





'''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()
snake_case__          =						logging.get_logger(__name__)




def 						snake_case__    ( lowerCamelCase__				:   Dict			)   ->   Optional[Any]:
					A_ :     Union[str, Any] =							MobileNetVaConfig(layer_norm_eps=0.001			)

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

					A_ :     List[Any] =							re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''			,		lowercase_			)
					if matches:
										A_ :     str =							float(matches[1]			)
										A_ :     Optional[int] =							int(matches[2]			)

					if model_name.startswith('''deeplabv3_'''			):
										A_ :     Any =							8
										A_ :     Dict =							2_1
										A_ :     Dict =							'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_ :     Dict =							1_0_0_1
										A_ :     int =							'imagenet-1k-id2label.json'

					A_ :     List[str] =							'huggingface/label-files'
					A_ :     Union[str, Any] =							json.load(open(hf_hub_download(lowercase_			,		lowercase_			,		repo_type='''dataset'''			)			,		'''r'''			)			)

					if config.num_labels == 1_0_0_1:
										A_ :     Union[str, Any] =							{int(lowercase_			) + 1: v for k, v in idalabel.items()}
										A_ :     Tuple =							'background'
					else:
										A_ :     Tuple =							{int(lowercase_			): v for k, v in idalabel.items()}

					A_ :     List[Any] =							idalabel
					A_ :     List[Any] =							{v: k for k, v in idalabel.items()}

					return config




def 						snake_case__    ( )   ->   Any:
					A_ :     List[Any] =							'http://images.cocodataset.org/val2017/000000039769.jpg'
					A_ :     Optional[Any] =							Image.open(requests.get(lowercase_			,		stream=lowercase_			).raw			)
					return im




@torch.no_grad()
def 						snake_case__    ( lowerCamelCase__				:   int			,		lowerCamelCase__				:   Any			,		lowerCamelCase__				:   Optional[Any]			,		lowerCamelCase__				:   Union[str, Any]=False			)   ->   Tuple:
					A_ :     Optional[Any] =							get_mobilenet_va_config(lowercase_			)

					# Load 🤗 model
					if model_name.startswith('''deeplabv3_'''			):
										A_ :     Any =							MobileNetVaForSemanticSegmentation(lowercase_			).eval()
					else:
										A_ :     str =							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_ :     Optional[int] =							MobileNetVaImageProcessor(
					    crop_size={'''width''': config.image_size, '''height''': config.image_size}			,		size={'''shortest_edge''': config.image_size + 3_2}			,		)
					A_ :     int =							image_processor(images=prepare_img()			,		return_tensors='''pt'''			)
					A_ :     List[Any] =							model(**lowercase_			)
					A_ :     str =							outputs.logits

					if model_name.startswith('''deeplabv3_'''			):
										assert logits.shape == (1, 2_1, 6_5, 6_5)

										if model_name == "deeplabv3_mobilenet_v2_1.0_513":
															A_ :     str =							torch.tensor(
															    [
															        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
															        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
															        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
															    ]			)

										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_0_0_1)

										if model_name == "mobilenet_v2_1.4_224":
															A_ :     Dict =							torch.tensor([0.0181, -1.0015, 0.4688]			)
										elif model_name == "mobilenet_v2_1.0_224":
															A_ :     str =							torch.tensor([0.2445, -1.1993, 0.1905]			)
										elif model_name == "mobilenet_v2_0.75_160":
															A_ :     List[str] =							torch.tensor([0.2482, 0.4136, 0.6669]			)
										elif model_name == "mobilenet_v2_0.35_96":
															A_ :     Optional[int] =							torch.tensor([0.1451, -0.4624, 0.7192]			)
										else:
															A_ :     int =							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_ :     Optional[int] =							'google/' + model_name
										image_processor.push_to_hub(lowercase_			)
										model.push_to_hub(lowercase_			)


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

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









'''simple docstring'''




def 						snake_case__    ( lowerCamelCase__				:   int			)   ->   bool:
					if not isinstance(lowerCamelCase__			,		lowerCamelCase__			):
										A_ :     int =							f'Input value of [number={number}] must be an integer'
										raise TypeError(lowerCamelCase__			)
					if number < 0 or number % 2 == 1:
										return False
					A_ :     Dict =							int(number**0.5			)
					return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()



