


'''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      :     Union[str, Any]										= logging.get_logger(__name__)






def       lowerCAmelCase__       (				lowerCamelCase     :			Optional[int]   ):
       _A     :	Any      =					MobileNetVaConfig(layer_norm_eps=0.001   )

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

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

       if model_name.startswith('deeplabv3_'   ):
              _A     :	Optional[Any]      =					8
              _A     :	List[str]      =					21
              _A     :	Optional[Any]      =					'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".
              _A     :	List[Any]      =					1001
              _A     :	Optional[Any]      =					'imagenet-1k-id2label.json'

       _A     :	Any      =					'huggingface/label-files'
       _A     :	Optional[Any]      =					json.load(open(hf_hub_download(lowerCamelCase  ,lowerCamelCase  ,repo_type='dataset'   )  ,'r'   )   )

       if config.num_labels == 1001:
              _A     :	Tuple      =					{int(lowerCamelCase   ) + 1: v for k, v in idalabel.items()}
              _A     :	Union[str, Any]      =					'background'
       else:
              _A     :	Dict      =					{int(lowerCamelCase   ): v for k, v in idalabel.items()}

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

       return config






def       lowerCAmelCase__       (				):
       _A     :	Any      =					'http://images.cocodataset.org/val2017/000000039769.jpg'
       _A     :	int      =					Image.open(requests.get(lowerCamelCase  ,stream=lowerCamelCase   ).raw   )
       return im






@torch.no_grad()
def       lowerCAmelCase__       (				lowerCamelCase     :			Dict  ,lowerCamelCase     :			Tuple  ,lowerCamelCase     :			Dict  ,lowerCamelCase     :			int=False   ):
       _A     :	List[str]      =					get_mobilenet_va_config(lowerCamelCase   )

       # Load 🤗 model
       if model_name.startswith('deeplabv3_'   ):
              _A     :	Optional[Any]      =					MobileNetVaForSemanticSegmentation(lowerCamelCase   ).eval()
       else:
              _A     :	Dict      =					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
       _A     :	int      =					MobileNetVaImageProcessor(
           crop_size={'width': config.image_size, 'height': config.image_size}  ,size={'shortest_edge': config.image_size + 32}  ,)
       _A     :	Any      =					image_processor(images=prepare_img()  ,return_tensors='pt'   )
       _A     :	Optional[Any]      =					model(**lowerCamelCase   )
       _A     :	Any      =					outputs.logits

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

              if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                     _A     :	Union[str, Any]      =					torch.tensor(
                         [
                             [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
                             [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
                             [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
                         ]   )

              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":
                     _A     :	Dict      =					torch.tensor([0.0_181, -1.0_015, 0.4_688]   )
              elif model_name == "mobilenet_v2_1.0_224":
                     _A     :	Optional[int]      =					torch.tensor([0.2_445, -1.1_993, 0.1_905]   )
              elif model_name == "mobilenet_v2_0.75_160":
                     _A     :	Dict      =					torch.tensor([0.2_482, 0.4_136, 0.6_669]   )
              elif model_name == "mobilenet_v2_0.35_96":
                     _A     :	Tuple      =					torch.tensor([0.1_451, -0.4_624, 0.7_192]   )
              else:
                     _A     :	Any      =					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...'   )
              _A     :	Optional[Any]      =					'google/' + model_name
              image_processor.push_to_hub(lowerCamelCase   )
              model.push_to_hub(lowerCamelCase   )


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

   A      :     Union[str, Any]										= 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






def       lowerCAmelCase__       (				lowerCamelCase     :			list   ):
       _A     :	Union[str, Any]      =					sorted(lowerCamelCase   )
       _A     :	Tuple      =					len(lowerCamelCase   )
       _A     :	int      =					length >> 1
       return (
           (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
           if length % 2 == 0
           else sorted_list[mid_index]
       )






def       lowerCAmelCase__       (				):
       import doctest

       doctest.testmod()


if __name__ == "__main__":
   main()






