

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					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	MobileNetVaConfig(layer_norm_eps=0.001       )

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

						snake_case_   =	re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''  ,		SCREAMING_SNAKE_CASE__       )
						if matches:
												snake_case_   =	float(matches[1]       )
												snake_case_   =	int(matches[2]       )

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

						return config


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						snake_case_   =	Image.open(requests.get(SCREAMING_SNAKE_CASE__  ,		stream=SCREAMING_SNAKE_CASE__       ).raw       )
						return im


@torch.no_grad()
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=False       ):
						snake_case_   =	get_mobilenet_va_config(SCREAMING_SNAKE_CASE__       )

						# Load 🤗 model
						snake_case_   =	MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE__       ).eval()

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

						# Check outputs on an image, prepared by MobileNetV1ImageProcessor
						snake_case_   =	MobileNetVaImageProcessor(
						    crop_size={'''width''': config.image_size, '''height''': config.image_size}  ,		size={'''shortest_edge''': config.image_size + 32}  ,		)
						snake_case_   =	image_processor(images=prepare_img()  ,		return_tensors='''pt'''       )
						snake_case_   =	model(**SCREAMING_SNAKE_CASE__       )
						snake_case_   =	outputs.logits

						assert logits.shape == (1, 1001)

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

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

						Path(SCREAMING_SNAKE_CASE__       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
						print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''       )
						model.save_pretrained(SCREAMING_SNAKE_CASE__       )
						print(F'''Saving image processor to {pytorch_dump_folder_path}'''       )
						image_processor.save_pretrained(SCREAMING_SNAKE_CASE__       )

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


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
					)







def      lowercase__      (     __snake_case    :							int     ,       __snake_case    :							int  ):


					'''simple docstring'''


					if a < 0 or b < 0:
										raise ValueError('the value of both inputs must be positive'  )

					UpperCAmelCase_    :      Tuple            =						str(bin(__snake_case  )  )[2:]  # remove the leading "0b"
					UpperCAmelCase_    :      Union[str, Any]            =						str(bin(__snake_case  )  )[2:]  # remove the leading "0b"

					UpperCAmelCase_    :      List[Any]            =						max(len(__snake_case  )     ,       len(__snake_case  )  )

					return "0b" + "".join(
					    str(int(char_a == '1' and char_b == '1'  )  )
					    for char_a, char_b in zip(a_binary.zfill(__snake_case  )     ,       b_binary.zfill(__snake_case  )  )  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






