





'''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,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
A__    :       Any              =logging.get_logger(__name__)
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							MobileNetVaConfig(layer_norm_eps=0.001  )

      if "_quant" in model_name:
            raise ValueError("""Quantized models are not supported."""  )

      _lowerCAmelCase												=							re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$"""							,     A__  )
      if matches:
            _lowerCAmelCase												=							float(matches[1]  )
            _lowerCAmelCase												=							int(matches[2]  )

      # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
      # the usual 1000. The first class (index 0) is "background".
      _lowerCAmelCase												=							10_01
      _lowerCAmelCase												=							"""imagenet-1k-id2label.json"""
      _lowerCAmelCase												=							"""huggingface/label-files"""
      _lowerCAmelCase												=							json.load(open(hf_hub_download(A__							,     A__							,     repo_type="""dataset"""  )							,     """r"""  )  )
      _lowerCAmelCase												=							{int(A__  ) + 1: v for k, v in idalabel.items()}
      _lowerCAmelCase												=							"""background"""
      _lowerCAmelCase												=							idalabel
      _lowerCAmelCase												=							{v: k for k, v in idalabel.items()}

      return config
def        UpperCamelCase__				(		):





      """simple docstring"""


      _lowerCAmelCase												=							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _lowerCAmelCase												=							Image.open(requests.get(A__							,     stream=A__  ).raw  )
      return im
@torch.no_grad()
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=False  ):





      """simple docstring"""


      _lowerCAmelCase												=							get_mobilenet_va_config(A__  )

      # Load 🤗 model
      _lowerCAmelCase												=							MobileNetVaForImageClassification(A__  ).eval()

      # Load weights from TensorFlow checkpoint
      load_tf_weights_in_mobilenet_va(A__							,     A__							,     A__  )

      # Check outputs on an image, prepared by MobileNetV1ImageProcessor
      _lowerCAmelCase												=							MobileNetVaImageProcessor(
          crop_size={"""width""": config.image_size, """height""": config.image_size}							,     size={"""shortest_edge""": config.image_size + 32}							,     )
      _lowerCAmelCase												=							image_processor(images=prepare_img()							,     return_tensors="""pt"""  )
      _lowerCAmelCase												=							model(**A__  )
      _lowerCAmelCase												=							outputs.logits

      assert logits.shape == (1, 10_01)

      if model_name == "mobilenet_v1_1.0_224":
            _lowerCAmelCase												=							torch.tensor([-4.1_739, -1.1_233, 3.1_205]  )
      elif model_name == "mobilenet_v1_0.75_192":
            _lowerCAmelCase												=							torch.tensor([-3.9_440, -2.3_141, -0.3_333]  )
      else:
            _lowerCAmelCase												=							None

      if expected_logits is not None:
            assert torch.allclose(logits[0, :3]							,     A__							,     atol=1e-4  )

      Path(A__  ).mkdir(exist_ok=A__  )
      print(f"Saving model {model_name} to {pytorch_dump_folder_path}"  )
      model.save_pretrained(A__  )
      print(f"Saving image processor to {pytorch_dump_folder_path}"  )
      image_processor.save_pretrained(A__  )

      if push_to_hub:
            print("""Pushing to the hub..."""  )
            _lowerCAmelCase												=							"""google/""" + model_name
            image_processor.push_to_hub(A__  )
            model.push_to_hub(A__  )


if __name__ == "__main__":
      A__    :       Any              =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--model_name''',
          default='''mobilenet_v1_1.0_224''',
          type=str,
          help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<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.'''
      )

      A__    :       Tuple              =parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )













'''simple docstring'''






def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      if len(lowerCAmelCase  ) != len(lowerCAmelCase  ):
            raise ValueError("""String lengths must match!"""  )

      _lowerCAmelCase												=							0

      for chara, chara in zip(lowerCAmelCase							,     lowerCAmelCase  ):
            if chara != chara:
                  count += 1

      return count


if __name__ == "__main__":
      import doctest

      doctest.testmod()






