



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()
__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)




def 						UpperCAmelCase (    _lowerCamelCase     ):
			A				:       List[Any]														=    MobileNetVaConfig(layer_norm_eps=0.0_01     )

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

			A				:       Optional[int]														=    re.match(R"^.*mobilenet_v2_([^_]*)_([^_]*)$"			,     _lowerCamelCase     )
			if matches:
						A				:       Union[str, Any]														=    float(matches[1]     )
						A				:       Union[str, Any]														=    int(matches[2]     )

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

			A				:       Optional[int]														=    "huggingface/label-files"
			A				:       Optional[Any]														=    json.load(open(hf_hub_download(_lowerCamelCase			,     _lowerCamelCase			,     repo_type="dataset"     )			,     "r"     )     )

			if config.num_labels == 1001:
						A				:       int														=    {int(_lowerCamelCase     ) + 1: v for k, v in idalabel.items()}
						A				:       Any														=    "background"
			else:
						A				:       Optional[int]														=    {int(_lowerCamelCase     ): v for k, v in idalabel.items()}

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

			return config




def 						UpperCAmelCase (    ):
			A				:       Union[str, Any]														=    "http://images.cocodataset.org/val2017/000000039769.jpg"
			A				:       str														=    Image.open(requests.get(_lowerCamelCase			,     stream=_lowerCamelCase     ).raw     )
			return im




@torch.no_grad()
def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase=False     ):
			A				:       int														=    get_mobilenet_va_config(_lowerCamelCase     )

			# Load 🤗 model
			if model_name.startswith("deeplabv3_"     ):
						A				:       Optional[Any]														=    MobileNetVaForSemanticSegmentation(_lowerCamelCase     ).eval()
			else:
						A				:       Optional[int]														=    MobileNetVaForImageClassification(_lowerCamelCase     ).eval()

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

			# Check outputs on an image, prepared by MobileNetV2ImageProcessor
			A				:       List[Any]														=    MobileNetVaImageProcessor(
			    crop_size={"width": config.image_size, "height": config.image_size}			,     size={"shortest_edge": config.image_size + 32}			,     )
			A				:       Dict														=    image_processor(images=prepare_img()			,     return_tensors="pt"     )
			A				:       Optional[int]														=    model(**_lowerCamelCase     )
			A				:       List[str]														=    outputs.logits

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

						if model_name == "deeplabv3_mobilenet_v2_1.0_513":
									A				:       List[Any]														=    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]			,     _lowerCamelCase			,     atol=1e-4     )
			else:
						assert logits.shape == (1, 1001)

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

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

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

			if push_to_hub:
						print("Pushing to the hub..."     )
						A				:       Optional[Any]														=    "google/" + model_name
						image_processor.push_to_hub(_lowerCamelCase     )
						model.push_to_hub(_lowerCamelCase     )


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

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




def 						UpperCAmelCase (    _lowerCamelCase     ):
			return "".join(chr(ord(_lowerCamelCase     ) + 32     ) if "A" <= char <= "Z" else char for char in word     )


if __name__ == "__main__":
							from doctest import testmod

							testmod()