

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()
UpperCAmelCase__			:  Dict					     =     logging.get_logger(__name__)






def        __lowercase   (  _A      )  ->    List[Any]:
      SCREAMING_SNAKE_CASE			:   List[str]	  = MobileNetVaConfig(layer_norm_eps=0.001      )

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

      SCREAMING_SNAKE_CASE			:   List[str]	  = re.match(R"""^.*mobilenet_v2_([^_]*)_([^_]*)$"""    ,     _A      )
      if matches:
            SCREAMING_SNAKE_CASE			:   Any	  = float(matches[1]      )
            SCREAMING_SNAKE_CASE			:   List[str]	  = int(matches[2]      )

      if model_name.startswith("""deeplabv3_"""      ):
            SCREAMING_SNAKE_CASE			:   Dict	  = 8
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = 21
            SCREAMING_SNAKE_CASE			:   Dict	  = """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".
            SCREAMING_SNAKE_CASE			:   int	  = 1001
            SCREAMING_SNAKE_CASE			:   int	  = """imagenet-1k-id2label.json"""

      SCREAMING_SNAKE_CASE			:   str	  = """huggingface/label-files"""
      SCREAMING_SNAKE_CASE			:   List[str]	  = json.load(open(hf_hub_download(_A    ,     _A    ,     repo_type="""dataset"""      )    ,     """r"""      )      )

      if config.num_labels == 1001:
            SCREAMING_SNAKE_CASE			:   Tuple	  = {int(_A      ) + 1: v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = """background"""
      else:
            SCREAMING_SNAKE_CASE			:   Dict	  = {int(_A      ): v for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE			:   Dict	  = idalabel
      SCREAMING_SNAKE_CASE			:   Tuple	  = {v: k for k, v in idalabel.items()}

      return config






def        __lowercase   (  )  ->    List[Any]:
      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = """http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE			:   str	  = Image.open(requests.get(_A    ,     stream=_A      ).raw      )
      return im






@torch.no_grad()
def        __lowercase   (  _A    ,     _A    ,     _A    ,     _A=False      )  ->    Optional[Any]:
      SCREAMING_SNAKE_CASE			:   int	  = get_mobilenet_va_config(_A      )

      # Load 🤗 model
      if model_name.startswith("""deeplabv3_"""      ):
            SCREAMING_SNAKE_CASE			:   Any	  = MobileNetVaForSemanticSegmentation(_A      ).eval()
      else:
            SCREAMING_SNAKE_CASE			:   Tuple	  = MobileNetVaForImageClassification(_A      ).eval()

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

      # Check outputs on an image, prepared by MobileNetV2ImageProcessor
      SCREAMING_SNAKE_CASE			:   Dict	  = MobileNetVaImageProcessor(
          crop_size={"""width""": config.image_size, """height""": config.image_size}    ,     size={"""shortest_edge""": config.image_size + 32}    ,     )
      SCREAMING_SNAKE_CASE			:   List[str]	  = image_processor(images=prepare_img()    ,     return_tensors="""pt"""      )
      SCREAMING_SNAKE_CASE			:   str	  = model(**_A      )
      SCREAMING_SNAKE_CASE			:   List[str]	  = outputs.logits

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

            if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                  SCREAMING_SNAKE_CASE			:   List[Any]	  = 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]    ,     _A    ,     atol=1e-4      )
      else:
            assert logits.shape == (1, 1001)

            if model_name == "mobilenet_v2_1.4_224":
                  SCREAMING_SNAKE_CASE			:   str	  = torch.tensor([0.0181, -1.0015, 0.4688]      )
            elif model_name == "mobilenet_v2_1.0_224":
                  SCREAMING_SNAKE_CASE			:   Tuple	  = torch.tensor([0.2445, -1.1993, 0.1905]      )
            elif model_name == "mobilenet_v2_0.75_160":
                  SCREAMING_SNAKE_CASE			:   Optional[int]	  = torch.tensor([0.2482, 0.4136, 0.6669]      )
            elif model_name == "mobilenet_v2_0.35_96":
                  SCREAMING_SNAKE_CASE			:   Optional[Any]	  = torch.tensor([0.1451, -0.4624, 0.7192]      )
            else:
                  SCREAMING_SNAKE_CASE			:   List[str]	  = None

            if expected_logits is not None:
                  assert torch.allclose(logits[0, :3]    ,     _A    ,     atol=1e-4      )

      Path(_A      ).mkdir(exist_ok=_A      )
      print(F"Saving model {model_name} to {pytorch_dump_folder_path}"      )
      model.save_pretrained(_A      )
      print(F"Saving image processor to {pytorch_dump_folder_path}"      )
      image_processor.save_pretrained(_A      )

      if push_to_hub:
            print("""Pushing to the hub..."""      )
            SCREAMING_SNAKE_CASE			:   int	  = """google/""" + model_name
            image_processor.push_to_hub(_A      )
            model.push_to_hub(_A      )


if __name__ == "__main__":
  UpperCAmelCase__			:  Tuple					     =     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."""
  )

  UpperCAmelCase__			:  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 itertools






def        __lowercase   (  _A = 1000000      )  ->    int:
      SCREAMING_SNAKE_CASE			:   Tuple	  = [1]

      for i in itertools.count(len(_A      )      ):
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = 0
            for j in itertools.count(1      ):
                  SCREAMING_SNAKE_CASE			:   str	  = -1 if j % 2 == 0 else +1
                  SCREAMING_SNAKE_CASE			:   Any	  = (j * j * 3 - j) // 2
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number
                  index += j
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number

            if item == 0:
                  return i
            partitions.append(_A      )

      return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")




