



"""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()
__snake_case							:   Dict      =    logging.get_logger(__name__)


def 			_lowercase				(  __snake_case	)						->   List[Any]:
			__lowerCAmelCase      :  str												=       MobileNetVaConfig(layer_norm_eps=0.001	)

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

			__lowerCAmelCase      :  str												=       re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$"					,__snake_case	)
			if matches:
						__lowerCAmelCase      :  List[Any]												=       float(matches[1]	)
						__lowerCAmelCase      :  Tuple												=       int(matches[2]	)

			# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
			# the usual 1000. The first class (index 0) is "background".
			__lowerCAmelCase      :  Optional[int]												=       1_001
			__lowerCAmelCase      :  str												=       "imagenet-1k-id2label.json"
			__lowerCAmelCase      :  Optional[Any]												=       "huggingface/label-files"
			__lowerCAmelCase      :  Dict												=       json.load(open(hf_hub_download(__snake_case					,__snake_case					,repo_type="dataset"	)					,"r"	)	)
			__lowerCAmelCase      :  List[str]												=       {int(__snake_case	) + 1: v for k, v in idalabel.items()}
			__lowerCAmelCase      :  str												=       "background"
			__lowerCAmelCase      :  int												=       idalabel
			__lowerCAmelCase      :  int												=       {v: k for k, v in idalabel.items()}

			return config


def 			_lowercase				(  )						->   List[Any]:
			__lowerCAmelCase      :  int												=       "http://images.cocodataset.org/val2017/000000039769.jpg"
			__lowerCAmelCase      :  Optional[Any]												=       Image.open(requests.get(__snake_case					,stream=__snake_case	).raw	)
			return im


@torch.no_grad()
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case=False	)						->   List[str]:
			__lowerCAmelCase      :  List[Any]												=       get_mobilenet_va_config(__snake_case	)

			# Load 🤗 model
			__lowerCAmelCase      :  Tuple												=       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      :  Any												=       MobileNetVaImageProcessor(
			    crop_size={"width": config.image_size, "height": config.image_size}					,size={"shortest_edge": config.image_size + 32}					,)
			__lowerCAmelCase      :  Tuple												=       image_processor(images=prepare_img()					,return_tensors="pt"	)
			__lowerCAmelCase      :  Any												=       model(**__snake_case	)
			__lowerCAmelCase      :  List[Any]												=       outputs.logits

			assert logits.shape == (1, 1_001)

			if model_name == "mobilenet_v1_1.0_224":
						__lowerCAmelCase      :  Tuple												=       torch.tensor([-4.1739, -1.1233, 3.1205]	)
			elif model_name == "mobilenet_v1_0.75_192":
						__lowerCAmelCase      :  Any												=       torch.tensor([-3.9440, -2.3141, -0.3333]	)
			else:
						__lowerCAmelCase      :  Union[str, Any]												=       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      :  str												=       "google/" + model_name
						image_processor.push_to_hub(__snake_case	)
						model.push_to_hub(__snake_case	)


if __name__ == "__main__":
	__snake_case							:   List[Any]      =    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.'
	)

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




"""simple docstring"""






import os
from math import logaa


def 			_lowercase				(  __snake_case = "base_exp.txt"	)						->   int:
			__lowerCAmelCase      :  float												=       0
			__lowerCAmelCase      :  Any												=       0
			for i, line in enumerate(open(os.path.join(os.path.dirname(__snake_case	)					,__snake_case	)	)	):
						__lowerCAmelCase						,	__lowerCAmelCase      :  List[str]												=       list(map(__snake_case					,line.split(","	)	)	)
						if x * logaa(__snake_case	) > largest:
									__lowerCAmelCase      :  Tuple												=       x * logaa(__snake_case	)
									__lowerCAmelCase      :  Optional[Any]												=       i + 1
			return result


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