code: 
  import argparse
  import json
  from pathlib import Path
  
  import requests
  import torch
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  snake_case				:	Tuple							   =						logging.get_logger(__name__)
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Optional[int]     )       ->	Dict:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	List[Any]			     =  ConvNextConfig()
  
  	if "tiny" in checkpoint_url:
  		__magic_name__	:	Dict			     =  [3, 3, 9, 3]
  		__magic_name__	:	Optional[Any]			     =  [96, 192, 384, 768]
  	if "small" in checkpoint_url:
  		__magic_name__	:	Optional[Any]			     =  [3, 3, 27, 3]
  		__magic_name__	:	int			     =  [96, 192, 384, 768]
  	if "base" in checkpoint_url:
  		__magic_name__	:	List[str]			     =  [3, 3, 27, 3]
  		__magic_name__	:	int			     =  [128, 256, 512, 1024]
  	if "large" in checkpoint_url:
  		__magic_name__	:	List[str]			     =  [3, 3, 27, 3]
  		__magic_name__	:	str			     =  [192, 384, 768, 1536]
  	if "xlarge" in checkpoint_url:
  		__magic_name__	:	Union[str, Any]			     =  [3, 3, 27, 3]
  		__magic_name__	:	Optional[Any]			     =  [256, 512, 1024, 2048]
  
  	if "1k" in checkpoint_url:
  		__magic_name__	:	Dict			     =  1000
  		__magic_name__	:	List[str]			     =  "imagenet-1k-id2label.json"
  		__magic_name__	:	int			     =  (1, 1000)
  	else:
  		__magic_name__	:	Optional[Any]			     =  21841
  		__magic_name__	:	int			     =  "imagenet-22k-id2label.json"
  		__magic_name__	:	Any			     =  (1, 21841)
  
  	__magic_name__	:	List[str]			     =  "huggingface/label-files"
  	__magic_name__	:	List[str]			     =  num_labels
  	__magic_name__	:	str			     =  json.load(open(hf_hub_download(_snake_case   ,     _snake_case   ,     repo_type="dataset"     )   ,     "r"     )     )
  	__magic_name__	:	List[Any]			     =  {int(_snake_case     ): v for k, v in idalabel.items()}
  	if "1k" not in checkpoint_url:
  		# this dataset contains 21843 labels but the model only has 21841
  		# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
  		del idalabel[9205]
  		del idalabel[15027]
  	__magic_name__	:	Optional[Any]			     =  idalabel
  	__magic_name__	:	int			     =  {v: k for k, v in idalabel.items()}
  	__magic_name__	:	int			     =  hidden_sizes
  	__magic_name__	:	Optional[int]			     =  depths
  
  	return config, expected_shape
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Tuple     )       ->	Tuple:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if "downsample_layers.0.0" in name:
  		__magic_name__	:	List[str]			     =  name.replace("downsample_layers.0.0"   ,     "embeddings.patch_embeddings"     )
  	if "downsample_layers.0.1" in name:
  		__magic_name__	:	Dict			     =  name.replace("downsample_layers.0.1"   ,     "embeddings.norm"     )  # we rename to layernorm later on
  	if "downsample_layers.1.0" in name:
  		__magic_name__	:	Dict			     =  name.replace("downsample_layers.1.0"   ,     "stages.1.downsampling_layer.0"     )
  	if "downsample_layers.1.1" in name:
  		__magic_name__	:	Optional[Any]			     =  name.replace("downsample_layers.1.1"   ,     "stages.1.downsampling_layer.1"     )
  	if "downsample_layers.2.0" in name:
  		__magic_name__	:	int			     =  name.replace("downsample_layers.2.0"   ,     "stages.2.downsampling_layer.0"     )
  	if "downsample_layers.2.1" in name:
  		__magic_name__	:	int			     =  name.replace("downsample_layers.2.1"   ,     "stages.2.downsampling_layer.1"     )
  	if "downsample_layers.3.0" in name:
  		__magic_name__	:	Any			     =  name.replace("downsample_layers.3.0"   ,     "stages.3.downsampling_layer.0"     )
  	if "downsample_layers.3.1" in name:
  		__magic_name__	:	List[str]			     =  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.
  		__magic_name__	:	Union[str, Any]			     =  name[: len("stages.0"     )] + ".layers" + name[len("stages.0"     ) :]
  	if "stages" in name:
  		__magic_name__	:	str			     =  name.replace("stages"   ,     "encoder.stages"     )
  	if "norm" in name:
  		__magic_name__	:	Optional[int]			     =  name.replace("norm"   ,     "layernorm"     )
  	if "gamma" in name:
  		__magic_name__	:	Optional[int]			     =  name.replace("gamma"   ,     "layer_scale_parameter"     )
  	if "head" in name:
  		__magic_name__	:	int			     =  name.replace("head"   ,     "classifier"     )
  
