

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			       =			logging.get_logger(__name__)
def 						UpperCamelCase_(			snake_case__:  Any   )       -> List[str]:
 UpperCAmelCase__							=				MobileNetVaConfig(layer_norm_eps=0.0_0_1   )

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

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

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

 UpperCAmelCase__							=				'huggingface/label-files'
 UpperCAmelCase__							=				json.load(open(hf_hub_download(snake_case__  ,    snake_case__  ,    repo_type='dataset'   )  ,    'r'   )   )

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

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

 return config
def 						UpperCamelCase_(			)       -> int:
 UpperCAmelCase__							=				'http://images.cocodataset.org/val2017/000000039769.jpg'
 UpperCAmelCase__							=				Image.open(requests.get(snake_case__  ,    stream=snake_case__   ).raw   )
 return im
@torch.no_grad()
def 						UpperCamelCase_(			snake_case__:  Any  ,    snake_case__:  Tuple  ,    snake_case__:  Optional[int]  ,    snake_case__:  List[Any]=False   )       -> str:
 UpperCAmelCase__							=				get_mobilenet_va_config(snake_case__   )

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

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

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

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

  if model_name == "deeplabv3_mobilenet_v2_1.0_513":
   UpperCAmelCase__							=				torch.tensor(
       [
           [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
           [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
           [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
       ]   )

  else:
   raise ValueError(f"Unknown model name: {model_name}"   )

  assert torch.allclose(logits[0, :3, :3, :3]  ,    snake_case__  ,    atol=1e-4   )
 else:
  assert logits.shape == (1, 10_01)

  if model_name == "mobilenet_v2_1.4_224":
   UpperCAmelCase__							=				torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]   )
  elif model_name == "mobilenet_v2_1.0_224":
   UpperCAmelCase__							=				torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]   )
  elif model_name == "mobilenet_v2_0.75_160":
   UpperCAmelCase__							=				torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]   )
  elif model_name == "mobilenet_v2_0.35_96":
   UpperCAmelCase__							=				torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]   )
  else:
   UpperCAmelCase__							=				None

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

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

 if push_to_hub:
  print('Pushing to the hub...'   )
  UpperCAmelCase__							=				'google/' + model_name
  image_processor.push_to_hub(snake_case__   )
  model.push_to_hub(snake_case__   )


if __name__ == "__main__":
 _UpperCamelCase			       =			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			       =			parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )






def 						UpperCamelCase_(			snake_case__:  list   )       -> list:
 UpperCAmelCase__							=				len(snake_case__   )

 # Set the gap value to be decreased by a factor of 1.3
 # after each iteration
 UpperCAmelCase__							=				1.3

 # Continue sorting until the gap is 1
 while gap > 1:
  # Decrease the gap value
  UpperCAmelCase__							=				int(gap / shrink   )

  # Sort the elements using insertion sort
  for i in range(snake_case__  ,    len(snake_case__   )   ):
   UpperCAmelCase__							=				collection[i]
   UpperCAmelCase__							=				i
   while j >= gap and collection[j - gap] > temp:
    UpperCAmelCase__							=				collection[j - gap]
    j -= gap
   UpperCAmelCase__							=				temp

 return collection


if __name__ == "__main__":
 import doctest

 doctest.testmod()



