




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     : str      =logging.get_logger(__name__)

def       lowerCAmelCase_      (	lowerCamelCase:							List[str]    )       ->      Optional[int]:


      '''simple docstring'''




      lowerCamelCase__   	=					MobileNetVaConfig(layer_norm_eps=0.001    )

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

      lowerCamelCase__   	=					re.match(r'^.*mobilenet_v2_([^_]*)_([^_]*)$'  ,					_UpperCAmelCase    )
      if matches:
            lowerCamelCase__   	=					float(matches[1]    )
            lowerCamelCase__   	=					int(matches[2]    )

      if model_name.startswith('deeplabv3_'    ):
            lowerCamelCase__   	=					8
            lowerCamelCase__   	=					21
            lowerCamelCase__   	=					"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__   	=					10_01
            lowerCamelCase__   	=					"imagenet-1k-id2label.json"

      lowerCamelCase__   	=					"huggingface/label-files"
      lowerCamelCase__   	=					json.load(open(hf_hub_download(_UpperCAmelCase  ,					_UpperCAmelCase  ,					repo_type='dataset'    )  ,					'r'    )    )

      if config.num_labels == 10_01:
            lowerCamelCase__   	=					{int(_UpperCAmelCase    ) + 1: v for k, v in idalabel.items()}
            lowerCamelCase__   	=					"background"
      else:
            lowerCamelCase__   	=					{int(_UpperCAmelCase    ): v for k, v in idalabel.items()}

      lowerCamelCase__   	=					idalabel
      lowerCamelCase__   	=					{v: k for k, v in idalabel.items()}

      return config

def       lowerCAmelCase_      (	)       ->      str:


      '''simple docstring'''




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

@torch.no_grad()
def       lowerCAmelCase_      (	lowerCamelCase:							Union[str, Any]  ,					lowerCamelCase:							Tuple  ,					lowerCamelCase:							Optional[int]  ,					lowerCamelCase:							int=False    )       ->      List[Any]:


      '''simple docstring'''




      lowerCamelCase__   	=					get_mobilenet_va_config(_UpperCAmelCase    )

      # Load 🤗 model
      if model_name.startswith('deeplabv3_'    ):
            lowerCamelCase__   	=					MobileNetVaForSemanticSegmentation(_UpperCAmelCase    ).eval()
      else:
            lowerCamelCase__   	=					MobileNetVaForImageClassification(_UpperCAmelCase    ).eval()

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

      # Check outputs on an image, prepared by MobileNetV2ImageProcessor
      lowerCamelCase__   	=					MobileNetVaImageProcessor(
          crop_size={'width': config.image_size, 'height': config.image_size}  ,					size={'shortest_edge': config.image_size + 32}  ,					)
      lowerCamelCase__   	=					image_processor(images=prepare_img()  ,					return_tensors='pt'    )
      lowerCamelCase__   	=					model(**_UpperCAmelCase    )
      lowerCamelCase__   	=					outputs.logits

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

            if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                  lowerCamelCase__   	=					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]  ,					_UpperCAmelCase  ,					atol=1e-4    )
      else:
            assert logits.shape == (1, 10_01)

            if model_name == "mobilenet_v2_1.4_224":
                  lowerCamelCase__   	=					torch.tensor([0.0181, -1.0015, 0.4688]    )
            elif model_name == "mobilenet_v2_1.0_224":
                  lowerCamelCase__   	=					torch.tensor([0.2445, -1.1993, 0.1905]    )
            elif model_name == "mobilenet_v2_0.75_160":
                  lowerCamelCase__   	=					torch.tensor([0.2482, 0.4136, 0.6669]    )
            elif model_name == "mobilenet_v2_0.35_96":
                  lowerCamelCase__   	=					torch.tensor([0.1451, -0.4624, 0.7192]    )
            else:
                  lowerCamelCase__   	=					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...'    )
            lowerCamelCase__   	=					"google/" + model_name
            image_processor.push_to_hub(_UpperCAmelCase    )
            model.push_to_hub(_UpperCAmelCase    )


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






'''simple docstring'''

from __future__ import annotations

lowerCAmelCase__      :   Tuple      	=			{1: 1}


def 						__UpperCamelCase						(			_UpperCAmelCase  ):
		if n in COLLATZ_SEQUENCE_LENGTHS:
				return COLLATZ_SEQUENCE_LENGTHS[n]
		__UpperCAmelCase	:							int     =     n // 2 if n % 2 == 0 else 3 * n + 1
		__UpperCAmelCase	:							str     =     collatz_sequence_length(_UpperCAmelCase  ) + 1
		__UpperCAmelCase	:							Union[str, Any]     =     sequence_length
		return sequence_length


def 						__UpperCamelCase						(			_UpperCAmelCase = 1000000  ):
		__UpperCAmelCase	:							Union[str, Any]     =     max((collatz_sequence_length(_UpperCAmelCase  ), i) for i in range(1,       _UpperCAmelCase  )  )
		return result[1]


if __name__ == "__main__":
			print(solution(int(input().strip())))




