code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  import json
  import re
  from pathlib import Path
  
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import (
      MobileNetVaConfig,
      MobileNetVaForImageClassification,
      MobileNetVaForSemanticSegmentation,
      MobileNetVaImageProcessor,
      load_tf_weights_in_mobilenet_va,
  )
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  lowerCamelCase_     :     Optional[int]    		=						logging.get_logger(__name__)
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=MobileNetVaConfig(layer_norm_eps=0.001	)
  
      if "quant" in model_name:
          raise ValueError('''Quantized models are not supported.'''	)
  
      a					=re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$''' ,					_UpperCamelCase	)
      if matches:
          a					=float(matches[1]	)
          a					=int(matches[2]	)
  
      if model_name.startswith('''deeplabv3_'''	):
          a					=8
          a					=21
          a					='''pascal-voc-id2label.json'''
      else:
          # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
          # of the usual 1000. The first class (index 0) is "background".
          a					=10_01
          a					='''imagenet-1k-id2label.json'''
  
      a					='''huggingface/label-files'''
      a					=json.load(open(hf_hub_download(_UpperCamelCase ,					_UpperCamelCase ,					repo_type='''dataset'''	) ,					'''r'''	)	)
  
      if config.num_labels == 10_01:
          a					={int(_UpperCamelCase	) + 1: v for k, v in idalabel.items()}
          a					='''background'''
      else:
          a					={int(_UpperCamelCase	): v for k, v in idalabel.items()}
  
      a					=idalabel
      a					={v: k for k, v in idalabel.items()}
  
      return config
  def _A		(		):
  
  
  
  
  
  
  
      """simple docstring"""
      a					='''http://images.cocodataset.org/val2017/000000039769.jpg'''
      a					=Image.open(requests.get(_UpperCamelCase ,					stream=_UpperCamelCase	).raw	)
      return im
  @torch.no_grad()
  def _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase=False	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=get_mobilenet_va_config(_UpperCamelCase	)
  
      # Load 🤗 model
      if model_name.startswith('''deeplabv3_'''	):
          a					=MobileNetVaForSemanticSegmentation(_UpperCamelCase	).eval()
      else:
          a					=MobileNetVaForImageClassification(_UpperCamelCase	).eval()
  
      # Load weights from TensorFlow checkpoint
      load_tf_weights_in_mobilenet_va(_UpperCamelCase ,					_UpperCamelCase ,					_UpperCamelCase	)
  
      # Check outputs on an image, prepared by MobileNetV2ImageProcessor
      a					=MobileNetVaImageProcessor(
          crop_size={'''width''': config.image_size, '''height''': config.image_size} ,					size={'''shortest_edge''': config.image_size + 32} ,					)
      a					=image_processor(images=prepare_img() ,					return_tensors='''pt'''	)
      a					=model(**_UpperCamelCase	)
      a					=outputs.logits
  
      if model_name.startswith('''deeplabv3_'''	):
          assert logits.shape == (1, 21, 65, 65)
  
          if model_name == "deeplabv3_mobilenet_v2_1.0_513":
              a					=torch.tensor(
                  [
                      [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
                      [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                      [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
                  ]	)
  
          else:
              raise ValueError(f'''Unknown model name: {model_name}'''	)
  
          assert torch.allclose(logits[0, :3, :3, :3] ,					_UpperCamelCase ,					atol=1E-4	)
      else:
          assert logits.shape == (1, 10_01)
  
          if model_name == "mobilenet_v2_1.4_224":
              a					=torch.tensor([0.0181, -1.0015, 0.4688]	)
          elif model_name == "mobilenet_v2_1.0_224":
              a					=torch.tensor([0.2445, -1.1993, 0.1905]	)
          elif model_name == "mobilenet_v2_0.75_160":
              a					=torch.tensor([0.2482, 0.4136, 0.6669]	)
          elif model_name == "mobilenet_v2_0.35_96":
              a					=torch.tensor([0.1451, -0.4624, 0.7192]	)
          else:
              a					=None
  
          if expected_logits is not None:
              assert torch.allclose(logits[0, :3] ,					_UpperCamelCase ,					atol=1E-4	)
  
      Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
      print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''	)
      model.save_pretrained(_UpperCamelCase	)
      print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
      image_processor.save_pretrained(_UpperCamelCase	)
  
      if push_to_hub:
          print('''Pushing to the hub...'''	)
          a					='''google/''' + model_name
          image_processor.push_to_hub(_UpperCamelCase	)
          model.push_to_hub(_UpperCamelCase	)
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Dict    		=						argparse.ArgumentParser()
         # Required parameters
         parser.add_argument(
             """--model_name""",
             default="""mobilenet_v2_1.0_224""",
             type=str,
             help="""Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.""",
         )
         parser.add_argument(
             """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
         )
         parser.add_argument(
             """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
         )
         parser.add_argument(
             """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
         )
  
         lowerCamelCase_     :     Union[str, Any]    		=						parser.parse_args()
         convert_movilevit_checkpoint(
             args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
         )
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase_     :     int    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Optional[Any]    		=						{
      """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"vilt"
  
  
   def __init__(	self				,					__A=3_0522				,					__A=2				,					__A=2				,					__A=40				,					__A=768				,					__A=12				,					__A=12				,					__A=3072				,					__A="gelu"				,					__A=0.0				,					__A=0.0				,					__A=0.02				,					__A=1E-1_2				,					__A=384				,					__A=32				,					__A=3				,					__A=True				,					__A=-1				,					__A=False				,					__A=-1				,					**__A				,					)      ->  Dict:
       super().__init__(tie_word_embeddings=__A				,					**__A		)
  
       a					=vocab_size
       a					=type_vocab_size
       a					=modality_type_vocab_size
       a					=max_position_embeddings
  
       a					=hidden_size
       a					=num_hidden_layers
       a					=num_attention_heads
       a					=intermediate_size
       a					=hidden_act
       a					=hidden_dropout_prob
       a					=attention_probs_dropout_prob
       a					=initializer_range
       a					=layer_norm_eps
  
       a					=image_size
       a					=patch_size
       a					=num_channels
       a					=qkv_bias
       a					=max_image_length
       a					=num_images