  	return name
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      )       ->	Optional[int]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	str			     =  "http://images.cocodataset.org/val2017/000000039769.jpg"
  	__magic_name__	:	Union[str, Any]			     =  Image.open(requests.get(_snake_case   ,     stream=_snake_case     ).raw     )
  	return im
  
  
  
  
  
  
  @torch.no_grad()
  def 						lowerCAmelCase_		(      _snake_case   :						Optional[Any]   ,     _snake_case   :						Optional[int]     )       ->	List[Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	str			     =  get_convnext_config(_snake_case     )
  	# load original state_dict from URL
  	__magic_name__	:	Optional[Any]			     =  torch.hub.load_state_dict_from_url(_snake_case     )["model"]
  	# rename keys
  	for key in state_dict.copy().keys():
  		__magic_name__	:	Optional[int]			     =  state_dict.pop(_snake_case     )
  		__magic_name__	:	str			     =  val
  	# add prefix to all keys expect classifier head
  	for key in state_dict.copy().keys():
  		__magic_name__	:	Union[str, Any]			     =  state_dict.pop(_snake_case     )
  		if not key.startswith("classifier"     ):
  			__magic_name__	:	int			     =  "convnext." + key
  		__magic_name__	:	Tuple			     =  val
  
  	# load HuggingFace model
  	__magic_name__	:	Dict			     =  ConvNextForImageClassification(_snake_case     )
  	model.load_state_dict(_snake_case     )
  	model.eval()
  
  	# Check outputs on an image, prepared by ConvNextImageProcessor
  	__magic_name__	:	int			     =  224 if "224" in checkpoint_url else 384
  	__magic_name__	:	int			     =  ConvNextImageProcessor(size=_snake_case     )
  	__magic_name__	:	List[str]			     =  image_processor(images=prepare_img()   ,     return_tensors="pt"     ).pixel_values
  
  	__magic_name__	:	Union[str, Any]			     =  model(_snake_case     ).logits
  
  	# note: the logits below were obtained without center cropping
  	if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
  		__magic_name__	:	Any			     =  torch.tensor([-0.1_210, -0.6_605, 0.1_918]     )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
  		__magic_name__	:	Dict			     =  torch.tensor([-0.4_473, -0.1_847, -0.6_365]     )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
  		__magic_name__	:	int			     =  torch.tensor([0.4_525, 0.7_539, 0.0_308]     )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
  		__magic_name__	:	List[str]			     =  torch.tensor([0.3_561, 0.6_350, -0.0_384]     )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
  		__magic_name__	:	Union[str, Any]			     =  torch.tensor([0.4_174, -0.0_989, 0.1_489]     )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
  		__magic_name__	:	Optional[Any]			     =  torch.tensor([0.2_513, -0.1_349, -0.1_613]     )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
  		__magic_name__	:	str			     =  torch.tensor([1.2_980, 0.3_631, -0.1_198]     )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
  		__magic_name__	:	Optional[Any]			     =  torch.tensor([1.2_963, 0.1_227, 0.1_723]     )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
  		__magic_name__	:	List[Any]			     =  torch.tensor([1.7_956, 0.8_390, 0.2_820]     )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
  		__magic_name__	:	List[Any]			     =  torch.tensor([-0.2_822, -0.0_502, -0.0_878]     )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
  		__magic_name__	:	Optional[int]			     =  torch.tensor([-0.5_672, -0.0_730, -0.4_348]     )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
  		__magic_name__	:	List[str]			     =  torch.tensor([0.2_681, 0.2_365, 0.6_246]     )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
  		__magic_name__	:	Optional[Any]			     =  torch.tensor([-0.2_642, 0.3_931, 0.5_116]     )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
  		__magic_name__	:	Any			     =  torch.tensor([-0.6_677, -0.1_873, -0.8_379]     )
  	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
  		__magic_name__	:	Tuple			     =  torch.tensor([-0.7_749, -0.2_967, -0.6_444]     )
  	else:
  		raise ValueError(F'''Unknown URL: {checkpoint_url}'''     )
  
