





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 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[Any]   )      ->   Any:
 __lowerCAmelCase					:	Any      =       MobileNetVaConfig(layer_norm_eps=0.0_01   )

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

 __lowerCAmelCase					:	Optional[int]      =       re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$"""   ,   lowerCamelCase_   )
 if matches:
  __lowerCAmelCase					:	Dict      =       float(matches[1]   )
  __lowerCAmelCase					:	List[str]      =       int(matches[2]   )

 # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
 # the usual 1000. The first class (index 0) is "background".
 __lowerCAmelCase					:	Optional[Any]      =       1_001
 __lowerCAmelCase					:	Optional[Any]      =       """imagenet-1k-id2label.json"""
 __lowerCAmelCase					:	List[str]      =       """huggingface/label-files"""
 __lowerCAmelCase					:	str      =       json.load(open(hf_hub_download(lowerCamelCase_   ,   lowerCamelCase_   ,   repo_type="""dataset"""   )   ,   """r"""   )   )
 __lowerCAmelCase					:	List[str]      =       {int(lowerCamelCase_   ) + 1: v for k, v in idalabel.items()}
 __lowerCAmelCase					:	Union[str, Any]      =       """background"""
 __lowerCAmelCase					:	Dict      =       idalabel
 __lowerCAmelCase					:	Optional[int]      =       {v: k for k, v in idalabel.items()}

 return config






def 	_SCREAMING_SNAKE_CASE		(      )      ->   Optional[Any]:
 __lowerCAmelCase					:	Tuple      =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
 __lowerCAmelCase					:	Any      =       Image.open(requests.get(lowerCamelCase_   ,   stream=lowerCamelCase_   ).raw   )
 return im






@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Dict   ,   SCREAMING_SNAKE_CASE		:List[Any]   ,   SCREAMING_SNAKE_CASE		:Union[str, Any]   ,   SCREAMING_SNAKE_CASE		:List[Any]=False   )      ->   List[str]:
 __lowerCAmelCase					:	str      =       get_mobilenet_va_config(lowerCamelCase_   )

 # Load 🤗 model
 __lowerCAmelCase					:	List[Any]      =       MobileNetVaForImageClassification(lowerCamelCase_   ).eval()

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

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

 assert logits.shape == (1, 1_001)

 if model_name == "mobilenet_v1_1.0_224":
  __lowerCAmelCase					:	Optional[Any]      =       torch.tensor([-4.17_39, -1.12_33, 3.12_05]   )
 elif model_name == "mobilenet_v1_0.75_192":
  __lowerCAmelCase					:	Optional[int]      =       torch.tensor([-3.94_40, -2.31_41, -0.33_33]   )
 else:
  __lowerCAmelCase					:	List[Any]      =       None

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

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

 if push_to_hub:
  print("""Pushing to the hub..."""   )
  __lowerCAmelCase					:	Optional[Any]      =       """google/""" + model_name
  image_processor.push_to_hub(lowerCamelCase_   )
  model.push_to_hub(lowerCamelCase_   )


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
      )






from math import isqrt






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   )      ->   list[int]:
 __lowerCAmelCase					:	Tuple      =       [True] * max_number
 for i in range(2   ,   isqrt(max_number - 1   ) + 1   ):
  if is_prime[i]:
   for j in range(i**2   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
    __lowerCAmelCase					:	Tuple      =       False

 return [i for i in range(2   ,   SCREAMING_SNAKE_CASE   ) if is_prime[i]]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int = 10**8   )      ->   int:
 __lowerCAmelCase					:	int      =       calculate_prime_numbers(max_number // 2   )

 __lowerCAmelCase					:	List[Any]      =       0
 __lowerCAmelCase					:	List[str]      =       0
 __lowerCAmelCase					:	List[Any]      =       len(SCREAMING_SNAKE_CASE   ) - 1
 while left <= right:
  while prime_numbers[left] * prime_numbers[right] >= max_number:
   right -= 1
  semiprimes_count += right - left + 1
  left += 1

 return semiprimes_count


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