





"""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()
lowerCamelCase_       =		logging.get_logger(__name__)



def 					__lowerCamelCase       (							a_					:		List[str]							)      ->				Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :Dict		    =  MobileNetVaConfig(layer_norm_eps=0.001							)

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

      __SCREAMING_SNAKE_CASE       :Dict		    =  re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$'''			,       a_							)
      if matches:
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  float(matches[1]							)
            __SCREAMING_SNAKE_CASE       :Any		    =  int(matches[2]							)

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

      return config



def 					__lowerCamelCase       (							)      ->				Dict:
      __SCREAMING_SNAKE_CASE       :Dict		    =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  Image.open(requests.get(a_			,       stream=a_							).raw							)
      return im



@torch.no_grad()
def 					__lowerCamelCase       (							a_					:		List[Any]			,       a_					:		Any			,       a_					:		Any			,       a_					:		Union[str, Any]=False							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :Any		    =  get_mobilenet_va_config(a_							)

      # Load 🤗 model
      __SCREAMING_SNAKE_CASE       :str		    =  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
      __SCREAMING_SNAKE_CASE       :Any		    =  MobileNetVaImageProcessor(
          crop_size={'''width''': config.image_size, '''height''': config.image_size}			,       size={'''shortest_edge''': config.image_size + 32}			,       )
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  image_processor(images=prepare_img()			,       return_tensors='''pt'''							)
      __SCREAMING_SNAKE_CASE       :Any		    =  model(**a_							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  outputs.logits

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

      if model_name == "mobilenet_v1_1.0_224":
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  torch.tensor([-4.1739, -1.1233, 3.1205]							)
      elif model_name == "mobilenet_v1_0.75_192":
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  torch.tensor([-3.9440, -2.3141, -0.3333]							)
      else:
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  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...'''							)
            __SCREAMING_SNAKE_CASE       :List[str]		    =  '''google/''' + model_name
            image_processor.push_to_hub(a_							)
            model.push_to_hub(a_							)


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

  lowerCamelCase_       =		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       (							a_					:		str			,       a_					:		str							)      ->				str:
      __SCREAMING_SNAKE_CASE       :int		    =  len(a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  len(a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  (
          first_str_length if first_str_length > second_str_length else second_str_length
      )
      __SCREAMING_SNAKE_CASE       :list		    =  []
      for char_count in range(a_							):
            if char_count < first_str_length:
                  output_list.append(first_str[char_count]							)
            if char_count < second_str_length:
                  output_list.append(second_str[char_count]							)
      return "".join(a_							)


if __name__ == "__main__":
  print(alternative_string_arrange("AB", "XYZ"), end=" ")