



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__  :  Tuple       = logging.get_logger(__name__)


def       UpperCamelCase(  __UpperCamelCase							:		int			):
		lowerCAmelCase_      :					Tuple     =			MobileNetVaConfig(layer_norm_eps=0.0_0_1			)

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

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

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

		lowerCAmelCase_      :					Any     =			'''huggingface/label-files'''
		lowerCAmelCase_      :					Dict     =			json.load(open(hf_hub_download(__UpperCamelCase					,__UpperCamelCase					,repo_type='''dataset'''			)					,'''r'''			)			)

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

		lowerCAmelCase_      :					Tuple     =			idalabel
		lowerCAmelCase_      :					Dict     =			{v: k for k, v in idalabel.items()}

		return config


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


@torch.no_grad()
def       UpperCamelCase(  __UpperCamelCase							:		List[Any]					,__UpperCamelCase							:		List[str]					,__UpperCamelCase							:		Any					,__UpperCamelCase							:		Dict=False			):
		lowerCAmelCase_      :					Dict     =			get_mobilenet_va_config(__UpperCamelCase			)

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

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

				if model_name == "deeplabv3_mobilenet_v2_1.0_513":
						lowerCAmelCase_      :					Any     =			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, 1001)

				if model_name == "mobilenet_v2_1.4_224":
						lowerCAmelCase_      :					Tuple     =			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":
						lowerCAmelCase_      :					List[str]     =			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":
						lowerCAmelCase_      :					Any     =			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":
						lowerCAmelCase_      :					Dict     =			torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]			)
				else:
						lowerCAmelCase_      :					Optional[int]     =			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...'''			)
				lowerCAmelCase_      :					Optional[int]     =			'''google/''' + model_name
				image_processor.push_to_hub(__UpperCamelCase			)
				model.push_to_hub(__UpperCamelCase			)


if __name__ == "__main__":
						A__  :  Optional[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.'''
						)

						A__  :  Tuple       = parser.parse_args()
						convert_movilevit_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)










A__  :  str       = 1000


def       UpperCamelCase(  __UpperCamelCase							:		float			):
		return x * x


def       UpperCamelCase(  __UpperCamelCase							:		Optional[int]					,__UpperCamelCase							:		float					,__UpperCamelCase							:		float					,__UpperCamelCase							:		int = 4			):
		assert callable(
		    __UpperCamelCase			), f"""the function(object) passed should be callable your input : {function}"""
		assert isinstance(__UpperCamelCase					,(float, int)			), f"""a should be float or integer your input : {a}"""
		assert isinstance(function(__UpperCamelCase			)					,(float, int)			), (
		    "the function should return integer or float return type of your function, "
		    f"""{type(__UpperCamelCase			)}"""
		)
		assert isinstance(__UpperCamelCase					,(float, int)			), f"""b should be float or integer your input : {b}"""
		assert (
		    isinstance(__UpperCamelCase					,__UpperCamelCase			) and precision > 0
		), f"""precision should be positive integer your input : {precision}"""

		# just applying the formula of simpson for approximate integration written in
		# mentioned article in first comment of this file and above this function

		lowerCAmelCase_      :					List[str]     =			(b - a) / N_STEPS
		lowerCAmelCase_      :					str     =			function(__UpperCamelCase			) + function(__UpperCamelCase			)

		for i in range(1					,__UpperCamelCase			):
				lowerCAmelCase_      :					Optional[Any]     =			a + h * i
				result += function(__UpperCamelCase			) * (4 if i % 2 else 2)

		result *= h / 3
		return round(__UpperCamelCase					,__UpperCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





