE:int						      =					{
      """levit-128S""": 128,
      """levit-128""": 128,
      """levit-192""": 192,
      """levit-256""": 256,
      """levit-384""": 384,
  }

  SCREAMING_SNAKE_CASE:Tuple						      =					{
      """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__":
    A_     :   Optional[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",
    )

    A_     :   Optional[int]       						=							parser.parse_args()
    A_     :   str       						=							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 warnings
from functools import wraps
from typing import Callable





def    A_  ( snake_case ):

  @wraps(snake_case )
  def _inner_fn(*snake_case      ,       **snake_case ):
    warnings.warn(
        (F'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')      ,       snake_case      ,       )
    return fn(*snake_case      ,       **snake_case )

  return _inner_fn






