






'''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()
UpperCamelCase     :   str	      =		logging.get_logger(__name__)
def     A__      (					__lowerCAmelCase		:      Tuple    ):
						lowerCamelCase__								=	MobileNetVaConfig(layer_norm_eps=0.001    )

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

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

						return config
def     A__      (					):
						lowerCamelCase__								=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						lowerCamelCase__								=	Image.open(requests.get(__lowerCAmelCase ,     stream=__lowerCAmelCase    ).raw    )
						return im
@torch.no_grad()
def     A__      (					__lowerCAmelCase		:      Any ,     __lowerCAmelCase		:      Union[str, Any] ,     __lowerCAmelCase		:      Tuple ,     __lowerCAmelCase		:      Any=False    ):
						lowerCamelCase__								=	get_mobilenet_va_config(__lowerCAmelCase    )

						# Load 🤗 model
						lowerCamelCase__								=	MobileNetVaForImageClassification(__lowerCAmelCase    ).eval()

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

						# 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(**__lowerCAmelCase    )
						lowerCamelCase__								=	outputs.logits

						assert logits.shape == (1, 1001)

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

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

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

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


if __name__ == "__main__":
							UpperCamelCase     :   Tuple	      =		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.'
							)

							UpperCamelCase     :   Tuple	      =		parser.parse_args()
							convert_movilevit_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)






"""simple docstring"""







def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						return int((input_a, input_a).count(0 ) != 0 )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						assert nand_gate(0     ,		0 ) == 1
						assert nand_gate(0     ,		1 ) == 1
						assert nand_gate(1     ,		0 ) == 1
						assert nand_gate(1     ,		1 ) == 0


if __name__ == "__main__":
			print(nand_gate(0, 0))
			print(nand_gate(0, 1))
			print(nand_gate(1, 0))
			print(nand_gate(1, 1))