  	assert torch.allclose(logits[0, :3]   ,     _snake_case   ,     atol=1E-3     )
  	assert logits.shape == expected_shape
  
  	Path(_snake_case     ).mkdir(exist_ok=_snake_case     )
  	print(F'''Saving model to {pytorch_dump_folder_path}'''     )
  	model.save_pretrained(_snake_case     )
  	print(F'''Saving image processor to {pytorch_dump_folder_path}'''     )
  	image_processor.save_pretrained(_snake_case     )
  
  	print("Pushing model to the hub..."     )
  	__magic_name__	:	Optional[int]			     =  "convnext"
  	if "tiny" in checkpoint_url:
  		model_name += "-tiny"
  	elif "small" in checkpoint_url:
  		model_name += "-small"
  	elif "base" in checkpoint_url:
  		model_name += "-base"
  	elif "xlarge" in checkpoint_url:
  		model_name += "-xlarge"
  	elif "large" in checkpoint_url:
  		model_name += "-large"
  	if "224" in checkpoint_url:
  		model_name += "-224"
  	elif "384" in checkpoint_url:
  		model_name += "-384"
  	if "22k" in checkpoint_url and "1k" not in checkpoint_url:
  		model_name += "-22k"
  	if "22k" in checkpoint_url and "1k" in checkpoint_url:
  		model_name += "-22k-1k"
  
  	model.push_to_hub(
  	    repo_path_or_name=Path(_snake_case   ,     _snake_case     )   ,     organization="nielsr"   ,     commit_message="Add model"   ,     )
  
  
  if __name__ == "__main__":
  				snake_case				:	Any							   =						argparse.ArgumentParser()
  				# Required parameters
  				parser.add_argument(
  				    "--checkpoint_url",
  				    default="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
  				    type=str,
  				    help="URL of the original ConvNeXT checkpoint you'd like to convert.",
  				)
  				parser.add_argument(
  				    "--pytorch_dump_folder_path",
  				    default=None,
  				    type=str,
  				    required=True,
  				    help="Path to the output PyTorch model directory.",
  				)
  
  				snake_case				:	Any							   =						parser.parse_args()
  				convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
  
  
  
  
  
  
style_context: 
  from __future__ import annotations
  
  snake_case				:	List[str]							   =						"Alexander Joslin"
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						int     )       ->	int:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if number <= 0:
  		__magic_name__	:	int			     =  F'''n must be greater than 0. Got n = {number}'''
  		raise ValueError(_snake_case     )
  
  	__magic_name__	:	List[str]			     =  [number + 1] * (number + 1)
  
  	# starting position
  	__magic_name__	:	Any			     =  0
  	for i in range(1   ,     _snake_case     ):
  		__magic_name__	:	Optional[int]			     =  min(table[i + 1]   ,     table[i] + 1     )
  		# check if out of bounds
  		if i * 2 <= number:
  			__magic_name__	:	str			     =  min(table[i * 2]   ,     table[i] + 1     )
  		# check if out of bounds
  		if i * 3 <= number:
  			__magic_name__	:	Any			     =  min(table[i * 3]   ,     table[i] + 1     )
  	return table[number]
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  