




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()
UpperCamelCase								=					logging.get_logger(__name__)



def 							_A       (						lowerCAmelCase_					: Optional[Any]      ):
     """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_v1_([^_]*)_([^_]*)$"       ,    lowerCAmelCase_      )
     if matches:
          lowerCAmelCase__          =			float(matches[1]      )
          lowerCAmelCase__          =			int(matches[2]      )

     # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
     # the usual 1000. The first class (index 0) is "background".
     lowerCAmelCase__          =			1001
     lowerCAmelCase__          =			"imagenet-1k-id2label.json"
     lowerCAmelCase__          =			"huggingface/label-files"
     lowerCAmelCase__          =			json.load(open(hf_hub_download(lowerCAmelCase_       ,    lowerCAmelCase_       ,    repo_type="dataset"      )       ,    "r"      )      )
     lowerCAmelCase__          =			{int(lowerCAmelCase_      ) + 1: v for k, v in idalabel.items()}
     lowerCAmelCase__          =			"background"
     lowerCAmelCase__          =			idalabel
     lowerCAmelCase__          =			{v: k for k, v in idalabel.items()}

     return config



def 							_A       (						):
     """simple docstring"""

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



@torch.no_grad()
def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Optional[Any]=False      ):
     """simple docstring"""

     lowerCAmelCase__          =			get_mobilenet_va_config(lowerCAmelCase_      )

     # Load 🤗 model
     lowerCAmelCase__          =			MobileNetVaForImageClassification(lowerCAmelCase_      ).eval()

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

     # Check outputs on an image, prepared by MobileNetV1ImageProcessor
     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(**lowerCAmelCase_      )
     lowerCAmelCase__          =			outputs.logits

     assert logits.shape == (1, 1001)

     if model_name == "mobilenet_v1_1.0_224":
          lowerCAmelCase__          =			torch.tensor([-4.1739, -1.1233, 3.1205]      )
     elif model_name == "mobilenet_v1_0.75_192":
          lowerCAmelCase__          =			torch.tensor([-3.9440, -2.3141, -0.3333]      )
     else:
          lowerCAmelCase__          =			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..."      )
          lowerCAmelCase__          =			"google/" + model_name
          image_processor.push_to_hub(lowerCAmelCase_      )
          model.push_to_hub(lowerCAmelCase_      )


if __name__ == "__main__":
     UpperCamelCase								=					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.'
     )

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






import random



def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			[], [], []
     for element in data:
          if element < pivot:
               less.append(lowerCAmelCase_      )
          elif element > pivot:
               greater.append(lowerCAmelCase_      )
          else:
               equal.append(lowerCAmelCase_      )
     return less, equal, greater






def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     if index >= len(lowerCAmelCase_      ) or index < 0:
          return None

     lowerCAmelCase__          =			items[random.randint(0       ,    len(lowerCAmelCase_      ) - 1      )]
     lowerCAmelCase__          =			0
     lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			_partition(lowerCAmelCase_       ,    lowerCAmelCase_      )
     lowerCAmelCase__          =			len(lowerCAmelCase_      )
     lowerCAmelCase__          =			len(lowerCAmelCase_      )

     # index is the pivot
     if m <= index < m + count:
          return pivot
     # must be in smaller
     elif m > index:
          return quick_select(lowerCAmelCase_       ,    lowerCAmelCase_      )
     # must be in larger
     else:
          return quick_select(lowerCAmelCase_       ,    index - (m + count)      )
