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()
lowerCAmelCase__  	=      logging.get_logger(__name__)

def 	__lowerCamelCase							(				__a    : Optional[Any]		)      ->       Dict:
	_lowercase   =MobileNetVaConfig(layer_norm_eps=0.001		)

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

	_lowercase   =re.match(R"^.*mobilenet_v2_([^_]*)_([^_]*)$"      ,							__a		)
	if matches:
		_lowercase   =float(matches[1]		)
		_lowercase   =int(matches[2]		)

	if model_name.startswith("deeplabv3_"		):
		_lowercase   =8
		_lowercase   =21
		_lowercase   ="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".
		_lowercase   =1_001
		_lowercase   ="imagenet-1k-id2label.json"

	_lowercase   ="huggingface/label-files"
	_lowercase   =json.load(open(hf_hub_download(__a      ,							__a      ,							repo_type="dataset"		)      ,							"r"		)		)

	if config.num_labels == 1_001:
		_lowercase   ={int(__a		) + 1: v for k, v in idalabel.items()}
		_lowercase   ="background"
	else:
		_lowercase   ={int(__a		): v for k, v in idalabel.items()}

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

	return config

def 	__lowerCamelCase							(				)      ->       Dict:
	_lowercase   ="http://images.cocodataset.org/val2017/000000039769.jpg"
	_lowercase   =Image.open(requests.get(__a      ,							stream=__a		).raw		)
	return im

@torch.no_grad()
def 	__lowerCamelCase							(				__a    : Optional[Any]      ,							__a    : Optional[int]      ,							__a    : List[Any]      ,							__a    : Optional[int]=False		)      ->       Optional[Any]:
	_lowercase   =get_mobilenet_va_config(__a		)

	# Load 🤗 model
	if model_name.startswith("deeplabv3_"		):
		_lowercase   =MobileNetVaForSemanticSegmentation(__a		).eval()
	else:
		_lowercase   =MobileNetVaForImageClassification(__a		).eval()

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

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

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

		if model_name == "deeplabv3_mobilenet_v2_1.0_513":
			_lowercase   =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]      ,							__a      ,							atol=1E-4		)
	else:
		assert logits.shape == (1, 1_001)

		if model_name == "mobilenet_v2_1.4_224":
			_lowercase   =torch.tensor([0.0_181, -1.0_015, 0.4_688]		)
		elif model_name == "mobilenet_v2_1.0_224":
			_lowercase   =torch.tensor([0.2_445, -1.1_993, 0.1_905]		)
		elif model_name == "mobilenet_v2_0.75_160":
			_lowercase   =torch.tensor([0.2_482, 0.4_136, 0.6_669]		)
		elif model_name == "mobilenet_v2_0.35_96":
			_lowercase   =torch.tensor([0.1_451, -0.4_624, 0.7_192]		)
		else:
			_lowercase   =None

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

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

	if push_to_hub:
		print("Pushing to the hub..."		)
		_lowercase   ="google/" + model_name
		image_processor.push_to_hub(__a		)
		model.push_to_hub(__a		)


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

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





def 	__lowerCamelCase							(				)      ->       Dict:
	_lowercase   =1

	while True:
		if (
		    sorted(str(__a		)		)
		    == sorted(str(2 * i		)		)
		    == sorted(str(3 * i		)		)
		    == sorted(str(4 * i		)		)
		    == sorted(str(5 * i		)		)
		    == sorted(str(6 * i		)		)
		):
			return i

		i += 1


if __name__ == "__main__":
						print(solution())




