




"""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()
__lowerCamelCase								=						logging.get_logger(__name__)



def 				a (						__snake_case     :				str  ):



				'''simple docstring'''





				UpperCAmelCase_					:List[Any]      			=      MobileNetVaConfig(layer_norm_eps=0.001  )

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

				UpperCAmelCase_					:List[str]      			=      re.match(r'''^.*mobilenet_v2_([^_]*)_([^_]*)$''',    __snake_case  )
				if matches:
								UpperCAmelCase_					:List[str]      			=      float(matches[1]  )
								UpperCAmelCase_					:List[str]      			=      int(matches[2]  )

				if model_name.startswith('''deeplabv3_'''  ):
								UpperCAmelCase_					:int      			=      8
								UpperCAmelCase_					:Optional[Any]      			=      21
								UpperCAmelCase_					:List[str]      			=      '''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".
								UpperCAmelCase_					:Union[str, Any]      			=      1001
								UpperCAmelCase_					:Optional[Any]      			=      '''imagenet-1k-id2label.json'''

				UpperCAmelCase_					:List[Any]      			=      '''huggingface/label-files'''
				UpperCAmelCase_					:Optional[int]      			=      json.load(open(hf_hub_download(__snake_case,    __snake_case,    repo_type='''dataset'''  ),    '''r'''  )  )

				if config.num_labels == 1001:
								UpperCAmelCase_					:int      			=      {int(__snake_case  ) + 1: v for k, v in idalabel.items()}
								UpperCAmelCase_					:List[Any]      			=      '''background'''
				else:
								UpperCAmelCase_					:Union[str, Any]      			=      {int(__snake_case  ): v for k, v in idalabel.items()}

				UpperCAmelCase_					:Optional[Any]      			=      idalabel
				UpperCAmelCase_					:int      			=      {v: k for k, v in idalabel.items()}

				return config



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Union[str, Any]      			=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				UpperCAmelCase_					:Tuple      			=      Image.open(requests.get(__snake_case,    stream=__snake_case  ).raw  )
				return im



@torch.no_grad()
def 				a (						__snake_case     :				Dict,    __snake_case     :				Optional[Any],    __snake_case     :				List[str],    __snake_case     :				Optional[int]=False  ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[int]      			=      get_mobilenet_va_config(__snake_case  )

				# Load 🤗 model
				if model_name.startswith('''deeplabv3_'''  ):
								UpperCAmelCase_					:Union[str, Any]      			=      MobileNetVaForSemanticSegmentation(__snake_case  ).eval()
				else:
								UpperCAmelCase_					:Union[str, Any]      			=      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 MobileNetV2ImageProcessor
				UpperCAmelCase_					:Any      			=      MobileNetVaImageProcessor(
				    crop_size={'''width''': config.image_size, '''height''': config.image_size},    size={'''shortest_edge''': config.image_size + 32},    )
				UpperCAmelCase_					:str      			=      image_processor(images=prepare_img(),    return_tensors='''pt'''  )
				UpperCAmelCase_					:int      			=      model(**__snake_case  )
				UpperCAmelCase_					:Union[str, Any]      			=      outputs.logits

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

								if model_name == "deeplabv3_mobilenet_v2_1.0_513":
												UpperCAmelCase_					:Tuple      			=      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],    __snake_case,    atol=1E-4  )
				else:
								assert logits.shape == (1, 1001)

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


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

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








"""simple docstring"""

def 				a (						__snake_case     :				int  ):



				'''simple docstring'''





				if not isinstance(__snake_case,    __snake_case  ) or number < 0:
								raise ValueError('''Input must be a non-negative integer'''  )

				UpperCAmelCase_					:Optional[Any]      			=      number & ~(number - 1)
				UpperCAmelCase_					:List[str]      			=      0
				while intermediate:
								intermediate >>= 1
								index += 1
				return index - 1


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)


