code: 
  
  
  
  
  
  # Copyright 2022 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from pathlib import Path
  
  from .config_args import default_config_file, load_config_from_file
  from .config_utils import SubcommandHelpFormatter
  
  
  _UpperCamelCase       = """Update an existing config file with the latest defaults while maintaining the old configuration."""
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    Union[str, Any]				) ->    Union[str, Any]:
    __snake_case   :				str        =       args.config_file
    if config_file is None and Path(lowerCAmelCase__				).exists():
      __snake_case   :				Dict        =       default_config_file
    elif not Path(lowerCAmelCase__				).exists():
      raise ValueError(f"""The passed config file located at {config_file} doesn't exist."""				)
    __snake_case   :				Any        =       load_config_from_file(lowerCAmelCase__				)
  
    if config_file.endswith(".json"				):
      config.to_json_file(lowerCAmelCase__				)
    else:
      config.to_yaml_file(lowerCAmelCase__				)
    return config_file
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    Optional[int]   ,     lowercase       :    List[str]				) ->    str:
    __snake_case   :				Dict        =       parser.add_parser("update"   ,     parents=lowerCAmelCase__   ,     help=lowerCAmelCase__   ,     formatter_class=lowerCAmelCase__				)
    parser.add_argument(
        "--config_file"   ,     default=lowerCAmelCase__   ,     help=(
            "The path to the config file to update. Will default to a file named default_config.yaml in the cache "
            "location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have "
            "such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed "
            "with \'huggingface\'."
        )   ,     )
  
    parser.set_defaults(func=lowerCAmelCase__				)
    return parser
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    Dict				) ->    Tuple:
    __snake_case   :				Tuple        =       update_config(lowerCAmelCase__				)
    print(f"""Sucessfully updated the configuration file at {config_file}."""				)
  
  
style_context: 
  
  
  
  
  
  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()
  _UpperCamelCase       = logging.get_logger(__name__)
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	lowercase       :    Dict				) ->    Union[str, Any]:
    __snake_case   :				List[str]        =       MobileNetVaConfig(layer_norm_eps=0.0_0_1				)
  
    if "quant" in model_name:
      raise ValueError("Quantized models are not supported."				)
  
    __snake_case   :				Dict        =       re.match(R"^.*mobilenet_v2_([^_]*)_([^_]*)$"   ,     lowercase				)
    if matches:
      __snake_case   :				Tuple        =       float(matches[1]				)
      __snake_case   :				List[Any]        =       int(matches[2]				)
  
    if model_name.startswith("deeplabv3_"				):
      __snake_case   :				Any        =       8
      __snake_case   :				Any        =       21
      __snake_case   :				Optional[Any]        =       "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".
      __snake_case   :				Optional[Any]        =       1001
      __snake_case   :				str        =       "imagenet-1k-id2label.json"
  
    __snake_case   :				Any        =       "huggingface/label-files"
    __snake_case   :				str        =       json.load(open(hf_hub_download(lowercase   ,     lowercase   ,     repo_type="dataset"				)   ,     "r"				)				)
  
    if config.num_labels == 1001:
      __snake_case   :				Optional[Any]        =       {int(lowercase				) + 1: v for k, v in idalabel.items()}
      __snake_case   :				Any        =       "background"
    else:
      __snake_case   :				Optional[Any]        =       {int(lowercase				): v for k, v in idalabel.items()}
  
    __snake_case   :				Any        =       idalabel
    __snake_case   :				List[str]        =       {v: k for k, v in idalabel.items()}
  
    return config
  
  
  
  
  
  
  
  def       lowerCAmelCase__(	) ->    Optional[Any]:
    __snake_case   :				List[Any]        =       "http://images.cocodataset.org/val2017/000000039769.jpg"
    __snake_case   :				Union[str, Any]        =       Image.open(requests.get(lowercase   ,     stream=lowercase				).raw				)
    return im
  
  
  
  
  
  
  
  @torch.no_grad()
  def       lowerCAmelCase__(	lowercase       :    str   ,     lowercase       :    List[str]   ,     lowercase       :    int   ,     lowercase       :    List[str]=False				) ->    str:
    __snake_case   :				Dict        =       get_mobilenet_va_config(lowercase				)
  
    # Load 🤗 model
    if model_name.startswith("deeplabv3_"				):
      __snake_case   :				str        =       MobileNetVaForSemanticSegmentation(lowercase				).eval()
    else:
      __snake_case   :				Any        =       MobileNetVaForImageClassification(lowercase				).eval()
  
    # Load weights from TensorFlow checkpoint
    load_tf_weights_in_mobilenet_va(lowercase   ,     lowercase   ,     lowercase				)
  
    # Check outputs on an image, prepared by MobileNetV2ImageProcessor
    __snake_case   :				List[str]        =       MobileNetVaImageProcessor(
        crop_size={"width": config.image_size, "height": config.image_size}   ,     size={"shortest_edge": config.image_size + 32}   ,     )
    __snake_case   :				Any        =       image_processor(images=prepare_img()   ,     return_tensors="pt"				)
    __snake_case   :				Dict        =       model(**lowercase				)
    __snake_case   :				List[Any]        =       outputs.logits
  
    if model_name.startswith("deeplabv3_"				):
      assert logits.shape == (1, 21, 65, 65)
  
      if model_name == "deeplabv3_mobilenet_v2_1.0_513":
        __snake_case   :				Any        =       torch.tensor(
            [
                [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
                [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
                [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
            ]				)
  
      else:
        raise ValueError(f"""Unknown model name: {model_name}"""				)
  
      assert torch.allclose(logits[0, :3, :3, :3]   ,     lowercase   ,     atol=1E-4				)
    else:
      assert logits.shape == (1, 1001)
  
      if model_name == "mobilenet_v2_1.4_224":
        __snake_case   :				List[str]        =       torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]				)
      elif model_name == "mobilenet_v2_1.0_224":
        __snake_case   :				str        =       torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]				)
      elif model_name == "mobilenet_v2_0.75_160":
        __snake_case   :				Dict        =       torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]				)
      elif model_name == "mobilenet_v2_0.35_96":
        __snake_case   :				List[Any]        =       torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]				)
      else:
        __snake_case   :				int        =       None
  
      if expected_logits is not None:
        assert torch.allclose(logits[0, :3]   ,     lowercase   ,     atol=1E-4				)
  
    Path(lowercase				).mkdir(exist_ok=lowercase				)
    print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
    model.save_pretrained(lowercase				)
    print(f"""Saving image processor to {pytorch_dump_folder_path}"""				)
    image_processor.save_pretrained(lowercase				)
  
    if push_to_hub:
      print("Pushing to the hub..."				)
      __snake_case   :				Optional[Any]        =       "google/" + model_name
      image_processor.push_to_hub(lowercase				)
      model.push_to_hub(lowercase				)
  
  
  if __name__ == "__main__":
       _UpperCamelCase       = 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.'''
       )
  
       _UpperCamelCase       = parser.parse_args()
       convert_movilevit_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )
  
  