



def 						_lowerCAmelCase    (					__lowerCamelCase :  list  ):



 """simple docstring"""





 if len(__lowerCamelCase  ) <= 1:
  return data
 else:
  return [
      *quick_sort([e for e in data[1:] if e <= data[0]]  ),
      data[0],
      *quick_sort([e for e in data[1:] if e > data[0]]  ),
  ]


if __name__ == "__main__":
      import doctest

      doctest.testmod()











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()
_lowerCamelCase         =	logging.get_logger(__name__)






def 						_lowerCAmelCase    (					__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						MobileNetVaConfig(layer_norm_eps=0.001  )

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

 __SCREAMING_SNAKE_CASE							:     Tuple							=						re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$"		,						__lowerCamelCase  )
 if matches:
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						float(matches[1]  )
  __SCREAMING_SNAKE_CASE							:     str							=						int(matches[2]  )

 if model_name.startswith("deeplabv3_"  ):
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						8
  __SCREAMING_SNAKE_CASE							:     Dict							=						21
  __SCREAMING_SNAKE_CASE							:     Tuple							=						"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							:     Dict							=						"imagenet-1k-id2label.json"

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						"huggingface/label-files"
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						json.load(open(hf_hub_download(__lowerCamelCase		,						__lowerCamelCase		,						repo_type="dataset"  )		,						"r"  )  )

 if config.num_labels == 1001:
  __SCREAMING_SNAKE_CASE							:     int							=						{int(__lowerCamelCase  ) + 1: v for k, v in idalabel.items()}
  __SCREAMING_SNAKE_CASE							:     int							=						"background"
 else:
  __SCREAMING_SNAKE_CASE							:     Any							=						{int(__lowerCamelCase  ): v for k, v in idalabel.items()}

 __SCREAMING_SNAKE_CASE							:     str							=						idalabel
 __SCREAMING_SNAKE_CASE							:     Tuple							=						{v: k for k, v in idalabel.items()}

 return config






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						"http://images.cocodataset.org/val2017/000000039769.jpg"
 __SCREAMING_SNAKE_CASE							:     Any							=						Image.open(requests.get(__lowerCamelCase		,						stream=__lowerCamelCase  ).raw  )
 return im






@torch.no_grad()
def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Tuple=False  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Any							=						get_mobilenet_va_config(__lowerCamelCase  )

 # Load 🤗 model
 if model_name.startswith("deeplabv3_"  ):
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						MobileNetVaForSemanticSegmentation(__lowerCamelCase  ).eval()
 else:
  __SCREAMING_SNAKE_CASE							:     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
 __SCREAMING_SNAKE_CASE							:     Tuple							=						MobileNetVaImageProcessor(
     crop_size={"width": config.image_size, "height": config.image_size}		,						size={"shortest_edge": config.image_size + 32}		,						)
 __SCREAMING_SNAKE_CASE							:     Any							=						image_processor(images=prepare_img()		,						return_tensors="pt"  )
 __SCREAMING_SNAKE_CASE							:     List[str]							=						model(**__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Any							=						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]		,						__lowerCamelCase		,						atol=1E-4  )
 else:
  assert logits.shape == (1, 1001)

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


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

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






