



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()
UpperCAmelCase__			:   Optional[int]      			=    logging.get_logger(__name__)


def 	_A	(   _UpperCamelCase			):
				_UpperCAmelCase						:  Optional[Any]     					=	MobileNetVaConfig(layer_norm_eps=0.001			)

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

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

				if model_name.startswith('''deeplabv3_'''			):
								_UpperCAmelCase						:  Optional[Any]     					=	8
								_UpperCAmelCase						:  List[str]     					=	21
								_UpperCAmelCase						:  Tuple     					=	'''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]     					=	1_001
								_UpperCAmelCase						:  Tuple     					=	'''imagenet-1k-id2label.json'''

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

				if config.num_labels == 1_001:
								_UpperCAmelCase						:  Any     					=	{int(_UpperCamelCase			) + 1: v for k, v in idalabel.items()}
								_UpperCAmelCase						:  Tuple     					=	'''background'''
				else:
								_UpperCAmelCase						:  str     					=	{int(_UpperCamelCase			): v for k, v in idalabel.items()}

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

				return config


def 	_A	(   ):
				_UpperCAmelCase						:  int     					=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_UpperCAmelCase						:  Optional[int]     					=	Image.open(requests.get(_UpperCamelCase      ,					stream=_UpperCamelCase			).raw			)
				return im


@torch.no_grad()
def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase=False			):
				_UpperCAmelCase						:  Optional[Any]     					=	get_mobilenet_va_config(_UpperCamelCase			)

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

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

								if model_name == "deeplabv3_mobilenet_v2_1.0_513":
												_UpperCAmelCase						:  Union[str, Any]     					=	torch.tensor(
												    [
												        [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
												        [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
												        [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
												    ]			)

								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, 1_001)

								if model_name == "mobilenet_v2_1.4_224":
												_UpperCAmelCase						:  Any     					=	torch.tensor([0.0_181, -1.0_015, 0.4_688]			)
								elif model_name == "mobilenet_v2_1.0_224":
												_UpperCAmelCase						:  int     					=	torch.tensor([0.2_445, -1.1_993, 0.1_905]			)
								elif model_name == "mobilenet_v2_0.75_160":
												_UpperCAmelCase						:  str     					=	torch.tensor([0.2_482, 0.4_136, 0.6_669]			)
								elif model_name == "mobilenet_v2_0.35_96":
												_UpperCAmelCase						:  List[str]     					=	torch.tensor([0.1_451, -0.4_624, 0.7_192]			)
								else:
												_UpperCAmelCase						:  Union[str, Any]     					=	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						:  int     					=	'''google/''' + model_name
								image_processor.push_to_hub(_UpperCamelCase			)
								model.push_to_hub(_UpperCamelCase			)


if __name__ == "__main__":
					UpperCAmelCase__			:   int      			=    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.'
					)

					UpperCAmelCase__			:   Union[str, Any]      			=    parser.parse_args()
					convert_movilevit_checkpoint(
					    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)





UpperCAmelCase__			:   Optional[int]      			=    [0.4_85, 0.4_56, 0.4_06]
UpperCAmelCase__			:   Union[str, Any]      			=    [0.2_29, 0.2_24, 0.2_25]
UpperCAmelCase__			:   Union[str, Any]      			=    [0.5, 0.5, 0.5]
UpperCAmelCase__			:   Optional[Any]      			=    [0.5, 0.5, 0.5]
UpperCAmelCase__			:   str      			=    [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
UpperCAmelCase__			:   Dict      			=    [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
