





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()
SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						MobileNetVaConfig(layer_norm_eps=0.001     )

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

      SCREAMING_SNAKE_CASE		=						re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$"""			,						_SCREAMING_SNAKE_CASE     )
      if matches:
            SCREAMING_SNAKE_CASE		=						float(matches[1]     )
            SCREAMING_SNAKE_CASE		=						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		=						10_01
      SCREAMING_SNAKE_CASE		=						"""imagenet-1k-id2label.json"""
      SCREAMING_SNAKE_CASE		=						"""huggingface/label-files"""
      SCREAMING_SNAKE_CASE		=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						repo_type="""dataset"""     )			,						"""r"""     )     )
      SCREAMING_SNAKE_CASE		=						{int(_SCREAMING_SNAKE_CASE     ) + 1: v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE		=						"""background"""
      SCREAMING_SNAKE_CASE		=						idalabel
      SCREAMING_SNAKE_CASE		=						{v: k for k, v in idalabel.items()}

      return config



def        __lowercase       (				)			->       Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE		=						Image.open(requests.get(_SCREAMING_SNAKE_CASE			,						stream=_SCREAMING_SNAKE_CASE     ).raw     )
      return im



@torch.no_grad()
def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=False     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						get_mobilenet_va_config(_SCREAMING_SNAKE_CASE     )

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

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

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


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

      SCREAMING_SNAKE_CASE_						       =	parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )









from __future__ import annotations

import math



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       bool:


      '''simple docstring'''


      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5			,						int(math.sqrt(_SCREAMING_SNAKE_CASE     ) + 1     )			,						6     ):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True



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


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						str(_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						[n]
      for i in range(1			,						len(_SCREAMING_SNAKE_CASE     )     ):
            list_nums.append(int(str_num[i:]     )     )
            list_nums.append(int(str_num[:-i]     )     )
      return list_nums



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       bool:


      '''simple docstring'''


      if len(str(_SCREAMING_SNAKE_CASE     )     ) > 3:
            if not is_prime(int(str(_SCREAMING_SNAKE_CASE     )[-3:]     )     ) or not is_prime(int(str(_SCREAMING_SNAKE_CASE     )[:3]     )     ):
                  return False
      return True



def        __lowercase       (				_SCREAMING_SNAKE_CASE = 11     )			->       list[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[]
      SCREAMING_SNAKE_CASE		=						13
      while len(_SCREAMING_SNAKE_CASE     ) != count:
            if validate(_SCREAMING_SNAKE_CASE     ):
                  SCREAMING_SNAKE_CASE		=						list_truncated_nums(_SCREAMING_SNAKE_CASE     )
                  if all(is_prime(_SCREAMING_SNAKE_CASE     ) for i in list_nums     ):
                        list_truncated_primes.append(_SCREAMING_SNAKE_CASE     )
            num += 2
      return list_truncated_primes



def        __lowercase       (				)			->       int:


      '''simple docstring'''


      return sum(compute_truncated_primes(11     )     )


if __name__ == "__main__":
      print(F'''{sum(compute_truncated_primes(1_1)) = }''')


