






"""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()
_snake_case          =   logging.get_logger()

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__ = True				):



  '''simple docstring'''
  print(F"""Converting {name}..."""				)

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

    from_model.eval()
    _a  :       Dict             =	LevitForImageClassificationWithTeacher(UpperCamelCase__				).eval()
    _a  :       Union[str, Any]             =	OrderedDict()

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

    _a  :       int             =	torch.randn((2, 3, 2_2_4, 2_2_4)				)
    _a  :       Dict             =	from_model(UpperCamelCase__				)
    _a  :       List[Any]             =	our_model(UpperCamelCase__				).logits

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

  _a  :       Dict             =	name
  print(UpperCamelCase__				)

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

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

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__ = None     ,   UpperCamelCase__ = True				):



  '''simple docstring'''
  _a  :       Dict             =	"""imagenet-1k-id2label.json"""
  _a  :       int             =	1_0_0_0
  _a  :       Any             =	(1, num_labels)

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

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

  _a  :       int             =	partial(UpperCamelCase__     ,   num_labels=UpperCamelCase__     ,   idalabel=UpperCamelCase__     ,   labelaid=UpperCamelCase__				)

  _a  :       Any             =	{
      """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  :       Tuple             =	{
      """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]     ,   UpperCamelCase__     ,   names_to_config[model_name]     ,   UpperCamelCase__     ,   UpperCamelCase__				)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name]     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)
  return config, expected_shape


if __name__ == "__main__":
 _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',
 )

 _snake_case          =   parser.parse_args()
 _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)








'''simple docstring'''







from ....utils import logging


lowerCAmelCase_					:    Union[str, Any]      	=			logging.get_logger(__name__)
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				def __init__(  self     :					Tuple					,				__a     :					int					,				__a     :					Any=None					,				__a     :					Optional[int]=20_48					):
								_a								= config.__dict__
								_a								= modal_hidden_size
								if num_labels:
												_a								= num_labels
