


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()
UpperCAmelCase    =					logging.get_logger(__name__)






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      snake_case_ =   MobileNetVaConfig(layer_norm_eps=0.0_0_1      )

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

      snake_case_ =   re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''      ,      SCREAMING_SNAKE_CASE      )
      if matches:
            snake_case_ =   float(matches[1]      )
            snake_case_ =   int(matches[2]      )

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

      return config






def  __lowerCAmelCase					()->						List[str]:




      """simple docstring"""

      snake_case_ =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      snake_case_ =   Image.open(requests.get(SCREAMING_SNAKE_CASE      ,      stream=SCREAMING_SNAKE_CASE      ).raw      )
      return im






@torch.no_grad()
def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=False      )->						int:




      """simple docstring"""

      snake_case_ =   get_mobilenet_va_config(SCREAMING_SNAKE_CASE      )

      # Load 🤗 model
      snake_case_ =   MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE      ).eval()

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

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

      assert logits.shape == (1, 1001)

      if model_name == "mobilenet_v1_1.0_224":
            snake_case_ =   torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]      )
      elif model_name == "mobilenet_v1_0.75_192":
            snake_case_ =   torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]      )
      else:
            snake_case_ =   None

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

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

      if push_to_hub:
            print('''Pushing to the hub...'''      )
            snake_case_ =   '''google/''' + model_name
            image_processor.push_to_hub(SCREAMING_SNAKE_CASE      )
            model.push_to_hub(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
   UpperCAmelCase    =					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."""
   )

   UpperCAmelCase    =					parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = 6008_5147_5143      )->						int:




      """simple docstring"""

      try:
            snake_case_ =   int(SCREAMING_SNAKE_CASE      )
      except (TypeError, ValueError):
            raise TypeError('''Parameter n must be int or castable to int.'''      )
      if n <= 0:
            raise ValueError('''Parameter n must be greater than or equal to one.'''      )
      snake_case_ =   1
      snake_case_ =   2
      while i * i <= n:
            while n % i == 0:
                  snake_case_ =   i
                  n //= i
            i += 1
      if n > 1:
            snake_case_ =   n
      return int(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
   print(f'''{solution() = }''')