


"""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()
snake_case__						:   Any			 = logging.get_logger(__name__)


def    _snake_case  (  _snake_case :	str	):
      lowerCAmelCase    :		Optional[int]				=      MobileNetVaConfig(layer_norm_eps=0.001	)

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

      lowerCAmelCase    :		Union[str, Any]				=      re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$'''       ,      _snake_case	)
      if matches:
            lowerCAmelCase    :		List[Any]				=      float(matches[1]	)
            lowerCAmelCase    :		Optional[Any]				=      int(matches[2]	)

      # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
      # the usual 1000. The first class (index 0) is "background".
      lowerCAmelCase    :		Tuple				=      1001
      lowerCAmelCase    :		Optional[int]				=      '''imagenet-1k-id2label.json'''
      lowerCAmelCase    :		List[Any]				=      '''huggingface/label-files'''
      lowerCAmelCase    :		str				=      json.load(open(hf_hub_download(_snake_case       ,      _snake_case       ,      repo_type='''dataset'''	)       ,      '''r'''	)	)
      lowerCAmelCase    :		List[str]				=      {int(_snake_case	) + 1: v for k, v in idalabel.items()}
      lowerCAmelCase    :		Optional[int]				=      '''background'''
      lowerCAmelCase    :		Tuple				=      idalabel
      lowerCAmelCase    :		Union[str, Any]				=      {v: k for k, v in idalabel.items()}

      return config


def    _snake_case  (  ):
      lowerCAmelCase    :		Any				=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase    :		List[str]				=      Image.open(requests.get(_snake_case       ,      stream=_snake_case	).raw	)
      return im


@torch.no_grad()
def    _snake_case  (  _snake_case :	List[str]       ,      _snake_case :	Optional[Any]       ,      _snake_case :	Any       ,      _snake_case :	Tuple=False	):
      lowerCAmelCase    :		Dict				=      get_mobilenet_va_config(_snake_case	)

      # Load 🤗 model
      lowerCAmelCase    :		Union[str, Any]				=      MobileNetVaForImageClassification(_snake_case	).eval()

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

      # Check outputs on an image, prepared by MobileNetV1ImageProcessor
      lowerCAmelCase    :		Dict				=      MobileNetVaImageProcessor(
          crop_size={'''width''': config.image_size, '''height''': config.image_size}       ,      size={'''shortest_edge''': config.image_size + 32}       ,      )
      lowerCAmelCase    :		Dict				=      image_processor(images=prepare_img()       ,      return_tensors='''pt'''	)
      lowerCAmelCase    :		List[Any]				=      model(**_snake_case	)
      lowerCAmelCase    :		Optional[Any]				=      outputs.logits

      assert logits.shape == (1, 1001)

      if model_name == "mobilenet_v1_1.0_224":
            lowerCAmelCase    :		str				=      torch.tensor([-4.1739, -1.1233, 3.1205]	)
      elif model_name == "mobilenet_v1_0.75_192":
            lowerCAmelCase    :		Any				=      torch.tensor([-3.9440, -2.3141, -0.3333]	)
      else:
            lowerCAmelCase    :		Union[str, Any]				=      None

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

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

      if push_to_hub:
            print('''Pushing to the hub...'''	)
            lowerCAmelCase    :		Optional[Any]				=      '''google/''' + model_name
            image_processor.push_to_hub(_snake_case	)
            model.push_to_hub(_snake_case	)


if __name__ == "__main__":
 snake_case__						:   Tuple			 = 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.'''
 )

 snake_case__						:   Optional[Any]			 = parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )






"""simple docstring"""





def    _snake_case  (  _snake_case :	int	):

      if not isinstance(_snake_case       ,      _snake_case	):
            raise TypeError('''only integers accepted as input'''	)
      else:
            lowerCAmelCase    :		List[str]				=      str(abs(_snake_case	)	)
            lowerCAmelCase    :		Optional[Any]				=      [list(_snake_case	) for char in range(len(_snake_case	)	)]
            for index in range(len(_snake_case	)	):
                  num_transpositions[index].pop(_snake_case	)
            return max(
                int(''''''.join(list(_snake_case	)	)	) for transposition in num_transpositions	)


if __name__ == "__main__":
 __import__('''doctest''').testmod()


