code: 
  """simple docstring"""
  
  
  
  
  
  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()
  _a	 =							logging.get_logger(__name__)
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase						):
     UpperCAmelCase_      :      Optional[int]      =					MobileNetVaConfig(layer_norm_eps=0.001						)
  
     if "quant" in model_name:
        raise ValueError("Quantized models are not supported."						)
  
     UpperCAmelCase_      :      str      =					re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$",	__lowerCamelCase						)
     if matches:
        UpperCAmelCase_      :      Optional[Any]      =					float(matches[1]						)
        UpperCAmelCase_      :      Optional[int]      =					int(matches[2]						)
  
     if model_name.startswith("deeplabv3_"						):
        UpperCAmelCase_      :      Union[str, Any]      =					8
        UpperCAmelCase_      :      Tuple      =					21
        UpperCAmelCase_      :      str      =					"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".
        UpperCAmelCase_      :      Union[str, Any]      =					1001
        UpperCAmelCase_      :      int      =					"imagenet-1k-id2label.json"
  
     UpperCAmelCase_      :      Union[str, Any]      =					"huggingface/label-files"
     UpperCAmelCase_      :      str      =					json.load(open(hf_hub_download(__lowerCamelCase,	__lowerCamelCase,	repo_type="dataset"						),	"r"						)						)
  
     if config.num_labels == 1001:
        UpperCAmelCase_      :      List[Any]      =					{int(__lowerCamelCase						) + 1: v for k, v in idalabel.items()}
        UpperCAmelCase_      :      Any      =					"background"
     else:
        UpperCAmelCase_      :      Dict      =					{int(__lowerCamelCase						): v for k, v in idalabel.items()}
  
     UpperCAmelCase_      :      Union[str, Any]      =					idalabel
     UpperCAmelCase_      :      Any      =					{v: k for k, v in idalabel.items()}
  
     return config
  
  
  
  
  
  
  
  def 			__a  (	):
     UpperCAmelCase_      :      Union[str, Any]      =					"http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase_      :      Dict      =					Image.open(requests.get(__lowerCamelCase,	stream=__lowerCamelCase						).raw						)
     return im
  
  
  
  
  
  
  
  @torch.no_grad()
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase=False						):
     UpperCAmelCase_      :      int      =					get_mobilenet_va_config(__lowerCamelCase						)
  
     # Load 🤗 model
     if model_name.startswith("deeplabv3_"						):
        UpperCAmelCase_      :      List[str]      =					MobileNetVaForSemanticSegmentation(__lowerCamelCase						).eval()
     else:
        UpperCAmelCase_      :      str      =					MobileNetVaForImageClassification(__lowerCamelCase						).eval()
  
     # Load weights from TensorFlow checkpoint
     load_tf_weights_in_mobilenet_va(__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						)
  
     # Check outputs on an image, prepared by MobileNetV2ImageProcessor
     UpperCAmelCase_      :      List[Any]      =					MobileNetVaImageProcessor(
         crop_size={"width": config.image_size, "height": config.image_size},	size={"shortest_edge": config.image_size + 32},	)
     UpperCAmelCase_      :      Tuple      =					image_processor(images=prepare_img(),	return_tensors="pt"						)
     UpperCAmelCase_      :      int      =					model(**__lowerCamelCase						)
     UpperCAmelCase_      :      Optional[Any]      =					outputs.logits
  
     if model_name.startswith("deeplabv3_"						):
        assert logits.shape == (1, 21, 65, 65)
  
        if model_name == "deeplabv3_mobilenet_v2_1.0_513":
           UpperCAmelCase_      :      Optional[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],	__lowerCamelCase,	atol=1E-4						)
     else:
        assert logits.shape == (1, 1001)
  
        if model_name == "mobilenet_v2_1.4_224":
           UpperCAmelCase_      :      Tuple      =					torch.tensor([0.0181, -1.0015, 0.4688]						)
        elif model_name == "mobilenet_v2_1.0_224":
           UpperCAmelCase_      :      Optional[int]      =					torch.tensor([0.2445, -1.1993, 0.1905]						)
        elif model_name == "mobilenet_v2_0.75_160":
           UpperCAmelCase_      :      Dict      =					torch.tensor([0.2482, 0.4136, 0.6669]						)
        elif model_name == "mobilenet_v2_0.35_96":
           UpperCAmelCase_      :      Optional[Any]      =					torch.tensor([0.1451, -0.4624, 0.7192]						)
        else:
           UpperCAmelCase_      :      Dict      =					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..."						)
        UpperCAmelCase_      :      str      =					"google/" + model_name
        image_processor.push_to_hub(__lowerCamelCase						)
        model.push_to_hub(__lowerCamelCase						)
  
  
  if __name__ == "__main__":
      _a	 =							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.'
      )
  
      _a	 =							parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  def 			__a  (	__lowerCamelCase = 1000						):
     UpperCAmelCase_      :      List[Any]      =					0
     UpperCAmelCase_      :      List[str]      =					(n - 1) // 3
     total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
     UpperCAmelCase_      :      Any      =					(n - 1) // 5
     total += ((terms) * (10 + (terms - 1) * 5)) // 2
     UpperCAmelCase_      :      Optional[int]      =					(n - 1) // 15
     total -= ((terms) * (30 + (terms - 1) * 15)) // 2
     return total
  
  
  if __name__ == "__main__":
      print(f"""{solution() = }""")
  
  
  