



'''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,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase										=     logging.get_logger(__name__)
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  int											= MobileNetVaConfig(layer_norm_eps=0.001      )

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

    lowerCAmelCase__			:  List[str]											= re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""							,   UpperCamelCase      )
    if matches:
        lowerCAmelCase__			:  str											= float(matches[1]      )
        lowerCAmelCase__			:  Union[str, Any]											= int(matches[2]      )

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

    return config
def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= """http://images.cocodataset.org/val2017/000000039769.jpg"""
    lowerCAmelCase__			:  Dict											= Image.open(requests.get(UpperCamelCase							,   stream=UpperCamelCase      ).raw      )
    return im
@torch.no_grad()
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase=False      ):



    """simple docstring"""





    lowerCAmelCase__			:  Any											= get_mobilenet_va_config(UpperCamelCase      )

    # Load 🤗 model
    lowerCAmelCase__			:  List[Any]											= MobileNetVaForImageClassification(UpperCamelCase      ).eval()

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

    # Check outputs on an image, prepared by MobileNetV1ImageProcessor
    lowerCAmelCase__			:  str											= MobileNetVaImageProcessor(
        crop_size={"""width""": config.image_size, """height""": config.image_size}							,   size={"""shortest_edge""": config.image_size + 32}							,   )
    lowerCAmelCase__			:  Optional[int]											= image_processor(images=prepare_img()							,   return_tensors="""pt"""      )
    lowerCAmelCase__			:  Optional[Any]											= model(**UpperCamelCase      )
    lowerCAmelCase__			:  str											= outputs.logits

    assert logits.shape == (1, 1001)

    if model_name == "mobilenet_v1_1.0_224":
        lowerCAmelCase__			:  Optional[Any]											= torch.tensor([-4.1739, -1.1233, 3.1205]      )
    elif model_name == "mobilenet_v1_0.75_192":
        lowerCAmelCase__			:  str											= torch.tensor([-3.9440, -2.3141, -0.3333]      )
    else:
        lowerCAmelCase__			:  List[str]											= None

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

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

    if push_to_hub:
        print("""Pushing to the hub..."""      )
        lowerCAmelCase__			:  Tuple											= """google/""" + model_name
        image_processor.push_to_hub(UpperCamelCase      )
        model.push_to_hub(UpperCamelCase      )


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

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







'''simple docstring'''





from PIL import Image
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= image.size
    lowerCAmelCase__			:  int											= 0
    lowerCAmelCase__			:  int											= image.load()
    for i in range(UpperCamelCase      ):
        for j in range(UpperCamelCase      ):
            lowerCAmelCase__			:  int											= pixels[j, i]
            mean += pixel
    mean //= width * height

    for j in range(UpperCamelCase      ):
        for i in range(UpperCamelCase      ):
            lowerCAmelCase__			:  Dict											= 255 if pixels[i, j] > mean else 0
    return image


if __name__ == "__main__":
 _lowerCAmelCase										=     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
 image.save('''output_image_path''')


