





def       lowerCAmelCase__(	lowercase       :    str				) ->    int:
  __snake_case   :				Any        =       str(lowercase				).strip()
  if not bin_string:
    raise ValueError("Empty string was passed to the function"				)
  __snake_case   :				Any        =       bin_string[0] == "-"
  if is_negative:
    __snake_case   :				Optional[Any]        =       bin_string[1:]
  if not all(char in "01" for char in bin_string				):
    raise ValueError("Non-binary value was passed to the function"				)
  __snake_case   :				Any        =       0
  for char in bin_string:
    __snake_case   :				str        =       2 * decimal_number + int(lowercase				)
  return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
     from doctest import testmod

     testmod()








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
     )

