


"""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      			= logging.get_logger(__name__)






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     MobileNetVaConfig(layer_norm_eps=0.001			)

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

						UpperCAmelCase__												=     re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''						,      _UpperCamelCase			)
						if matches:
												UpperCAmelCase__												=     float(matches[1]			)
												UpperCAmelCase__												=     int(matches[2]			)

						if model_name.startswith('''deeplabv3_'''			):
												UpperCAmelCase__												=     8
												UpperCAmelCase__												=     21
												UpperCAmelCase__												=     '''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__												=     1001
												UpperCAmelCase__												=     '''imagenet-1k-id2label.json'''

						UpperCAmelCase__												=     '''huggingface/label-files'''
						UpperCAmelCase__												=     json.load(open(hf_hub_download(_UpperCamelCase						,      _UpperCamelCase						,      repo_type='''dataset'''			)						,      '''r'''			)			)

						if config.num_labels == 1001:
												UpperCAmelCase__												=     {int(_UpperCamelCase			) + 1: v for k, v in idalabel.items()}
												UpperCAmelCase__												=     '''background'''
						else:
												UpperCAmelCase__												=     {int(_UpperCamelCase			): v for k, v in idalabel.items()}

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

						return config






def        a__			(							):





						"""simple docstring"""

						UpperCAmelCase__												=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						UpperCAmelCase__												=     Image.open(requests.get(_UpperCamelCase						,      stream=_UpperCamelCase			).raw			)
						return im






@torch.no_grad()
def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase=False			):





						"""simple docstring"""

						UpperCAmelCase__												=     get_mobilenet_va_config(_UpperCamelCase			)

						# Load 🤗 model
						if model_name.startswith('''deeplabv3_'''			):
												UpperCAmelCase__												=     MobileNetVaForSemanticSegmentation(_UpperCamelCase			).eval()
						else:
												UpperCAmelCase__												=     MobileNetVaForImageClassification(_UpperCamelCase			).eval()

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

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

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

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

												if model_name == "mobilenet_v2_1.4_224":
																		UpperCAmelCase__												=     torch.tensor([0.0181, -1.0015, 0.4688]			)
												elif model_name == "mobilenet_v2_1.0_224":
																		UpperCAmelCase__												=     torch.tensor([0.2445, -1.1993, 0.1905]			)
												elif model_name == "mobilenet_v2_0.75_160":
																		UpperCAmelCase__												=     torch.tensor([0.2482, 0.4136, 0.6669]			)
												elif model_name == "mobilenet_v2_0.35_96":
																		UpperCAmelCase__												=     torch.tensor([0.1451, -0.4624, 0.7192]			)
												else:
																		UpperCAmelCase__												=     None

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

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

						if push_to_hub:
												print('''Pushing to the hub...'''			)
												UpperCAmelCase__												=     '''google/''' + model_name
												image_processor.push_to_hub(_UpperCamelCase			)
												model.push_to_hub(_UpperCamelCase			)


if __name__ == "__main__":
						__a      			= 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      			= 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__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round((float(_UpperCamelCase			) * 9 / 5) + 32						,      _UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round(float(_UpperCamelCase			) + 273.15						,      _UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round((float(_UpperCamelCase			) * 9 / 5) + 491.67						,      _UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round((float(_UpperCamelCase			) - 32) * 5 / 9						,      _UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round(((float(_UpperCamelCase			) - 32) * 5 / 9) + 273.15						,      _UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round(float(_UpperCamelCase			) + 459.67						,      _UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round(float(_UpperCamelCase			) - 273.15						,      _UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round(((float(_UpperCamelCase			) - 273.15) * 9 / 5) + 32						,      _UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round((float(_UpperCamelCase			) * 9 / 5)						,      _UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round((float(_UpperCamelCase			) - 491.67) * 5 / 9						,      _UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round(float(_UpperCamelCase			) - 459.67						,      _UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round((float(_UpperCamelCase			) * 5 / 9)						,      _UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round((float(_UpperCamelCase			) * 1.25 + 273.15)						,      _UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round((float(_UpperCamelCase			) * 2.25 + 32)						,      _UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round((float(_UpperCamelCase			) * 1.25)						,      _UpperCamelCase			)






def        a__			(							_UpperCamelCase						,      _UpperCamelCase = 2			):





						"""simple docstring"""

						return round((float(_UpperCamelCase			) * 2.25 + 32 + 459.67)						,      _UpperCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()