"""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()
_a  :  List[Any]=				logging.get_logger(__name__)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Tuple						)							->    Optional[int]:

	'''simple docstring'''





	__snake_case    :							Tuple   	=				MobileNetVaConfig(layer_norm_eps=0.001						)

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

	__snake_case    :							Any   	=				re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'    ,							a_						)
	if matches:
		__snake_case    :							List[Any]   	=				float(matches[1]						)
		__snake_case    :							Tuple   	=				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    :							List[str]   	=				10_01
	__snake_case    :							Union[str, Any]   	=				'''imagenet-1k-id2label.json'''
	__snake_case    :							Optional[int]   	=				'''huggingface/label-files'''
	__snake_case    :							Tuple   	=				json.load(open(hf_hub_download(a_    ,							a_    ,							repo_type='dataset'						)    ,							'r'						)						)
	__snake_case    :							Any   	=				{int(a_						) + 1: v for k, v in idalabel.items()}
	__snake_case    :							int   	=				'''background'''
	__snake_case    :							str   	=				idalabel
	__snake_case    :							List[Any]   	=				{v: k for k, v in idalabel.items()}

	return config
def 						__UpperCAmelCase       (					)							->    List[str]:

	'''simple docstring'''





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

	'''simple docstring'''





	__snake_case    :							int   	=				get_mobilenet_va_config(a_						)

	# Load 🤗 model
	__snake_case    :							Any   	=				MobileNetVaForImageClassification(a_						).eval()

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

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

	assert logits.shape == (1, 10_01)

	if model_name == "mobilenet_v1_1.0_224":
		__snake_case    :							Optional[int]   	=				torch.tensor([-4.1_739, -1.1_233, 3.1_205]						)
	elif model_name == "mobilenet_v1_0.75_192":
		__snake_case    :							str   	=				torch.tensor([-3.9_440, -2.3_141, -0.3_333]						)
	else:
		__snake_case    :							Optional[Any]   	=				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...'						)
		__snake_case    :							Union[str, Any]   	=				'''google/''' + model_name
		image_processor.push_to_hub(a_						)
		model.push_to_hub(a_						)


if __name__ == "__main__":
	_a  :  Optional[int]=				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  :  Dict=				parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)







"""simple docstring"""







from abc import ABC, abstractmethod
from argparse import ArgumentParser

class 	UpperCamelCase    (					lowercase		):



				@staticmethod
				@abstractmethod
				def       _lowercase  (_A       :   ArgumentParser)					->			Tuple:
					raise NotImplementedError()






				@abstractmethod
				def       _lowercase  (self       :   Any)					->			Optional[Any]:
					raise NotImplementedError()





