






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		:	Optional[Any]      					=			logging.get_logger(__name__)






def     lowerCamelCase__				(   __lowerCamelCase      :					Any		):
		__UpperCAmelCase							:     List[str]									=  MobileNetVaConfig(layer_norm_eps=0.0_0_1		)

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

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

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

		__UpperCAmelCase							:     Any									=  """huggingface/label-files"""
		__UpperCAmelCase							:     str									=  json.load(open(hf_hub_download(lowerCamelCase_  ,		lowerCamelCase_  ,		repo_type="""dataset"""		)  ,		"""r"""		)		)

		if config.num_labels == 1001:
				__UpperCAmelCase							:     List[Any]									=  {int(lowerCamelCase_		) + 1: v for k, v in idalabel.items()}
				__UpperCAmelCase							:     List[str]									=  """background"""
		else:
				__UpperCAmelCase							:     Tuple									=  {int(lowerCamelCase_		): v for k, v in idalabel.items()}

		__UpperCAmelCase							:     Union[str, Any]									=  idalabel
		__UpperCAmelCase							:     Optional[int]									=  {v: k for k, v in idalabel.items()}

		return config






def     lowerCamelCase__				(   ):
		__UpperCAmelCase							:     Tuple									=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
		__UpperCAmelCase							:     Dict									=  Image.open(requests.get(lowerCamelCase_  ,		stream=lowerCamelCase_		).raw		)
		return im






@torch.no_grad()
def     lowerCamelCase__				(   __lowerCamelCase      :					List[str]  ,		__lowerCamelCase      :					List[str]  ,		__lowerCamelCase      :					str  ,		__lowerCamelCase      :					Optional[Any]=False		):
		__UpperCAmelCase							:     List[Any]									=  get_mobilenet_va_config(lowerCamelCase_		)

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

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

				if model_name == "deeplabv3_mobilenet_v2_1.0_513":
						__UpperCAmelCase							:     Optional[int]									=  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]  ,		lowerCamelCase_  ,		atol=1E-4		)
		else:
				assert logits.shape == (1, 1001)

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


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










def     lowerCamelCase__				(   __lowerCamelCase      :					list		):
  if len(__lowerCamelCase		) <= 1:
    return data
  else:
    return [
        *quick_sort([e for e in data[1:] if e <= data[0]]		),
        data[0],
        *quick_sort([e for e in data[1:] if e > data[0]]		),
    ]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


