




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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ :   Any						=     logging.get_logger(__name__)

def   A   (   _SCREAMING_SNAKE_CASE					)			->				List[str]:
       lowerCamelCase     :   str    				=      MobileNetVaConfig(layer_norm_eps=0.001					)

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

       lowerCamelCase     :   str    				=      re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$"				,_SCREAMING_SNAKE_CASE					)
       if matches:
              lowerCamelCase     :   Optional[Any]    				=      float(matches[1]					)
              lowerCamelCase     :   List[str]    				=      int(matches[2]					)

       if model_name.startswith("deeplabv3_"					):
              lowerCamelCase     :   int    				=      8
              lowerCamelCase     :   List[Any]    				=      21
              lowerCamelCase     :   Union[str, Any]    				=      "pascal-voc-id2label.json"
       else:
              # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
              # of the usual 1000. The first class (index 0) is "background".
              lowerCamelCase     :   Any    				=      1001
              lowerCamelCase     :   Dict    				=      "imagenet-1k-id2label.json"

       lowerCamelCase     :   Optional[int]    				=      "huggingface/label-files"
       lowerCamelCase     :   str    				=      json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,repo_type="dataset"					)				,"r"					)					)

       if config.num_labels == 1001:
              lowerCamelCase     :   Any    				=      {int(_SCREAMING_SNAKE_CASE					) + 1: v for k, v in idalabel.items()}
              lowerCamelCase     :   Union[str, Any]    				=      "background"
       else:
              lowerCamelCase     :   List[Any]    				=      {int(_SCREAMING_SNAKE_CASE					): v for k, v in idalabel.items()}

       lowerCamelCase     :   Union[str, Any]    				=      idalabel
       lowerCamelCase     :   str    				=      {v: k for k, v in idalabel.items()}

       return config

def   A   (   )			->				str:
       lowerCamelCase     :   Any    				=      "http://images.cocodataset.org/val2017/000000039769.jpg"
       lowerCamelCase     :   Tuple    				=      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:
       lowerCamelCase     :   List[str]    				=      get_mobilenet_va_config(_SCREAMING_SNAKE_CASE					)

       # Load 🤗 model
       if model_name.startswith("deeplabv3_"					):
              lowerCamelCase     :   int    				=      MobileNetVaForSemanticSegmentation(_SCREAMING_SNAKE_CASE					).eval()
       else:
              lowerCamelCase     :   List[Any]    				=      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 MobileNetV2ImageProcessor
       lowerCamelCase     :   Optional[Any]    				=      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     :   Tuple    				=      model(**_SCREAMING_SNAKE_CASE					)
       lowerCamelCase     :   Optional[int]    				=      outputs.logits

       if model_name.startswith("deeplabv3_"					):
              assert logits.shape == (1, 21, 65, 65)

              if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                     lowerCamelCase     :   int    				=      torch.tensor(
                         [
                             [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
                             [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
                             [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
                         ]					)

              else:
                     raise ValueError(f'''Unknown model name: {model_name}'''					)

              assert torch.allclose(logits[0, :3, :3, :3]				,_SCREAMING_SNAKE_CASE				,atol=1e-4					)
       else:
              assert logits.shape == (1, 1001)

              if model_name == "mobilenet_v2_1.4_224":
                     lowerCamelCase     :   Tuple    				=      torch.tensor([0.0181, -1.0015, 0.4688]					)
              elif model_name == "mobilenet_v2_1.0_224":
                     lowerCamelCase     :   List[str]    				=      torch.tensor([0.2445, -1.1993, 0.1905]					)
              elif model_name == "mobilenet_v2_0.75_160":
                     lowerCamelCase     :   Dict    				=      torch.tensor([0.2482, 0.4136, 0.6669]					)
              elif model_name == "mobilenet_v2_0.35_96":
                     lowerCamelCase     :   str    				=      torch.tensor([0.1451, -0.4624, 0.7192]					)
              else:
                     lowerCamelCase     :   Union[str, Any]    				=      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..."					)
              lowerCamelCase     :   Any    				=      "google/" + model_name
              image_processor.push_to_hub(_SCREAMING_SNAKE_CASE					)
              model.push_to_hub(_SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__ :   int						=     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model_name',
           default='mobilenet_v2_1.0_224',
           type=str,
           help='Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<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.'
       )

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












from itertools import compress, repeat
from math import ceil, sqrt

def   A   (   _SCREAMING_SNAKE_CASE					)			->				list[int]:

       if num <= 2:
              return []
       if num == 3:
              return [2]

       # Odd sieve for numbers in range [3, num - 1]
       lowerCamelCase     :   str    				=      bytearray(B"\x01"					) * ((num >> 1) - 1)

       for i in range(3				,int(sqrt(_SCREAMING_SNAKE_CASE					)					) + 1				,2					):
              if sieve[(i >> 1) - 1]:
                     lowerCamelCase     :   Tuple    				=      i**2
                     lowerCamelCase     :   str    				=      repeat(
                         0				,ceil((num - i_squared) / (i << 1)					)					)

       return [2] + list(compress(range(3				,_SCREAMING_SNAKE_CASE				,2					)				,_SCREAMING_SNAKE_CASE					)					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






