code: 
  
  import argparse
  import json
  import os
  
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
  from transformers.image_utils import PILImageResampling
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  A_:      Optional[int]        =			logging.get_logger(__name__)
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	ConvNextVaConfig()
  
  							if "atto" in checkpoint_url:
  														_lowercase				=	[2, 2, 6, 2]
  														_lowercase				=	[40, 80, 160, 320]
  							if "femto" in checkpoint_url:
  														_lowercase				=	[2, 2, 6, 2]
  														_lowercase				=	[48, 96, 192, 384]
  							if "pico" in checkpoint_url:
  														_lowercase				=	[2, 2, 6, 2]
  														_lowercase				=	[64, 128, 256, 512]
  							if "nano" in checkpoint_url:
  														_lowercase				=	[2, 2, 8, 2]
  														_lowercase				=	[80, 160, 320, 640]
  							if "tiny" in checkpoint_url:
  														_lowercase				=	[3, 3, 9, 3]
  														_lowercase				=	[96, 192, 384, 768]
  							if "base" in checkpoint_url:
  														_lowercase				=	[3, 3, 27, 3]
  														_lowercase				=	[128, 256, 512, 1_024]
  							if "large" in checkpoint_url:
  														_lowercase				=	[3, 3, 27, 3]
  														_lowercase				=	[192, 384, 768, 1_536]
  							if "huge" in checkpoint_url:
  														_lowercase				=	[3, 3, 27, 3]
  														_lowercase				=	[352, 704, 1_408, 2_816]
  
  							_lowercase				=	1_000
  							_lowercase				=	"""imagenet-1k-id2label.json"""
  							_lowercase				=	(1, 1_000)
  
  							_lowercase				=	"""huggingface/label-files"""
  							_lowercase				=	num_labels
  							_lowercase				=	json.load(open(hf_hub_download(_A ,_A ,repo_type="""dataset"""					) ,"""r"""					)					)
  							_lowercase				=	{int(_A					): v for k, v in idalabel.items()}
  
  							_lowercase				=	idalabel
  							_lowercase				=	{v: k for k, v in idalabel.items()}
  							_lowercase				=	hidden_sizes
  							_lowercase				=	depths
  
  							return config, expected_shape
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							if "downsample_layers.0.0" in name:
  														_lowercase				=	name.replace("""downsample_layers.0.0""" ,"""embeddings.patch_embeddings"""					)
  							if "downsample_layers.0.1" in name:
  														_lowercase				=	name.replace("""downsample_layers.0.1""" ,"""embeddings.norm"""					)  # we rename to layernorm later on
  							if "downsample_layers.1.0" in name:
  														_lowercase				=	name.replace("""downsample_layers.1.0""" ,"""stages.1.downsampling_layer.0"""					)
  							if "downsample_layers.1.1" in name:
  														_lowercase				=	name.replace("""downsample_layers.1.1""" ,"""stages.1.downsampling_layer.1"""					)
  							if "downsample_layers.2.0" in name:
  														_lowercase				=	name.replace("""downsample_layers.2.0""" ,"""stages.2.downsampling_layer.0"""					)
  							if "downsample_layers.2.1" in name:
  														_lowercase				=	name.replace("""downsample_layers.2.1""" ,"""stages.2.downsampling_layer.1"""					)
  							if "downsample_layers.3.0" in name:
  														_lowercase				=	name.replace("""downsample_layers.3.0""" ,"""stages.3.downsampling_layer.0"""					)
  							if "downsample_layers.3.1" in name:
  														_lowercase				=	name.replace("""downsample_layers.3.1""" ,"""stages.3.downsampling_layer.1"""					)
  							if "stages" in name and "downsampling_layer" not in name:
  														# stages.0.0. for instance should be renamed to stages.0.layers.0.
  														_lowercase				=	name[: len("""stages.0"""					)] + """.layers""" + name[len("""stages.0"""					) :]
  							if "gamma" in name:
  														_lowercase				=	name.replace("""gamma""" ,"""weight"""					)
  							if "beta" in name:
  														_lowercase				=	name.replace("""beta""" ,"""bias"""					)
  							if "stages" in name:
  														_lowercase				=	name.replace("""stages""" ,"""encoder.stages"""					)
  							if "norm" in name:
  														_lowercase				=	name.replace("""norm""" ,"""layernorm"""					)
  							if "head" in name:
  														_lowercase				=	name.replace("""head""" ,"""classifier"""					)
  
  							return name
  
  
  def 					__lowerCAmelCase			(  ):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  							_lowercase				=	Image.open(requests.get(_A ,stream=_A					).raw					)
  							return im
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							if "224" in checkpoint_url:
  														_lowercase				=	224
  														_lowercase				=	224 / 256
  							elif "384" in checkpoint_url:
  														_lowercase				=	384
  														_lowercase				=	None
  							else:
  														_lowercase				=	512
  														_lowercase				=	None
  
  							return ConvNextImageProcessor(
  							    size=_A ,crop_pct=_A ,image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] ,image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5] ,resample=PILImageResampling.BICUBIC ,)
  
