





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	:       List[Any]    	=							logging.get_logger(__name__)







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   List[Any]:
  __snake_case:						List[Any]			  =						MobileNetVaConfig(layer_norm_eps=0.001)

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

  __snake_case:						Dict			  =						re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$""" , SCREAMING_SNAKE_CASE__)
  if matches:
    __snake_case:						Union[str, Any]			  =						float(matches[1])
    __snake_case:						Tuple			  =						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:						str			  =						1001
  __snake_case:						List[Any]			  =						"""imagenet-1k-id2label.json"""
  __snake_case:						List[Any]			  =						"""huggingface/label-files"""
  __snake_case:						str			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , repo_type="""dataset""") , """r"""))
  __snake_case:						Union[str, Any]			  =						{int(SCREAMING_SNAKE_CASE__) + 1: v for k, v in idalabel.items()}
  __snake_case:						List[str]			  =						"""background"""
  __snake_case:						Optional[int]			  =						idalabel
  __snake_case:						int			  =						{v: k for k, v in idalabel.items()}

  return config







def       A__							(   )       ->   Union[str, Any]:
  __snake_case:						str			  =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  __snake_case:						int			  =						Image.open(requests.get(SCREAMING_SNAKE_CASE__ , stream=SCREAMING_SNAKE_CASE__).raw)
  return im







@torch.no_grad()
def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False)       ->   Dict:
  __snake_case:						List[Any]			  =						get_mobilenet_va_config(SCREAMING_SNAKE_CASE__)

  # Load 🤗 model
  __snake_case:						Optional[int]			  =						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:						Dict			  =						MobileNetVaImageProcessor(
      crop_size={"""width""": config.image_size, """height""": config.image_size} , size={"""shortest_edge""": config.image_size + 32} , )
  __snake_case:						str			  =						image_processor(images=prepare_img() , return_tensors="""pt""")
  __snake_case:						Optional[Any]			  =						model(**SCREAMING_SNAKE_CASE__)
  __snake_case:						Optional[Any]			  =						outputs.logits

  assert logits.shape == (1, 1001)

  if model_name == "mobilenet_v1_1.0_224":
    __snake_case:						Optional[Any]			  =						torch.tensor([-4.1_739, -1.1_233, 3.1_205])
  elif model_name == "mobilenet_v1_0.75_192":
    __snake_case:						Union[str, Any]			  =						torch.tensor([-3.9_440, -2.3_141, -0.3_333])
  else:
    __snake_case:						Tuple			  =						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:						str			  =						"""google/""" + model_name
    image_processor.push_to_hub(SCREAMING_SNAKE_CASE__)
    model.push_to_hub(SCREAMING_SNAKE_CASE__)


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

    __UpperCAmelCase	:       int    	=							parser.parse_args()
    convert_movilevit_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )













import math







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   int:

  if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
    __snake_case:						Optional[int]			  =						F'''Input value of [number={number}] must be an integer'''
    raise TypeError(SCREAMING_SNAKE_CASE__)

  if number < 1:
    __snake_case:						Optional[int]			  =						F'''Input value of [number={number}] must be > 0'''
    raise ValueError(SCREAMING_SNAKE_CASE__)
  elif number == 1:
    return 3
  elif number == 2:
    return 5
  else:
    __snake_case:						List[Any]			  =						int(math.log(number // 3 , 2)) + 2

    __snake_case:						str			  =						[3, 5]
    __snake_case:						int			  =						2
    __snake_case:						List[str]			  =						3
    for block in range(1 , SCREAMING_SNAKE_CASE__):
      for _ in range(SCREAMING_SNAKE_CASE__):
        proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1])
        proth_index += 1
      increment *= 2

  return proth_list[number - 1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      for number in range(11):
            __UpperCAmelCase	:       Optional[int]    	=							0
            try:
                  __UpperCAmelCase	:       int    	=							proth(number)
            except ValueError:
                  print(f'ValueError: there is no {number}th Proth number')
                  continue

            print(f'The {number}th Proth number: {value}')






