





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()
_a						=   logging.get_logger(__name__)





def lowerCAmelCase__(__snake_case							)       ->   int:







	'''simple docstring'''







	lowerCamelCase__              =	MobileNetVaConfig(layer_norm_eps=0.0_0_1							)

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

	lowerCamelCase__              =	re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$''' ,__snake_case							)
	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(__snake_case ,__snake_case ,repo_type='''dataset'''							) ,'''r'''							)							)
	lowerCamelCase__              =	{int(__snake_case							) + 1: v for k, v in idalabel.items()}
	lowerCamelCase__              =	'''background'''
	lowerCamelCase__              =	idalabel
	lowerCamelCase__              =	{v: k for k, v in idalabel.items()}

	return config





def lowerCAmelCase__()       ->   Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__              =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	lowerCamelCase__              =	Image.open(requests.get(__snake_case ,stream=__snake_case							).raw							)
	return im





@torch.no_grad()
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case=False							)       ->   List[str]:







	'''simple docstring'''







	lowerCamelCase__              =	get_mobilenet_va_config(__snake_case							)

	# Load 🤗 model
	lowerCamelCase__              =	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 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(**__snake_case							)
	lowerCamelCase__              =	outputs.logits

	assert logits.shape == (1, 1001)

	if model_name == "mobilenet_v1_1.0_224":
		lowerCamelCase__              =	torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]							)
	elif model_name == "mobilenet_v1_0.75_192":
		lowerCamelCase__              =	torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]							)
	else:
		lowerCamelCase__              =	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...'''							)
		lowerCamelCase__              =	'''google/''' + model_name
		image_processor.push_to_hub(__snake_case							)
		model.push_to_hub(__snake_case							)


if __name__ == "__main__":
			_a						=   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."
			)

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










from __future__ import annotations





def lowerCAmelCase__(__snake_case ,__snake_case = None ,__snake_case = None							)       ->   None:







	'''simple docstring'''







	if start is None:
		lowerCamelCase__              =	0

	if end is None:
		lowerCamelCase__              =	len(__snake_case							) - 1

	if start >= end:
		return

	lowerCamelCase__              =	(start + end) // 2

	slowsort(__snake_case ,__snake_case ,__snake_case							)
	slowsort(__snake_case ,mid + 1 ,__snake_case							)

	if sequence[end] < sequence[mid]:
		lowerCamelCase__       ,			lowerCamelCase__              =	sequence[mid], sequence[end]

	slowsort(__snake_case ,__snake_case ,end - 1							)


if __name__ == "__main__":
			from doctest import testmod

			testmod()