  
  @torch.no_grad()
  def 					__lowerCAmelCase			(  _A ,_A ,_A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							print("""Downloading original model from checkpoint..."""					)
  							# define ConvNeXTV2 configuration based on URL
  							_lowercase				,       _lowercase				=	get_convnextva_config(_A					)
  							# load original state_dict from URL
  							_lowercase				=	torch.hub.load_state_dict_from_url(_A					)["""model"""]
  
  							print("""Converting model parameters..."""					)
  							# rename keys
  							for key in state_dict.copy().keys():
  														_lowercase				=	state_dict.pop(_A					)
  														_lowercase				=	val
  							# add prefix to all keys expect classifier head
  							for key in state_dict.copy().keys():
  														_lowercase				=	state_dict.pop(_A					)
  														if not key.startswith("""classifier"""					):
  																					_lowercase				=	"""convnextv2.""" + key
  														_lowercase				=	val
  
  							# load HuggingFace model
  							_lowercase				=	ConvNextVaForImageClassification(_A					)
  							model.load_state_dict(_A					)
  							model.eval()
  
  							# Check outputs on an image, prepared by ConvNextImageProcessor
  							_lowercase				=	convert_preprocessor(_A					)
  							_lowercase				=	preprocessor(images=prepare_img() ,return_tensors="""pt"""					)
  							_lowercase				=	model(**_A					).logits
  
  							# note: the logits below were obtained without center cropping
  							if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
  														_lowercase				=	torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
  														_lowercase				=	torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
  														_lowercase				=	torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
  														_lowercase				=	torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
  														_lowercase				=	torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
  														_lowercase				=	torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
  														_lowercase				=	torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
  														_lowercase				=	torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
  														_lowercase				=	torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
  														_lowercase				=	torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
  														_lowercase				=	torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
  														_lowercase				=	torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
  														_lowercase				=	torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
  														_lowercase				=	torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
  														_lowercase				=	torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
  														_lowercase				=	torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
  														_lowercase				=	torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9]					)
  							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
  														_lowercase				=	torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6]					)
  							else:
  														raise ValueError(f'''Unknown URL: {checkpoint_url}'''					)
  
  							assert torch.allclose(logits[0, :5] ,_A ,atol=1E-3					)
  							assert logits.shape == expected_shape
  							print("""Model outputs match the original results!"""					)
  
  							if save_model:
  														print("""Saving model to local..."""					)
  														# Create folder to save model
  														if not os.path.isdir(_A					):
  																					os.mkdir(_A					)
  
  														model.save_pretrained(_A					)
  														preprocessor.save_pretrained(_A					)
  
  							_lowercase				=	"""convnextv2"""
  							if "atto" in checkpoint_url:
  														model_name += "-atto"
  							if "femto" in checkpoint_url:
  														model_name += "-femto"
  							if "pico" in checkpoint_url:
  														model_name += "-pico"
  							if "nano" in checkpoint_url:
  														model_name += "-nano"
  							elif "tiny" in checkpoint_url:
  														model_name += "-tiny"
  							elif "base" in checkpoint_url:
  														model_name += "-base"
  							elif "large" in checkpoint_url:
  														model_name += "-large"
  							elif "huge" in checkpoint_url:
  														model_name += "-huge"
  							if "22k" in checkpoint_url and "1k" not in checkpoint_url:
  														model_name += "-22k"
  							elif "22k" in checkpoint_url and "1k" in checkpoint_url:
  														model_name += "-22k-1k"
  							elif "1k" in checkpoint_url:
  														model_name += "-1k"
  							if "224" in checkpoint_url:
  														model_name += "-224"
  							elif "384" in checkpoint_url:
  														model_name += "-384"
  							elif "512" in checkpoint_url:
  														model_name += "-512"
  
  							if push_to_hub:
  														print(f'''Pushing {model_name} to the hub...'''					)
  														model.push_to_hub(_A					)
  														preprocessor.push_to_hub(_A					)
  
  
  if __name__ == "__main__":
  				A_:      str        =			argparse.ArgumentParser()
  				# Required parameters
  				parser.add_argument(
  				    '--checkpoint_url',
  				    default='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt',
  				    type=str,
  				    help='URL of the original ConvNeXTV2 checkpoint you\'d like to convert.',
  				)
  				parser.add_argument(
  				    '--pytorch_dump_folder_path',
  				    default='model',
  				    type=str,
  				    help='Path to the output PyTorch model directory.',
  				)
  				parser.add_argument('--save_model', action='store_true', help='Save model to local')
  				parser.add_argument('--push_to_hub', action='store_true', help='Push model and image preprocessor to the hub')
  
  				A_:      Optional[int]        =			parser.parse_args()
  				convert_convnextva_checkpoint(
  				    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
  				)
  
  
  
style_context: def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							if len(_A					) <= 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()
  
  
  