





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()
a__: Any							   =						logging.get_logger(__name__)


def       UpperCamelCase__(			UpperCamelCase__     :			List[Any]    )->int:
       A__		   =		MobileNetVaConfig(layer_norm_eps=0.001    )

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

       A__		   =		re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$'''					,	UpperCamelCase__    )
       if matches:
              A__		   =		float(matches[1]    )
              A__		   =		int(matches[2]    )

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

       return config


def       UpperCamelCase__(			)->List[Any]:
       A__		   =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       A__		   =		Image.open(requests.get(UpperCamelCase__					,	stream=UpperCamelCase__    ).raw    )
       return im


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

       # Load 🤗 model
       A__		   =		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
       A__		   =		MobileNetVaImageProcessor(
           crop_size={'''width''': config.image_size, '''height''': config.image_size}					,	size={'''shortest_edge''': config.image_size + 32}					,	)
       A__		   =		image_processor(images=prepare_img()					,	return_tensors='''pt'''    )
       A__		   =		model(**UpperCamelCase__    )
       A__		   =		outputs.logits

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

       if model_name == "mobilenet_v1_1.0_224":
              A__		   =		torch.tensor([-4.1739, -1.1233, 3.1205]    )
       elif model_name == "mobilenet_v1_0.75_192":
              A__		   =		torch.tensor([-3.9440, -2.3141, -0.3333]    )
       else:
              A__		   =		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...'''    )
              A__		   =		'''google/''' + model_name
              image_processor.push_to_hub(UpperCamelCase__    )
              model.push_to_hub(UpperCamelCase__    )


if __name__ == "__main__":
       a__: int							   =						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.'
       )

       a__: Union[str, Any]							   =						parser.parse_args()
       convert_movilevit_checkpoint(
           args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )












import math


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->list:
       A__		   =		[True] * n
       A__		   =		False
       A__		   =		False
       A__		   =		True

       for i in range(3					,	int(n**0.5 + 1    )					,	2    ):
              A__		   =		i * 2
              while index < n:
                     A__		   =		False
                     A__		   =		index + i

       A__		   =		[2]

       for i in range(3					,	UpperCamelCase__					,	2    ):
              if is_prime[i]:
                     primes.append(UpperCamelCase__    )

       return primes


def       UpperCamelCase__(			UpperCamelCase__     :			int = 99_99_66_66_33_33    )->int:
       A__		   =		math.floor(math.sqrt(UpperCamelCase__    )    ) + 1_00
       A__		   =		prime_sieve(UpperCamelCase__    )

       A__		   =		0
       A__		   =		0
       A__		   =		primes[prime_index]

       while (last_prime**2) <= limit:
              A__		   =		primes[prime_index + 1]

              A__		   =		last_prime**2
              A__		   =		next_prime**2

              # Get numbers divisible by lps(current)
              A__		   =		lower_bound + last_prime
              while upper_bound > current <= limit:
                     matches_sum += current
                     current += last_prime

              # Reset the upper_bound
              while (upper_bound - next_prime) > limit:
                     upper_bound -= next_prime

              # Add the numbers divisible by ups(current)
              A__		   =		upper_bound - next_prime
              while current > lower_bound:
                     matches_sum += current
                     current -= next_prime

              # Remove the numbers divisible by both ups and lps
              A__		   =		0
              while upper_bound > current <= limit:
                     if current <= lower_bound:
                            # Increment the current number
                            current += last_prime * next_prime
                            continue

                     if current > limit:
                            break

                     # Remove twice since it was added by both ups and lps
                     matches_sum -= current * 2

                     # Increment the current number
                     current += last_prime * next_prime

              # Setup for next pair
              A__		   =		next_prime
              prime_index += 1

       return matches_sum


if __name__ == "__main__":
       print(solution())





