'''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()
lowercase__  :      Optional[int]        =					logging.get_logger(__name__)



def  __lowerCamelCase       (      _UpperCamelCase  :  List[Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  MobileNetVaConfig(layer_norm_eps=0.001					)

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

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

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

      return config



def  __lowerCamelCase       (      ):


      '''simple docstring'''


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



@torch.no_grad()
def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  List[Any]							,    _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  Optional[int]=False					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_mobilenet_va_config(_UpperCamelCase					)

      # Load 🤗 model
      UpperCAmelCase_            =  MobileNetVaForImageClassification(_UpperCamelCase					).eval()

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

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

      assert logits.shape == (1, 1001)

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

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

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

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


if __name__ == "__main__":
      lowercase__  :      str        =					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."
      )

      lowercase__  :      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  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  list[str]					):


      '''simple docstring'''


      UpperCAmelCase_            =  ''''''
      for word_or_phrase in separated:
            if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
                  raise Exception('''join() accepts only strings to be joined'''					)
            joined += word_or_phrase + separator
      return joined.strip(_UpperCamelCase					)


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

      testmod()





