



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								=   logging.get_logger(__name__)

def      A_ (		__a       :			List[str]						):

				"""simple docstring"""
				a__			 =						MobileNetVaConfig(layer_norm_eps=0.0_0_1						)

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

				a__			 =						re.match(r"""^.*mobilenet_v2_([^_]*)_([^_]*)$"""	,						_UpperCamelCase						)
				if matches:
								a__			 =						float(matches[1]						)
								a__			 =						int(matches[2]						)

				if model_name.startswith("""deeplabv3_"""						):
								a__			 =						8
								a__			 =						21
								a__			 =						"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__			 =						1_001
								a__			 =						"imagenet-1k-id2label.json"

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

				if config.num_labels == 1_001:
								a__			 =						{int(_UpperCamelCase						) + 1: v for k, v in idalabel.items()}
								a__			 =						"background"
				else:
								a__			 =						{int(_UpperCamelCase						): v for k, v in idalabel.items()}

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

				return config

def      A_ (		):

				"""simple docstring"""
				a__			 =						"http://images.cocodataset.org/val2017/000000039769.jpg"
				a__			 =						Image.open(requests.get(_UpperCamelCase	,						stream=_UpperCamelCase						).raw						)
				return im

@torch.no_grad()
def      A_ (		__a       :			str	,						__a       :			Optional[int]	,						__a       :			Tuple	,						__a       :			List[Any]=False						):

				"""simple docstring"""
				a__			 =						get_mobilenet_va_config(_UpperCamelCase						)

				# Load 🤗 model
				if model_name.startswith("""deeplabv3_"""						):
								a__			 =						MobileNetVaForSemanticSegmentation(_UpperCamelCase						).eval()
				else:
								a__			 =						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
				a__			 =						MobileNetVaImageProcessor(
				    crop_size={"""width""": config.image_size, """height""": config.image_size}	,						size={"""shortest_edge""": config.image_size + 32}	,						)
				a__			 =						image_processor(images=prepare_img()	,						return_tensors="""pt"""						)
				a__			 =						model(**_UpperCamelCase						)
				a__			 =						outputs.logits

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

								if model_name == "deeplabv3_mobilenet_v2_1.0_513":
												a__			 =						torch.tensor(
												    [
												        [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
												        [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
												        [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
												    ]						)

								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":
												a__			 =						torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]						)
								elif model_name == "mobilenet_v2_1.0_224":
												a__			 =						torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]						)
								elif model_name == "mobilenet_v2_0.75_160":
												a__			 =						torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]						)
								elif model_name == "mobilenet_v2_0.35_96":
												a__			 =						torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]						)
								else:
												a__			 =						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..."""						)
								a__			 =						"google/" + model_name
								image_processor.push_to_hub(_UpperCamelCase						)
								model.push_to_hub(_UpperCamelCase						)


if __name__ == "__main__":
	UpperCAmelCase								=   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								=   parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)






from __future__ import annotations

def      A_ (		__a       :			list[int | float]						):

				"""simple docstring"""
				if len(__a						) == 0:
								raise ValueError("""find_max() arg is an empty sequence"""						)
				a__			 =						nums[0]
				for x in nums:
								if x > max_num:
												a__			 =						x
				return max_num


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)

