





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							     =			{
    "facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "levit"



     def __init__(   self							:			int   ,       __snake_case							:			Optional[Any]=2_24   ,       __snake_case							:			Any=3   ,       __snake_case							:			Optional[Any]=3   ,       __snake_case							:			Tuple=2   ,       __snake_case							:			Optional[int]=1   ,       __snake_case							:			int=16   ,       __snake_case							:			Optional[int]=[1_28, 2_56, 3_84]   ,       __snake_case							:			Optional[Any]=[4, 8, 12]   ,       __snake_case							:			List[str]=[4, 4, 4]   ,       __snake_case							:			Union[str, Any]=[16, 16, 16]   ,       __snake_case							:			Any=0   ,       __snake_case							:			List[Any]=[2, 2, 2]   ,       __snake_case							:			int=[2, 2, 2]   ,       __snake_case							:			Union[str, Any]=0.02   ,       **__snake_case							:			str   ,       )->	int:
        super().__init__(**__snake_case						)
        snake_case 						=   image_size
        snake_case 						=   num_channels
        snake_case 						=   kernel_size
        snake_case 						=   stride
        snake_case 						=   padding
        snake_case 						=   hidden_sizes
        snake_case 						=   num_attention_heads
        snake_case 						=   depths
        snake_case 						=   key_dim
        snake_case 						=   drop_path_rate
        snake_case 						=   patch_size
        snake_case 						=   attention_ratio
        snake_case 						=   mlp_ratio
        snake_case 						=   initializer_range
        snake_case 						=   [
            ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
            ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
        ]




class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   version.parse("1.11"	)



     @property
     def      lowerCAmelCase			(   self							:			Optional[int]						)->	Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ]						)



     @property
     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	float:
        return 1e-4








'''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							     =			logging.get_logger()





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							LevitConfig      ,    __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							bool = True		)		->  int:
   print(F'''Converting {name}...'''		)

   with torch.no_grad():
      if hidden_sizes == 1_28:
         if name[-1] == "S":
            snake_case 						=   timm.create_model("""levit_128s"""      ,    pretrained=__lowerCAmelCase		)
         else:
            snake_case 						=   timm.create_model("""levit_128"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 1_92:
         snake_case 						=   timm.create_model("""levit_192"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 2_56:
         snake_case 						=   timm.create_model("""levit_256"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 3_84:
         snake_case 						=   timm.create_model("""levit_384"""      ,    pretrained=__lowerCAmelCase		)

      from_model.eval()
      snake_case 						=   LevitForImageClassificationWithTeacher(__lowerCAmelCase		).eval()
      snake_case 						=   OrderedDict()

      snake_case 						=   from_model.state_dict()
      snake_case 						=   list(from_model.state_dict().keys()		)
      snake_case 						=   list(our_model.state_dict().keys()		)
      print(len(__lowerCAmelCase		)      ,    len(__lowerCAmelCase		)		)
      for i in range(len(__lowerCAmelCase		)		):
         snake_case 						=   weights[og_keys[i]]
      our_model.load_state_dict(__lowerCAmelCase		)

      snake_case 						=   torch.randn((2, 3, 2_24, 2_24)		)
      snake_case 						=   from_model(__lowerCAmelCase		)
      snake_case 						=   our_model(__lowerCAmelCase		).logits

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

   snake_case 						=   name
   print(__lowerCAmelCase		)

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

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





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							str = None      ,    __lowerCAmelCase       :							bool = True		)		->  List[Any]:
   snake_case 						=   """imagenet-1k-id2label.json"""
   snake_case 						=   10_00
   snake_case 						=   (1, num_labels)

   snake_case 						=   """huggingface/label-files"""
   snake_case 						=   num_labels
   snake_case 						=   json.load(open(hf_hub_download(__lowerCAmelCase      ,    __lowerCAmelCase      ,    repo_type="""dataset"""		)      ,    """r"""		)		)
   snake_case 						=   {int(__lowerCAmelCase		): v for k, v in idalabel.items()}

   snake_case 						=   idalabel
   snake_case 						=   {v: k for k, v in idalabel.items()}

   snake_case 						=   partial(__lowerCAmelCase      ,    num_labels=__lowerCAmelCase      ,    idalabel=__lowerCAmelCase      ,    labelaid=__lowerCAmelCase		)

   snake_case 						=   {
       """levit-128S""": 1_28,
       """levit-128""": 1_28,
       """levit-192""": 1_92,
       """levit-256""": 2_56,
       """levit-384""": 3_84,
   }

   snake_case 						=   {
       """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__":
 _SCREAMING_SNAKE_CASE							     =			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							     =			parser.parse_args()
 _SCREAMING_SNAKE_CASE							     =			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)

