

"""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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__A	:       Optional[int]        =						logging.get_logger(__name__)
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   MobileNetVaConfig(layer_norm_eps=0.0_01 )

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

					A     =   re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$"     ,				lowercase__ )
					if matches:
										A     =   float(matches[1] )
										A     =   int(matches[2] )

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

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

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

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

					return config
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A     =   "http://images.cocodataset.org/val2017/000000039769.jpg"
					A     =   Image.open(requests.get(lowercase__     ,				stream=lowercase__ ).raw )
					return im
@torch.no_grad()
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__=False ):
					"""simple docstring"""
					A     =   get_mobilenet_va_config(lowercase__ )

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

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

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

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

										if model_name == "deeplabv3_mobilenet_v2_1.0_513":
															A     =   torch.tensor(
															    [
															        [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
															        [[-2.15_95, -2.09_77, -2.37_41], [-2.42_26, -2.30_28, -2.68_35], [-2.78_19, -2.59_91, -2.77_06]],
															        [[4.20_58, 4.83_17, 4.76_38], [4.41_36, 5.03_61, 4.93_83], [4.50_28, 4.96_44, 4.87_34]],
															    ] )

										else:
															raise ValueError(F"""Unknown model name: {model_name}""" )

										assert torch.allclose(logits[0, :3, :3, :3]     ,				lowercase__     ,				atol=1e-4 )
					else:
										assert logits.shape == (1, 1_001)

										if model_name == "mobilenet_v2_1.4_224":
															A     =   torch.tensor([0.01_81, -1.00_15, 0.46_88] )
										elif model_name == "mobilenet_v2_1.0_224":
															A     =   torch.tensor([0.24_45, -1.19_93, 0.19_05] )
										elif model_name == "mobilenet_v2_0.75_160":
															A     =   torch.tensor([0.24_82, 0.41_36, 0.66_69] )
										elif model_name == "mobilenet_v2_0.35_96":
															A     =   torch.tensor([0.14_51, -0.46_24, 0.71_92] )
										else:
															A     =   None

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

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

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


if __name__ == "__main__":
	__A	:       Optional[Any]        =						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.'
	)

	__A	:       List[str]        =						parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)









def 		snake_case_   (				lowercase__     :    str     ):






				'''simple docstring'''






				_lowerCAmelCase  						=53_81
				for x in s:
								_lowerCAmelCase  						=((hash_value << 5) + hash_value) + ord(lowercase__     )
				return hash_value & 0Xffff_ffff

