


'''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()
UpperCAmelCase_      :		int          =		logging.get_logger()


def    UpperCAmelCase_  (       A		,	A		,	A		,	A		,	A = True      ):
   '''simple docstring'''





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

   with torch.no_grad():
      if hidden_sizes == 1_2_8:
         if name[-1] == "S":
            _a					:  Tuple						=  timm.create_model('levit_128s'		,	pretrained=A      )
         else:
            _a					:  Tuple						=  timm.create_model('levit_128'		,	pretrained=A      )
      if hidden_sizes == 1_9_2:
         _a					:  int						=  timm.create_model('levit_192'		,	pretrained=A      )
      if hidden_sizes == 2_5_6:
         _a					:  List[Any]						=  timm.create_model('levit_256'		,	pretrained=A      )
      if hidden_sizes == 3_8_4:
         _a					:  int						=  timm.create_model('levit_384'		,	pretrained=A      )

      from_model.eval()
      _a					:  Dict						=  LevitForImageClassificationWithTeacher(A      ).eval()
      _a					:  Dict						=  OrderedDict()

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

      _a					:  Any						=  torch.randn((2, 3, 2_2_4, 2_2_4)      )
      _a					:  Tuple						=  from_model(A      )
      _a					:  Union[str, Any]						=  our_model(A      ).logits

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

   _a					:  Tuple						=  name
   print(A      )

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

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


def    UpperCAmelCase_  (       A		,	A = None		,	A = True      ):
   '''simple docstring'''





   _a					:  str						=  'imagenet-1k-id2label.json'
   _a					:  Tuple						=  1_0_0_0
   _a					:  str						=  (1, num_labels)

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

   _a					:  int						=  idalabel
   _a					:  Dict						=  {v: k for k, v in idalabel.items()}

   _a					:  Tuple						=  partial(A		,	num_labels=A		,	idalabel=A		,	labelaid=A      )

   _a					:  Optional[int]						=  {
       'levit-128S': 1_2_8,
       'levit-128': 1_2_8,
       'levit-192': 1_9_2,
       'levit-256': 2_5_6,
       'levit-384': 3_8_4,
   }

   _a					:  str						=  {
       'levit-128S': ImageNetPreTrainedConfig(
           hidden_sizes=[1_2_8, 2_5_6, 3_8_4]		,	num_attention_heads=[4, 6, 8]		,	depths=[2, 3, 4]		,	key_dim=[1_6, 1_6, 1_6]		,	drop_path_rate=0		,	),
       'levit-128': ImageNetPreTrainedConfig(
           hidden_sizes=[1_2_8, 2_5_6, 3_8_4]		,	num_attention_heads=[4, 8, 1_2]		,	depths=[4, 4, 4]		,	key_dim=[1_6, 1_6, 1_6]		,	drop_path_rate=0		,	),
       'levit-192': ImageNetPreTrainedConfig(
           hidden_sizes=[1_9_2, 2_8_8, 3_8_4]		,	num_attention_heads=[3, 5, 6]		,	depths=[4, 4, 4]		,	key_dim=[3_2, 3_2, 3_2]		,	drop_path_rate=0		,	),
       'levit-256': ImageNetPreTrainedConfig(
           hidden_sizes=[2_5_6, 3_8_4, 5_1_2]		,	num_attention_heads=[4, 6, 8]		,	depths=[4, 4, 4]		,	key_dim=[3_2, 3_2, 3_2]		,	drop_path_rate=0		,	),
       'levit-384': ImageNetPreTrainedConfig(
           hidden_sizes=[3_8_4, 5_1_2, 7_6_8]		,	num_attention_heads=[6, 9, 1_2]		,	depths=[4, 4, 4]		,	key_dim=[3_2, 3_2, 3_2]		,	drop_path_rate=0.1		,	),
   }

   if model_name:
      convert_weight_and_push(
          names_to_hidden_sizes[model_name]		,	A		,	names_to_config[model_name]		,	A		,	A      )
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(names_to_hidden_sizes[model_name]		,	A		,	A		,	A		,	A      )
   return config, expected_shape


if __name__ == "__main__":
 UpperCAmelCase_      :		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",
 )

 UpperCAmelCase_      :		List[Any]          =		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)






'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_      :		Tuple          =		logging.get_logger(__name__)







class 					a			(				snake_case__  ):
 '''simple docstring'''




 def __init__(    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    None:
    warnings.warn(
        'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
        ' Please use PerceiverImageProcessor instead.'		,      lowerCamelCase_		,      )
    super().__init__(*lowerCamelCase_		,      **lowerCamelCase_      )


